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| author | CoprDistGit <infra@openeuler.org> | 2023-05-31 06:56:30 +0000 |
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| committer | CoprDistGit <infra@openeuler.org> | 2023-05-31 06:56:30 +0000 |
| commit | bcc4f85e519ad95b8aad557f64b39e9ea998a355 (patch) | |
| tree | 37690f0e3096bf291c0b97adf2147cdac9d777f2 | |
| parent | 8bd5e6cd1572777f67543eedd1339fb45ef1a278 (diff) | |
automatic import of python-mealpy
| -rw-r--r-- | .gitignore | 1 | ||||
| -rw-r--r-- | python-mealpy.spec | 2566 | ||||
| -rw-r--r-- | sources | 1 |
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@@ -0,0 +1 @@ +/mealpy-2.5.3.tar.gz diff --git a/python-mealpy.spec b/python-mealpy.spec new file mode 100644 index 0000000..fe85f3c --- /dev/null +++ b/python-mealpy.spec @@ -0,0 +1,2566 @@ +%global _empty_manifest_terminate_build 0 +Name: python-mealpy +Version: 2.5.3 +Release: 1 +Summary: MEALPY: An Open-source Library for Latest Meta-heuristic Algorithms in Python +License: GPLv3 +URL: https://github.com/thieu1995/mealpy +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/b0/3c/9cace18c6f3911344317fcc16a37752c3829643a0434a71678da567e2042/mealpy-2.5.3.tar.gz +BuildArch: noarch + +Requires: python3-numpy +Requires: python3-matplotlib +Requires: python3-scipy +Requires: python3-pandas +Requires: python3-opfunu +Requires: python3-pytest +Requires: python3-twine + +%description +[](https://github.com/thieu1995/mealpy/releases) +[](https://pypi.python.org/pypi/mealpy) +[](https://badge.fury.io/py/mealpy) + + + +[](https://pepy.tech/project/mealpy) +[](https://github.com/thieu1995/mealpy/actions/workflows/publish-package.yaml) + +[](https://mealpy.readthedocs.io/en/latest/?badge=latest) +[](https://t.me/+fRVCJGuGJg1mNDg1) +[](http://isitmaintained.com/project/thieu1995/mealpy "Average time to resolve an issue") +[](http://isitmaintained.com/project/thieu1995/mealpy "Percentage of issues still open") + +[](https://git-scm.com/book/en/v2/GitHub-Contributing-to-a-Project) +[](https://doi.org/10.5281/zenodo.3711948) +[](https://www.gnu.org/licenses/gpl-3.0) +MEALPY is the largest python library for most of the cutting-edge nature-inspired meta-heuristic algorithms (population-based). Population meta-heuristic algorithms (PMA) are the most popular algorithms in the field of +approximate optimization. +* **Free software:** GNU General Public License (GPL) V3 license +* **Total algorithms**: 174 (102 original, 45 official variants, 27 developed variants) +* **Documentation:** https://mealpy.readthedocs.io/en/latest/ +* **Python versions:** 3.7.x, 3.8.x, 3.9.x, 3.10.x, 3.11.x +* **Dependencies:** numpy, scipy, pandas, matplotlib +# Goals +Our goals are to implement all of the classical as well as the state-of-the-art nature-inspired algorithms, create a simple interface that helps researchers access optimization algorithms as quickly as possible, and share knowledge of the optimization field with everyone without a fee. What you can do with mealpy: +- Analyse parameters of meta-heuristic algorithms. +- Perform Qualitative and Quantitative Analysis of algorithms. +- Analyse rate of convergence of algorithms. +- Test and Analyse the scalability and the robustness of algorithms. +- Save results in various formats (csv, json, pickle, png, pdf, jpeg) +- Export and import models can also be done with Mealpy. +# Installation +### Install with pip +Install the [current PyPI release](https://pypi.python.org/pypi/mealpy): +```sh +$ pip install mealpy==2.5.3 +``` +### Install from source +In case you want to install directly from the source code, use: +```sh +$ git clone https://github.com/thieu1995/mealpy.git +$ cd mealpy +$ python setup.py install +``` +# Usage +After installation, you can import Mealpy as any other Python module: +```sh +$ python +>>> import mealpy +>>> mealpy.__version__ +``` +Let's go through a basic and advanced example. +## Examples +### Simple Benchmark Function +```python +from mealpy.bio_based import SMA +import numpy as np +def fitness_function(solution): + return np.sum(solution**2) +problem = { + "fit_func": fitness_function, + "lb": [-100, ] * 30, + "ub": [100, ] * 30, + "minmax": "min", + "log_to": None, + "save_population": False, +} +## Run the algorithm +model = SMA.BaseSMA(epoch=100, pop_size=50, pr=0.03) +best_position, best_fitness = model.solve(problem) +print(f"Best solution: {best_position}, Best fitness: {best_fitness}") +``` +### Constrained Benchmark Function +* [The Constrained Benchmark Function](https://github.com/thieu1995/mealpy/tree/master/examples/applications/run_constraint_functions.py) +### Multi-objective Benchmark Function +* [Multi-objective benchmark functions](https://github.com/thieu1995/mealpy/tree/master/examples/applications/run_multi_objective_functions.py) +### Custom Problem +For our custom problem, we can create a class and inherit from the Problem class, named the child class the +'Squared' class. In the initialization method of the 'Squared' class, we have to set the *lb*, *ub*, and *minmax* +of the problem (lb: a list of lower bound values, ub: a list of upper bound values, and minmax: a string specifying +whether the problem is a 'min' or 'max' problem). +Afterwards, we have to override the abstract method 'fit_func()', which takes a parameter 'solution' (the solution +to be evaluated) and returns the function value. The resulting code should look something like the code snippet +below. 'Name' is an additional parameter we want to include in this class, and you can include any other additional +parameters you need. +```python +import numpy as np +from mealpy.bio_based import BBO +from mealpy.utils.problem import Problem +# Our custom problem class +class Squared(Problem): + def __init__(self, lb=(-5, -5, -5, -5, -5, -5), ub=(5, 5, 5, 5, 5, 5), minmax="min", name="Squared", **kwargs): + super().__init__(lb, ub, minmax, **kwargs) + self.name = name + def fit_func(self, solution): + return np.sum(solution ** 2) +``` +Now, we define an algorithm, and pass an instance of our *Squared* class as the problem argument. +```python +problem = Squared(lb=[-10] * 20, ub=[10] * 20, minmax="min") +model = BBO.BaseBBO(epoch=10, pop_size=50) +best_position, best_fitness = model.solve(problem) +print(best_position) +print(best_fitness) +print(model.get_parameters()) +print(model.get_name()) +print(model.get_attributes()["solution"]) +print(model.problem.get_name()) +print(model.problem.n_dims) +``` +### Tuner class (GridSearchCV/ParameterSearch, Hyper-parameter tuning) +We build a dedicated class, Tuner, that can help you tune your algorithm's parameters. +```python +import numpy as np +from mealpy.bio_based import BBO +from mealpy.tuner import Tuner # Remember this +def fitness(solution): + return np.sum(solution**2) +problem = { + "lb": [-100, ]*50, + "ub": [100, ]*50, + "minmax": "min", + "fit_func": fitness, + "name": "Squared Problem", + "log_to": None, +} +paras_bbo_grid = { + "epoch": [100], + "pop_size": [50], + "elites": [2, 3, 4, 5], + "p_m": [0.01, 0.02, 0.05, 0.1, 0.15, 0.2] +} +term = { + "max_fe": 10000 +} +if __name__ == "__main__": + model = BBO.BaseBBO() + tuner = Tuner(model, paras_bbo_grid) + tuner.execute(problem=problem, termination=term, n_trials=5, n_jobs=5, mode="thread", n_workers=4, verbose=True) + ## Solve this problem 5 times (n_trials) using 5 processes (n_jobs), each process will handle 1 trial. + ## The mode to run the solver is thread (mode), we will calculate the fitness of 4 solutions (n_workers) at the same time + print(tuner.best_score) + print(tuner.best_params) + print(tuner.best_algorithm) + print(tuner.best_algorithm.get_name()) + ## Save results to csv file + tuner.export_results(save_path="history/tuning", save_as="csv") + ## Re-solve the best model on your problem + best_position, best_fitness = tuner.resolve() + print(best_position, best_fitness) + print(tuner.problem.get_name()) +``` +### Multitask class (Multitask solving) +We also build a dedicated class, Multitask, that can help you run several scenarios. For example: +1. Run 1 algorithm with 1 problem, and multiple trials +2. Run 1 algorithm with multiple problems, and multiple trials +3. Run multiple algorithms with 1 problem, and multiple trials +4. Run multiple algorithms with multiple problems, and multiple trials +```python +#### Using multiple algorithm to solve multiple problems with multiple trials +## Import libraries +## For example, we want to solve F5, F10, F29 problem in CEC-2017 +from opfunu.cec_based.cec2017 import F52017, F102017, F292017 +from mealpy.bio_based import BBO +from mealpy.evolutionary_based import DE +from mealpy.multitask import Multitask # Remember this +## You can define your own problems +f1 = F52017(30, f_bias=0) +f2 = F102017(30, f_bias=0) +f3 = F292017(30, f_bias=0) +p1 = { + "lb": f1.lb.tolist(), + "ub": f1.ub.tolist(), + "minmax": "min", + "fit_func": f1.evaluate, + "name": "F5-CEC2017", + "log_to": None, +} +p2 = { + "lb": f2.lb.tolist(), + "ub": f2.ub.tolist(), + "minmax": "min", + "fit_func": f2.evaluate, + "name": "F10-CEC2017", + "log_to": None, +} +p3 = { + "lb": f3.lb.tolist(), + "ub": f3.ub.tolist(), + "minmax": "min", + "fit_func": f3.evaluate, + "name": "F29-CEC2017", + "log_to": None, +} +## Define models +model1 = BBO.BaseBBO(epoch=10, pop_size=50) +model2 = BBO.OriginalBBO(epoch=10, pop_size=50) +model3 = DE.BaseDE(epoch=10, pop_size=50) +## Define termination if needed +term = { + "max_fe": 10000 +} +## Define and run Multitask +if __name__ == "__main__": + multitask = Multitask(algorithms=(model1, model2, model3), problems=(p1, p2, p3), terminations=(term, ), modes=("thread", )) + # default modes = "single", default termination = epoch (as defined in problem dictionary) + multitask.execute(n_trials=5, n_jobs=5, save_path="history", save_as="csv", save_convergence=False, verbose=False) + ## Check the directory: history/, you will see list of .csv result files +``` +For more usage examples please look at [examples](/examples) folder. +More advanced examples can also be found in the [Mealpy-examples repository](https://github.com/thieu1995/mealpy_examples). +### Get Visualize Figures +* [Tutorials](/examples/utils/visualize/all_charts.py) +<p align="center"><img src="https://thieu1995.github.io/post/2022-04/19-mealpy-tutorials/mealpy2.png" alt="MEALPY"/> +</p> +## Mealpy Application +### Mealpy + Neural Network (Replace the Gradient Descent Optimizer) +* Time-series Problem: + * Traditional MLP + code: [Link](https://github.com/thieu1995/mealpy/tree/master/examples/applications/keras/traditional-mlp-time-series.py) + * Hybrid code (Mealpy + + MLP): [Link](https://github.com/thieu1995/mealpy/tree/master/examples/applications/keras/mha-hybrid-mlp-time-series.py) +* Classification Problem: + * Traditional MLP + code: [Link](https://github.com/thieu1995/mealpy/blob/master/examples/applications/keras/traditional-mlp-classification.py) + * Hybrid code (Mealpy + + MLP): [Link](https://github.com/thieu1995/mealpy/blob/master/examples/applications/keras/mha-hybrid-mlp-classification.py) +### Mealpy + Neural Network (Optimize Neural Network Hyper-parameter) +Code: [Link](https://github.com/thieu1995/mealpy/blob/master/examples/applications/keras/mha-hyper-parameter-mlp-time-series.py) +### Other Applications +* Solving Knapsack Problem (Discrete + problems): [Link](https://github.com/thieu1995/mealpy/blob/master/examples/applications/discrete-problems/knapsack-problem.py) +* Optimize SVM (SVC) + model: [Link](https://github.com/thieu1995/mealpy/blob/master/examples/applications/sklearn/svm_classification.py) +* Optimize Linear Regression + Model: [Link](https://github.com/thieu1995/mealpy/blob/master/examples/applications/pytorch/linear_regression.py) +* Travelling Salesman Problem: https://github.com/thieu1995/MHA-TSP +* Feature selection problem: https://github.com/thieu1995/MHA-FS +## Tutorial Videos +All tutorial videos: [Link](https://mealpy.readthedocs.io/en/latest/pages/general/video_tutorials.html) +All code examples: [Link](https://github.com/thieu1995/mealpy/tree/master/examples) +All visualization examples: [Link](https://mealpy.readthedocs.io/en/latest/pages/visualization.html) +### Get helps (questions, problems) +* Official source code repo: https://github.com/thieu1995/mealpy +* Official document: https://mealpy.readthedocs.io/ +* Download releases: https://pypi.org/project/mealpy/ +* Issue tracker: https://github.com/thieu1995/mealpy/issues +* Notable changes log: https://github.com/thieu1995/mealpy/blob/master/ChangeLog.md +* Examples with different meapy version: https://github.com/thieu1995/mealpy/blob/master/EXAMPLES.md +* This project also related to our another projects which are "meta-heuristics" and "neural-network", check it here + * https://github.com/thieu1995/opfunu + * https://github.com/thieu1995/metaheuristics + * https://github.com/aiir-team +**Want to have an instant assistant? Join our telegram community at [link](https://t.me/+fRVCJGuGJg1mNDg1)** +We share lots of information, questions, and answers there. You will get more support and knowledge there. +### Cite Us +If you are using mealpy in your project, we would appreciate citations: +```bibtex +@article{van2023mealpy, + title={MEALPY: An open-source library for latest meta-heuristic algorithms in Python}, + author={Van Thieu, Nguyen and Mirjalili, Seyedali}, + journal={Journal of Systems Architecture}, + year={2023}, + publisher={Elsevier} +} +@article{van2023groundwater, + title={Groundwater level modeling using Augmented Artificial Ecosystem Optimization}, + author={Van Thieu, Nguyen and Barma, Surajit Deb and Van Lam, To and Kisi, Ozgur and Mahesha, Amai}, + journal={Journal of Hydrology}, + volume={617}, + pages={129034}, + year={2023}, + publisher={Elsevier} +} +``` +# List of papers used MEALPY +- Min, J., Oh, M., Kim, W., Seo, H., & Paek, J. (2022, October). Evaluation of Metaheuristic Algorithms for TAS Scheduling in Time-Sensitive Networking. In 2022 13th International Conference on Information and Communication Technology Convergence (ICTC) (pp. 809-812). IEEE. +- Khozeimeh, F., Sharifrazi, D., Izadi, N. H., Joloudari, J. H., Shoeibi, A., Alizadehsani, R., ... & Islam, S. M. S. (2021). Combining a convolutional neural network with autoencoders to predict the survival chance of COVID-19 patients. Scientific Reports, 11(1), 15343. +- Rajesh, K., Jain, E., & Kotecha, P. (2022). A Multi-Objective approach to the Electric Vehicle Routing Problem. arXiv preprint arXiv:2208.12440. +- Sánchez, A. J. H., & Upegui, F. R. (2022). Una herramienta para el diseño de redes MSMN de banda ancha en líneas de transmisión basada en algoritmos heurísticos de optimización comparados. Revista Ingeniería UC, 29(2), 106-123. +- Khanmohammadi, M., Armaghani, D. J., & Sabri Sabri, M. M. (2022). Prediction and Optimization of Pile Bearing Capacity Considering Effects of Time. Mathematics, 10(19), 3563. +- Kudela, J. (2023). The Evolutionary Computation Methods No One Should Use. arXiv preprint arXiv:2301.01984. +- Vieira, M., Faia, R., Pinto, T., & Vale, Z. (2022, September). Schedule Peer-to-Peer Transactions of an Energy Community Using Particle Swarm. In 2022 18th International Conference on the European Energy Market (EEM) (pp. 1-6). IEEE. +- Bui, X. N., Nguyen, H., Le, Q. T., & Le, T. N. Forecasting PM. MINING SCIENCE ANDTECHNOLOGY (Russia), 111. +- Bui, X. N., Nguyen, H., Le, Q. T., & Le, T. N. (2022). Forecasting PM 2.5 emissions in open-pit minesusing a functional link neural network optimized by various optimization algorithms. Gornye nauki i tekhnologii= Mining Science and Technology (Russia), 7(2), 111-125. +- Doğan, E., & Yörükeren, N. (2022). Enhancement of Transmission System Security with Archimedes Optimization Algorithm. +- Ayub, N., Aurangzeb, K., Awais, M., & Ali, U. (2020, November). Electricity theft detection using CNN-GRU and manta ray foraging optimization algorithm. In 2020 IEEE 23Rd international multitopic conference (INMIC) (pp. 1-6). IEEE. +- Pintilie, L., Nechita, M. T., Suditu, G. D., Dafinescu, V., & Drăgoi, E. N. (2022). Photo-decolorization of Eriochrome Black T: process optimization with Differential Evolution algorithm. In PASEW-22, MESSH-22 & CABES-22 April 19–21, 2022 Paris (France). Eminent Association of Pioneers. +- LaTorre, A., Molina, D., Osaba, E., Poyatos, J., Del Ser, J., & Herrera, F. (2021). A prescription of methodological guidelines for comparing bio-inspired optimization algorithms. Swarm and Evolutionary Computation, 67, 100973. +- Gottam, S., Nanda, S. J., & Maddila, R. K. (2021, December). A CNN-LSTM Model Trained with Grey Wolf Optimizer for Prediction of Household Power Consumption. In 2021 IEEE International Symposium on Smart Electronic Systems (iSES)(Formerly iNiS) (pp. 355-360). IEEE. +- Darius, P. S., Devadason, J., & Solomon, D. G. (2022, December). Prospects of Ant Colony Optimization (ACO) in Various Domains. In 2022 4th International Conference on Circuits, Control, Communication and Computing (I4C) (pp. 79-84). IEEE. +- Ayub, N., Irfan, M., Awais, M., Ali, U., Ali, T., Hamdi, M., ... & Muhammad, F. (2020). Big data analytics for short and medium-term electricity load forecasting using an AI techniques ensembler. Energies, 13(19), 5193. +- Biundini, I. Z., Melo, A. G., Coelho, F. O., Honório, L. M., Marcato, A. L., & Pinto, M. F. (2022). Experimentation and Simulation with Autonomous Coverage Path Planning for UAVs. Journal of Intelligent & Robotic Systems, 105(2), 46. +- Yousaf, I., Anwar, F., Imtiaz, S., Almadhor, A. S., Ishmanov, F., & Kim, S. W. (2022). An Optimized Hyperparameter of Convolutional Neural Network Algorithm for Bug Severity Prediction in Alzheimer’s-Based IoT System. Computational Intelligence and Neuroscience, 2022. +- Xu, L., Yan, W., & Ji, J. (2023). The research of a novel WOG-YOLO algorithm for autonomous driving object detection. Scientific reports, 13(1), 3699. +- Costache, R. D., Arabameri, A., Islam, A. R. M. T., Abba, S. I., Pandey, M., Ajin, R. S., & Pham, B. T. (2022). Flood susceptibility computation using state-of-the-art machine learning and optimization algorithms. +- Del Ser, J., Osaba, E., Martinez, A. D., Bilbao, M. N., Poyatos, J., Molina, D., & Herrera, F. (2021, December). More is not always better: insights from a massive comparison of meta-heuristic algorithms over real-parameter optimization problems. In 2021 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 1-7). IEEE. +- Rustam, F., Aslam, N., De La Torre Díez, I., Khan, Y. D., Mazón, J. L. V., Rodríguez, C. L., & Ashraf, I. (2022, November). White Blood Cell Classification Using Texture and RGB Features of Oversampled Microscopic Images. In Healthcare (Vol. 10, No. 11, p. 2230). MDPI. +- Neupane, D., Kafle, S., Gurung, S., Neupane, S., & Bhattarai, N. (2021). Optimal sizing and financial analysis of a stand-alone SPV-micro-hydropower hybrid system considering generation uncertainty. International Journal of Low-Carbon Technologies, 16(4), 1479-1491. +- Liang, R., Le-Hung, T., & Nguyen-Thoi, T. (2022). Energy consumption prediction of air-conditioning systems in eco-buildings using hunger games search optimization-based artificial neural network model. Journal of Building Engineering, 59, 105087. +- He, Z., Nguyen, H., Vu, T. H., Zhou, J., Asteris, P. G., & Mammou, A. (2022). Novel integrated approaches for predicting the compressibility of clay using cascade forward neural networks optimized by swarm-and evolution-based algorithms. Acta Geotechnica, 1-16. +- Xu, L., Yan, W., & Ji, J. (2022). The research of a novel WOG-YOLO algorithm forautonomous driving object detection. +- Nasir Ayub, M. I., Awais, M., Ali, U., Ali, T., Hamdi, M., Alghamdi, A., & Muhammad, F. Big Data Analytics for Short and Medium Term Electricity Load Forecasting using AI Techniques Ensembler. +- Xie, C., Nguyen, H., Choi, Y., & Armaghani, D. J. (2022). Optimized functional linked neural network for predicting diaphragm wall deflection induced by braced excavations in clays. Geoscience Frontiers, 13(2), 101313. +- Hakemi, S., Houshmand, M., & Hosseini, S. A. (2022). A Dynamic Quantum-Inspired Genetic Algorithm with Lengthening Chromosome Size. +- Kashifi, M. T. City-Wide Crash Risk Prediction and Interpretation Using Deep Learning Model with Multi-Source Big Data. Available at SSRN 4329686. +- Nguyen, H., & Hoang, N. D. (2022). Computer vision-based classification of concrete spall severity using metaheuristic-optimized Extreme Gradient Boosting Machine and Deep Convolutional Neural Network. Automation in Construction, 140, 104371. +- Zheng, J., Lu, Z., Wu, K., Ning, G. H., & Li, D. (2020). Coinage-metal-based cyclic trinuclear complexes with metal–metal interactions: Theories to experiments and structures to functions. Chemical Reviews, 120(17), 9675-9742. +- Van Thieu, N., Barma, S. D., Van Lam, T., Kisi, O., & Mahesha, A. (2023). Groundwater level modeling using Augmented Artificial Ecosystem Optimization. Journal of Hydrology, 617, 129034. +- Mo, Z., Zhang, Z., Miao, Q., & Tsui, K. L. (2022). Intelligent Informative Frequency Band Searching Assisted by a Dynamic Bandit Tree Method for Machine Fault Diagnosis. IEEE/ASME Transactions on Mechatronics. +- Dangi, D., Chandel, S. T., Dixit, D. K., Sharma, S., & Bhagat, A. (2023). An Efficient Model for Sentiment Analysis using Artificial Rabbits Optimized Vector Functional Link Network. Expert Systems with Applications, 119849. +- Dey, S., Roychoudhury, R., Malakar, S., & Sarkar, R. (2022). An optimized fuzzy ensemble of convolutional neural networks for detecting tuberculosis from Chest X-ray images. Applied Soft Computing, 114, 108094. +- Mousavirad, S. J., & Alexandre, L. A. (2022). Population-based JPEG Image Compression: Problem Re-Formulation. arXiv preprint arXiv:2212.06313. +- Tsui, K. L. Intelligent Informative Frequency Band Searching Assisted by A Dynamic Bandit Tree Method for Machine Fault Diagnosis. +- Neupane, D. (2020). Optimal Sizing and Performance Analysis of Solar PV-Micro hydropower Hybrid System in the Context of Rural Area of Nepal (Doctoral dissertation, Pulchowk Campus). +- LaTorre, A., Molina, D., Osaba, E., Poyatos, J., Del Ser, J., & Herrera, F. Swarm and Evolutionary Computation. +- Vieira, M. A. (2022). Otimização dos custos operacionais de uma comunidade energética considerando transações locais em “peer-to-peer” (Doctoral dissertation). +- Toğaçar, M. (2022). Using DarkNet models and metaheuristic optimization methods together to detect weeds growing along with seedlings. Ecological Informatics, 68, 101519. +- Toğaçar, M. (2021). Detection of segmented uterine cancer images by Hotspot Detection method using deep learning models, Pigeon-Inspired Optimization, types-based dominant activation selection approaches. Computers in Biology and Medicine, 136, 104659. +- Khan, N. A Short Term Electricity Load and Price Forecasting Model Based on BAT Algorithm in Logistic Regression and CNN-GRU with WOA. +- Yelisetti, S., Saini, V. K., Kumar, R., & Lamba, R. (2022, May). Energy Consumption Cost Benefits through Smart Home Energy Management in Residential Buildings: An Indian Case Study. In 2022 IEEE IAS Global Conference on Emerging Technologies (GlobConET) (pp. 930-935). IEEE. +- Nguyen, H., Cao, M. T., Tran, X. L., Tran, T. H., & Hoang, N. D. (2022). A novel whale optimization algorithm optimized XGBoost regression for estimating bearing capacity of concrete piles. Neural Computing and Applications, 1-28. +- Hirsching, C., de Jongh, S., Eser, D., Suriyah, M., & Leibfried, T. (2022). Meta-heuristic optimization of control structure and design for MMC-HVdc applications. Electric Power Systems Research, 213, 108371. +- Amelin, V., Gatiyatullin, E., Romanov, N., Samarkhanov, R., Vasilyev, R., & Yanovich, Y. (2022). Black-Box for Blockchain Parameters Adjustment. IEEE Access, 10, 101795-101802. +- Ngo, T. Q., Nguyen, L. Q., & Tran, V. Q. (2022). Novel hybrid machine learning models including support vector machine with meta-heuristic algorithms in predicting unconfined compressive strength of organic soils stabilised with cement and lime. International Journal of Pavement Engineering, 1-18. +- Zhu, Y., & Iiduka, H. (2021). Unified Algorithm Framework for Nonconvex Stochastic Optimization in Deep Neural Networks. IEEE Access, 9, 143807-143823. +- Hakemi, S., Houshmand, M., KheirKhah, E., & Hosseini, S. A. (2022). A review of recent advances in quantum-inspired metaheuristics. Evolutionary Intelligence, 1-16. +- Das, A., Das, S. R., Panda, J. P., Dey, A., Gajrani, K. K., Somani, N., & Gupta, N. (2022). Machine learning based modelling and optimization in hard turning of AISI D6 steel with newly developed AlTiSiN coated carbide tool. arXiv preprint arXiv:2202.00596. +- Yelisetti, S., Saini, V. K., Kumar, R., Lamba, R., & Saxena, A. (2022). Optimal energy management system for residential buildings considering the time of use price with swarm intelligence algorithms. Journal of Building Engineering, 59, 105062. +- Valdés, G. T. (2022). Algoritmo para la detección de vehículos y peatones combinando CNN´ sy técnicas de búsqueda. +- Sallam, N. M., Saleh, A. I., Ali, H. A., & Abdelsalam, M. M. (2023). An efficient EGWO algorithm as feature selection for B-ALL diagnoses and its subtypes classification using peripheral blood smear images. Alexandria Engineering Journal, 68, 39-66. +# Documents +* Meta-heuristic Categories: (Based on this article: [link](https://doi.org/10.1016/j.procs.2020.09.075)) + + Evolutionary-based: Idea from Darwin's law of natural selection, evolutionary computing + + Swarm-based: Idea from movement, interaction of birds, organization of social ... + + Physics-based: Idea from physics law such as Newton's law of universal gravitation, black hole, multiverse + + Human-based: Idea from human interaction such as queuing search, teaching learning, ... + + Biology-based: Idea from biology creature (or microorganism),... + + System-based: Idea from eco-system, immune-system, network-system, ... + + Math-based: Idea from mathematical form or mathematical law such as sin-cosin + + Music-based: Idea from music instrument +* Difficulty - Difficulty Level (Personal Opinion): **Objective observation from author**. Depend on the number of + parameters, number of equations, the original ideas, time spend for coding, source lines of code (SLOC). + + Easy: A few paras, few equations, SLOC very short + + Medium: more equations than Easy level, SLOC longer than Easy level + + Hard: Lots of equations, SLOC longer than Medium level, the paper hard to read. + + Hard* - Very hard: Lots of equations, SLOC too long, the paper is very hard to read. +** For newbie, we recommend to read the paper of algorithms which difficulty is "easy" or "medium" difficulty level. +| **Group** | **Name** | **Module** | **Class** | **Year** | **Paras** | **Difficulty** | +|--------------|-------------------------------------------------|------------|------------------|----------|-----------|----------------| +| Evolutionary | Evolutionary Programming | EP | OriginalEP | 1964 | 3 | easy | +| Evolutionary | - | - | LevyEP | - | 3 | easy | +| Evolutionary | Evolution Strategies | ES | OriginalES | 1971 | 3 | easy | +| Evolutionary | - | - | LevyES | - | 3 | easy | +| Evolutionary | Memetic Algorithm | MA | OriginalMA | 1989 | 7 | easy | +| Evolutionary | Genetic Algorithm | GA | BaseGA | 1992 | 4 | easy | +| Evolutionary | - | - | SingleGA | - | 7 | easy | +| Evolutionary | - | - | MultiGA | - | 7 | easy | +| Evolutionary | - | - | EliteSingleGA | - | 10 | easy | +| Evolutionary | - | - | EliteMultiGA | - | 10 | easy | +| Evolutionary | Differential Evolution | DE | BaseDE | 1997 | 5 | easy | +| Evolutionary | - | - | JADE | 2009 | 6 | medium | +| Evolutionary | - | - | SADE | 2005 | 2 | medium | +| Evolutionary | - | - | SHADE | 2013 | 4 | medium | +| Evolutionary | - | - | L_SHADE | 2014 | 4 | medium | +| Evolutionary | - | - | SAP_DE | 2006 | 3 | medium | +| Evolutionary | Flower Pollination Algorithm | FPA | OriginalFPA | 2014 | 4 | medium | +| Evolutionary | Coral Reefs Optimization | CRO | OriginalCRO | 2014 | 11 | medium | +| Evolutionary | - | - | OCRO | 2019 | 12 | medium | +| - | - | - | - | - | - | - | +| Swarm | Particle Swarm Optimization | PSO | OriginalPSO | 1995 | 6 | easy | +| Swarm | - | - | PPSO | 2019 | 2 | medium | +| Swarm | - | - | HPSO_TVAC | 2017 | 4 | medium | +| Swarm | - | - | C_PSO | 2015 | 6 | medium | +| Swarm | - | - | CL_PSO | 2006 | 6 | medium | +| Swarm | Bacterial Foraging Optimization | BFO | OriginalBFO | 2002 | 10 | hard | +| Swarm | - | - | ABFO | 2019 | 8 | medium | +| Swarm | Bees Algorithm | BeesA | OriginalBeesA | 2005 | 8 | medium | +| Swarm | - | - | ProbBeesA | 2015 | 5 | medium | +| Swarm | Cat Swarm Optimization | CSO | OriginalCSO | 2006 | 11 | hard | +| Swarm | Artificial Bee Colony | ABC | OriginalABC | 2007 | 8 | medium | +| Swarm | Ant Colony Optimization | ACO-R | OriginalACOR | 2008 | 5 | easy | +| Swarm | Cuckoo Search Algorithm | CSA | OriginalCSA | 2009 | 3 | medium | +| Swarm | Firefly Algorithm | FFA | OriginalFFA | 2009 | 8 | easy | +| Swarm | Fireworks Algorithm | FA | OriginalFA | 2010 | 7 | medium | +| Swarm | Bat Algorithm | BA | OriginalBA | 2010 | 6 | medium | +| Swarm | - | - | AdaptiveBA | - | 8 | medium | +| Swarm | - | - | ModifiedBA | - | 5 | medium | +| Swarm | Fruit-fly Optimization Algorithm | FOA | OriginalFOA | 2012 | 2 | easy | +| Swarm | - | - | BaseFOA | - | 2 | easy | +| Swarm | - | - | WhaleFOA | 2020 | 2 | medium | +| Swarm | Social Spider Optimization | SSpiderO | OriginalSSpiderO | 2018 | 4 | hard* | +| Swarm | Grey Wolf Optimizer | GWO | OriginalGWO | 2014 | 2 | easy | +| Swarm | - | - | RW_GWO | 2019 | 2 | easy | +| Swarm | Social Spider Algorithm | SSpiderA | OriginalSSpiderA | 2015 | 5 | medium | +| Swarm | Ant Lion Optimizer | ALO | OriginalALO | 2015 | 2 | easy | +| Swarm | - | - | BaseALO | - | 2 | easy | +| Swarm | Moth Flame Optimization | MFO | OriginalMFO | 2015 | 2 | easy | +| Swarm | - | - | BaseMFO | - | 2 | easy | +| Swarm | Elephant Herding Optimization | EHO | OriginalEHO | 2015 | 5 | easy | +| Swarm | Jaya Algorithm | JA | OriginalJA | 2016 | 2 | easy | +| Swarm | - | - | BaseJA | - | 2 | easy | +| Swarm | - | - | LevyJA | 2021 | 2 | easy | +| Swarm | Whale Optimization Algorithm | WOA | OriginalWOA | 2016 | 2 | medium | +| Swarm | - | - | HI_WOA | 2019 | 3 | medium | +| Swarm | Dragonfly Optimization | DO | OriginalDO | 2016 | 2 | medium | +| Swarm | Bird Swarm Algorithm | BSA | OriginalBSA | 2016 | 9 | medium | +| Swarm | Spotted Hyena Optimizer | SHO | OriginalSHO | 2017 | 4 | medium | +| Swarm | Salp Swarm Optimization | SSO | OriginalSSO | 2017 | 2 | easy | +| Swarm | Swarm Robotics Search And Rescue | SRSR | OriginalSRSR | 2017 | 2 | hard* | +| Swarm | Grasshopper Optimisation Algorithm | GOA | OriginalGOA | 2017 | 4 | easy | +| Swarm | Coyote Optimization Algorithm | COA | OriginalCOA | 2018 | 3 | medium | +| Swarm | Moth Search Algorithm | MSA | OriginalMSA | 2018 | 5 | easy | +| Swarm | Sea Lion Optimization | SLO | OriginalSLO | 2019 | 2 | medium | +| Swarm | - | - | ModifiedSLO | - | 2 | medium | +| Swarm | - | - | ImprovedSLO | - | 4 | medium | +| Swarm | Nake Mole-Rat Algorithm | NMRA | OriginalNMRA | 2019 | 3 | easy | +| Swarm | - | - | ImprovedNMRA | - | 4 | medium | +| Swarm | Pathfinder Algorithm | PFA | OriginalPFA | 2019 | 2 | medium | +| Swarm | Sailfish Optimizer | SFO | OriginalSFO | 2019 | 5 | easy | +| Swarm | - | - | ImprovedSFO | - | 3 | medium | +| Swarm | Harris Hawks Optimization | HHO | OriginalHHO | 2019 | 2 | medium | +| Swarm | Manta Ray Foraging Optimization | MRFO | OriginalMRFO | 2020 | 3 | medium | +| Swarm | Bald Eagle Search | BES | OriginalBES | 2020 | 7 | easy | +| Swarm | Sparrow Search Algorithm | SSA | OriginalSSA | 2020 | 5 | medium | +| Swarm | - | - | BaseSSA | - | 5 | medium | +| Swarm | Hunger Games Search | HGS | OriginalHGS | 2021 | 4 | medium | +| Swarm | Aquila Optimizer | AO | OriginalAO | 2021 | 2 | easy | +| Swarm | Hybrid Grey Wolf - Whale Optimization Algorithm | GWO | GWO_WOA | 2022 | 2 | easy | +| Swarm | Marine Predators Algorithm | MPA | OriginalMPA | 2020 | 2 | medium | +| Swarm | Honey Badger Algorithm | HBA | OriginalHBA | 2022 | 2 | easy | +| Swarm | Sand Cat Swarm Optimization | SCSO | OriginalSCSO | 2022 | 2 | easy | +| Swarm | Tuna Swarm Optimization | TSO | OriginalTSO | 2021 | 2 | medium | +| Swarm | African Vultures Optimization Algorithm | AVOA | OriginalAVOA | 2022 | 7 | medium | +| Swarm | Artificial Gorilla Troops Optimization | AGTO | OriginalAGTO | 2021 | 5 | medium | +| Swarm | Artificial Rabbits Optimization | ARO | OriginalARO | 2022 | 2 | easy | +| Swarm | Dwarf Mongoose Optimization Algorithm | DMOA | OriginalDMOA | 2022 | 4 | medium | +| Swarm | - | - | DevDMOA | - | 3 | medium | +| - | - | - | - | - | - | - | +| Physics | Simulated Annealling | SA | OriginalSA | 1987 | 9 | medium | +| Physics | Wind Driven Optimization | WDO | OriginalWDO | 2013 | 7 | easy | +| Physics | Multi-Verse Optimizer | MVO | OriginalMVO | 2016 | 4 | easy | +| Physics | - | - | BaseMVO | - | 4 | easy | +| Physics | Tug of War Optimization | TWO | OriginalTWO | 2016 | 2 | easy | +| Physics | - | - | OppoTWO | - | 2 | medium | +| Physics | - | - | LevyTWO | - | 2 | medium | +| Physics | - | - | EnhancedTWO | 2020 | 2 | medium | +| Physics | Electromagnetic Field Optimization | EFO | OriginalEFO | 2016 | 6 | easy | +| Physics | - | - | BaseEFO | - | 6 | medium | +| Physics | Nuclear Reaction Optimization | NRO | OriginalNRO | 2019 | 2 | hard* | +| Physics | Henry Gas Solubility Optimization | HGSO | OriginalHGSO | 2019 | 3 | medium | +| Physics | Atom Search Optimization | ASO | OriginalASO | 2019 | 4 | medium | +| Physics | Equilibrium Optimizer | EO | OriginalEO | 2019 | 2 | easy | +| Physics | - | - | ModifiedEO | 2020 | 2 | medium | +| Physics | - | - | AdaptiveEO | 2020 | 2 | medium | +| Physics | Archimedes Optimization Algorithm | ArchOA | OriginalArchOA | 2021 | 8 | medium | +| - | - | - | - | - | - | - | +| Human | Culture Algorithm | CA | OriginalCA | 1994 | 3 | easy | +| Human | Imperialist Competitive Algorithm | ICA | OriginalICA | 2007 | 8 | hard* | +| Human | Teaching Learning-based Optimization | TLO | OriginalTLO | 2011 | 2 | easy | +| Human | - | - | BaseTLO | 2012 | 2 | easy | +| Human | - | - | ITLO | 2013 | 3 | medium | +| Human | Brain Storm Optimization | BSO | OriginalBSO | 2011 | 8 | medium | +| Human | - | - | ImprovedBSO | 2017 | 7 | medium | +| Human | Queuing Search Algorithm | QSA | OriginalQSA | 2019 | 2 | hard | +| Human | - | - | BaseQSA | - | 2 | hard | +| Human | - | - | OppoQSA | - | 2 | hard | +| Human | - | - | LevyQSA | - | 2 | hard | +| Human | - | - | ImprovedQSA | 2021 | 2 | hard | +| Human | Search And Rescue Optimization | SARO | OriginalSARO | 2019 | 4 | medium | +| Human | - | - | BaseSARO | - | 4 | medium | +| Human | Life Choice-Based Optimization | LCO | OriginalLCO | 2019 | 3 | easy | +| Human | - | - | BaseLCO | - | 3 | easy | +| Human | - | - | ImprovedLCO | - | 2 | easy | +| Human | Social Ski-Driver Optimization | SSDO | OriginalSSDO | 2019 | 2 | easy | +| Human | Gaining Sharing Knowledge-based Algorithm | GSKA | OriginalGSKA | 2019 | 6 | medium | +| Human | - | - | BaseGSKA | - | 4 | medium | +| Human | Coronavirus Herd Immunity Optimization | CHIO | OriginalCHIO | 2020 | 4 | medium | +| Human | - | - | BaseCHIO | - | 4 | medium | +| Human | Forensic-Based Investigation Optimization | FBIO | OriginalFBIO | 2020 | 2 | medium | +| Human | - | - | BaseFBIO | - | 2 | medium | +| Human | Battle Royale Optimization | BRO | OriginalBRO | 2020 | 3 | medium | +| Human | - | - | BaseBRO | - | 3 | medium | +| Human | Student Psychology Based Optimization | SPBO | OriginalSPBO | 2020 | 2 | medium | +| Human | - | - | DevSPBO | | 2 | medium | +| - | - | - | - | - | - | - | +| Bio | Invasive Weed Optimization | IWO | OriginalIWO | 2006 | 7 | easy | +| Bio | Biogeography-Based Optimization | BBO | OriginalBBO | 2008 | 4 | easy | +| Bio | - | - | BaseBBO | - | 4 | easy | +| Bio | Virus Colony Search | VCS | OriginalVCS | 2016 | 4 | hard* | +| Bio | - | - | BaseVCS | - | 4 | hard* | +| Bio | Satin Bowerbird Optimizer | SBO | OriginalSBO | 2017 | 5 | easy | +| Bio | - | - | BaseSBO | - | 5 | easy | +| Bio | Earthworm Optimisation Algorithm | EOA | OriginalEOA | 2018 | 8 | medium | +| Bio | Wildebeest Herd Optimization | WHO | OriginalWHO | 2019 | 12 | hard | +| Bio | Slime Mould Algorithm | SMA | OriginalSMA | 2020 | 3 | easy | +| Bio | - | - | BaseSMA | - | 3 | easy | +| Bio | Barnacles Mating Optimizer | BMO | OriginalBMO | 2018 | 3 | easy | +| Bio | Tunicate Swarm Algorithm | TSA | OriginalTSA | 2020 | 2 | easy | +| Bio | Symbiotic Organisms Search | SOS | OriginalSOS | 2014 | 2 | medium | +| Bio | Seagull Optimization Algorithm | SOA | OriginalSOA | 2019 | 3 | easy | +| Bio | - | - | DevSOA | - | 3 | easy | +| - | - | - | - | - | - | - | +| System | Germinal Center Optimization | GCO | OriginalGCO | 2018 | 4 | medium | +| System | - | - | BaseGCO | - | 4 | medium | +| System | Water Cycle Algorithm | WCA | OriginalWCA | 2012 | 5 | medium | +| System | Artificial Ecosystem-based Optimization | AEO | OriginalAEO | 2019 | 2 | easy | +| System | - | - | EnhancedAEO | 2020 | 2 | medium | +| System | - | - | ModifiedAEO | 2020 | 2 | medium | +| System | - | - | ImprovedAEO | 2021 | 2 | medium | +| System | - | - | AugmentedAEO | 2022 | 2 | medium | +| - | - | - | - | - | - | - | +| Math | Hill Climbing | HC | OriginalHC | 1993 | 3 | easy | +| Math | - | - | SwarmHC | - | 3 | easy | +| Math | Cross-Entropy Method | CEM | OriginalCEM | 1997 | 4 | easy | +| Math | Sine Cosine Algorithm | SCA | OriginalSCA | 2016 | 2 | easy | +| Math | - | - | BaseSCA | - | 2 | easy | +| Math | Gradient-Based Optimizer | GBO | OriginalGBO | 2020 | 5 | medium | +| Math | Arithmetic Optimization Algorithm | AOA | OrginalAOA | 2021 | 6 | easy | +| Math | Chaos Game Optimization | CGO | OriginalCGO | 2021 | 2 | easy | +| Math | Pareto-like Sequential Sampling | PSS | OriginalPSS | 2021 | 4 | medium | +| Math | weIghted meaN oF vectOrs | INFO | OriginalINFO | 2022 | 2 | medium | +| Math | RUNge Kutta optimizer | RUN | OriginalRUN | 2021 | 2 | hard | +| Math | Circle Search Algorithm | CircleSA | OriginalCircleSA | 2022 | 3 | easy | +| - | - | - | - | - | - | - | +| Music | Harmony Search | HS | OriginalHS | 2001 | 4 | easy | +| Music | - | - | BaseHS | - | 4 | easy | +### A +* **ABC - Artificial Bee Colony** + * **OriginalABC**: Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization (Vol. 200, pp. 1-10). Technical report-tr06, Erciyes university, engineering faculty, computer engineering department. +* **ACOR - Ant Colony Optimization**. + * **OriginalACOR**: Socha, K., & Dorigo, M. (2008). Ant colony optimization for continuous domains. European journal of operational research, 185(3), 1155-1173. +* **ALO - Ant Lion Optimizer** + * **OriginalALO**: Mirjalili S (2015). “The Ant Lion Optimizer.” Advances in Engineering Software, 83, 80-98. doi: [10.1016/j.advengsoft.2015.01.010](https://doi.org/10.1016/j.advengsoft.2015.01.010) + * **BaseALO**: The developed version +* **AEO - Artificial Ecosystem-based Optimization** + * **OriginalAEO**: Zhao, W., Wang, L., & Zhang, Z. (2019). Artificial ecosystem-based optimization: a novel nature-inspired meta-heuristic algorithm. Neural Computing and Applications, 1-43. + * **AugmentedAEO**: Van Thieu, N., Barma, S. D., Van Lam, T., Kisi, O., & Mahesha, A. (2022). Groundwater level modeling using Augmented Artificial Ecosystem Optimization. Journal of Hydrology, 129034. + * **ImprovedAEO**: Rizk-Allah, R. M., & El-Fergany, A. A. (2020). Artificial ecosystem optimizer for parameters identification of proton exchange membrane fuel cells model. International Journal of Hydrogen Energy. + * **EnhancedAEO**: Eid, A., Kamel, S., Korashy, A., & Khurshaid, T. (2020). An Enhanced Artificial Ecosystem-Based Optimization for Optimal Allocation of Multiple Distributed Generations. IEEE Access, 8, 178493-178513. + * **ModifiedAEO**: Menesy, A. S., Sultan, H. M., Korashy, A., Banakhr, F. A., Ashmawy, M. G., & Kamel, S. (2020). Effective parameter extraction of different polymer electrolyte membrane fuel cell stack models using a modified artificial ecosystem optimization algorithm. IEEE Access, 8, 31892-31909. +* **ASO - Atom Search Optimization** + * **OriginalASO**: Zhao, W., Wang, L., & Zhang, Z. (2019). Atom search optimization and its application to solve a hydrogeologic parameter estimation problem. Knowledge-Based Systems, 163, 283-304. +* **ArchOA - Archimedes Optimization Algorithm** + * **OriginalArchOA**: Hashim, F. A., Hussain, K., Houssein, E. H., Mabrouk, M. S., & Al-Atabany, W. (2021). Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems. Applied Intelligence, 51(3), 1531-1551. +* **AOA - Arithmetic Optimization Algorithm** + * **OriginalAOA**: Abualigah, L., Diabat, A., Mirjalili, S., Abd Elaziz, M., & Gandomi, A. H. (2021). The arithmetic optimization algorithm. Computer methods in applied mechanics and engineering, 376, 113609. +* **AO - Aquila Optimizer** + * **OriginalAO**: Abualigah, L., Yousri, D., Abd Elaziz, M., Ewees, A. A., Al-qaness, M. A., & Gandomi, A. H. (2021). Aquila Optimizer: A novel meta-heuristic optimization Algorithm. Computers & Industrial Engineering, 157, 107250. +* **AVOA - African Vultures Optimization Algorithm** + * **OriginalAVOA**: Abdollahzadeh, B., Gharehchopogh, F. S., & Mirjalili, S. (2021). African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems. Computers & Industrial Engineering, 158, 107408. +* **AGTO - Artificial Gorilla Troops Optimization** + * **OriginalAGTO**: Abdollahzadeh, B., Soleimanian Gharehchopogh, F., & Mirjalili, S. (2021). Artificial gorilla troops optimizer: a new nature‐inspired metaheuristic algorithm for global optimization problems. International Journal of Intelligent Systems, 36(10), 5887-5958. +* **ARO - Artificial Rabbits Optimization**: + * **OriginalARO**: Wang, L., Cao, Q., Zhang, Z., Mirjalili, S., & Zhao, W. (2022). Artificial rabbits optimization: A new bio-inspired meta-heuristic algorithm for solving engineering optimization problems. Engineering Applications of Artificial Intelligence, 114, 105082. +### B +* **BFO - Bacterial Foraging Optimization** + * **OriginalBFO**: Passino, K. M. (2002). Biomimicry of bacterial foraging for distributed optimization and control. IEEE control systems magazine, 22(3), 52-67. + * **ABFO**: Nguyen, T., Nguyen, B. M., & Nguyen, G. (2019, April). Building resource auto-scaler with functional-link neural network and adaptive bacterial foraging optimization. In International Conference on Theory and Applications of Models of Computation (pp. 501-517). Springer, Cham. +* **BeesA - Bees Algorithm** + * **OriginalBeesA**: Pham, D. T., Ghanbarzadeh, A., Koc, E., Otri, S., Rahim, S., & Zaidi, M. (2005). The bees algorithm. Technical Note, Manufacturing Engineering Centre, Cardiff University, UK. + * **ProbBeesA**: The probabilitic version of: Pham, D. T., Ghanbarzadeh, A., Koç, E., Otri, S., Rahim, S., & Zaidi, M. (2006). The bees algorithm—a novel tool for complex optimisation problems. In Intelligent production machines and systems (pp. 454-459). Elsevier Science Ltd. +* **BBO - Biogeography-Based Optimization** + * **OriginalBBO**: Simon, D. (2008). Biogeography-based optimization. IEEE transactions on evolutionary computation, 12(6), 702-713. + * **BaseBBO**: The developed version +* **BA - Bat Algorithm** + * **OriginalBA**: Yang, X. S. (2010). A new metaheuristic bat-inspired algorithm. In Nature inspired cooperative strategies for optimization (NICSO 2010) (pp. 65-74). Springer, Berlin, Heidelberg. + * **AdaptiveBA**: Wang, X., Wang, W. and Wang, Y., 2013, July. An adaptive bat algorithm. In International Conference on Intelligent Computing(pp. 216-223). Springer, Berlin, Heidelberg. + * **ModifiedBA**: Dong, H., Li, T., Ding, R. and Sun, J., 2018. A novel hybrid genetic algorithm with granular information for feature selection and optimization. Applied Soft Computing, 65, pp.33-46. +* **BSO - Brain Storm Optimization** + * **OriginalBSO**: . Shi, Y. (2011, June). Brain storm optimization algorithm. In International conference in swarm intelligence (pp. 303-309). Springer, Berlin, Heidelberg. + * **ImprovedBSO**: El-Abd, M., 2017. Global-best brain storm optimization algorithm. Swarm and evolutionary computation, 37, pp.27-44. +* **BSA - Bird Swarm Algorithm** + * **OriginalBSA**: Meng, X. B., Gao, X. Z., Lu, L., Liu, Y., & Zhang, H. (2016). A new bio-inspired optimisation algorithm:Bird Swarm Algorithm. Journal of Experimental & Theoretical Artificial Intelligence, 28(4), 673-687. +* **BMO - Barnacles Mating Optimizer**: + * **OriginalBMO**: Sulaiman, M. H., Mustaffa, Z., Saari, M. M., Daniyal, H., Daud, M. R., Razali, S., & Mohamed, A. I. (2018, June). Barnacles mating optimizer: a bio-inspired algorithm for solving optimization problems. In 2018 19th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) (pp. 265-270). IEEE. +* **BES - Bald Eagle Search** + * **OriginalBES**: Alsattar, H. A., Zaidan, A. A., & Zaidan, B. B. (2019). Novel meta-heuristic bald eagle search optimisation algorithm. Artificial Intelligence Review, 1-28. +* **BRO - Battle Royale Optimization** + * **OriginalBRO**: Rahkar Farshi, T. (2020). Battle royale optimization algorithm. Neural Computing and Applications, 1-19. + * **BaseBRO**: The developed version +### C +* **CA - Culture Algorithm** + * **OriginalCA**: Reynolds, R.G., 1994, February. An introduction to cultural algorithms. In Proceedings of the third annual conference on evolutionary programming (Vol. 24, pp. 131-139). River Edge, NJ: World Scientific. +* **CEM - Cross Entropy Method** + * **OriginalCEM**: Rubinstein, R. (1999). The cross-entropy method for combinatorial and continuous optimization. Methodology and computing in applied probability, 1(2), 127-190. +* **CSO - Cat Swarm Optimization** + * **OriginalCSO**: Chu, S. C., Tsai, P. W., & Pan, J. S. (2006, August). Cat swarm optimization. In Pacific Rim international conference on artificial intelligence (pp. 854-858). Springer, Berlin, Heidelberg. +* **CSA - Cuckoo Search Algorithm** + * **OriginalCSA**: Yang, X. S., & Deb, S. (2009, December). Cuckoo search via Lévy flights. In 2009 World congress on nature & biologically inspired computing (NaBIC) (pp. 210-214). Ieee. +* **CRO - Coral Reefs Optimization** + * **OriginalCRO**: Salcedo-Sanz, S., Del Ser, J., Landa-Torres, I., Gil-López, S., & Portilla-Figueras, J. A. (2014). The coral reefs optimization algorithm: a novel metaheuristic for efficiently solving optimization problems. The Scientific World Journal, 2014. + * **OCRO**: Nguyen, T., Nguyen, T., Nguyen, B. M., & Nguyen, G. (2019). Efficient time-series forecasting using neural network and opposition-based coral reefs optimization. International Journal of Computational Intelligence Systems, 12(2), 1144-1161. +* **COA - Coyote Optimization Algorithm** + * **OriginalCOA**: Pierezan, J., & Coelho, L. D. S. (2018, July). Coyote optimization algorithm: a new metaheuristic for global optimization problems. In 2018 IEEE congress on evolutionary computation (CEC) (pp. 1-8). IEEE. +* **CHIO - Coronavirus Herd Immunity Optimization** + * **OriginalCHIO**: Al-Betar, M. A., Alyasseri, Z. A. A., Awadallah, M. A., & Abu Doush, I. (2021). Coronavirus herd immunity optimizer (CHIO). Neural Computing and Applications, 33(10), 5011-5042. + * **BaseCHIO**: The developed version +* **CGO - Chaos Game Optimization** + * **OriginalCGO**: Talatahari, S., & Azizi, M. (2021). Chaos Game Optimization: a novel metaheuristic algorithm. Artificial Intelligence Review, 54(2), 917-1004. +* **CSA - Circle Search Algorithm** + * **OriginalCSA**: Qais, M. H., Hasanien, H. M., Turky, R. A., Alghuwainem, S., Tostado-Véliz, M., & Jurado, F. (2022). Circle Search Algorithm: A Geometry-Based Metaheuristic Optimization Algorithm. Mathematics, 10(10), 1626. +### D +* **DE - Differential Evolution** + * **BaseDE**: Storn, R., & Price, K. (1997). Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization, 11(4), 341-359. + * **JADE**: Zhang, J., & Sanderson, A. C. (2009). JADE: adaptive differential evolution with optional external archive. IEEE Transactions on evolutionary computation, 13(5), 945-958. + * **SADE**: Qin, A. K., & Suganthan, P. N. (2005, September). Self-adaptive differential evolution algorithm for numerical optimization. In 2005 IEEE congress on evolutionary computation (Vol. 2, pp. 1785-1791). IEEE. + * **SHADE**: Tanabe, R., & Fukunaga, A. (2013, June). Success-history based parameter adaptation for differential evolution. In 2013 IEEE congress on evolutionary computation (pp. 71-78). IEEE. + * **L_SHADE**: Tanabe, R., & Fukunaga, A. S. (2014, July). Improving the search performance of SHADE using linear population size reduction. In 2014 IEEE congress on evolutionary computation (CEC) (pp. 1658-1665). IEEE. + * **SAP_DE**: Teo, J. (2006). Exploring dynamic self-adaptive populations in differential evolution. Soft Computing, 10(8), 673-686. +* **DSA - Differential Search Algorithm (not done)** + * **BaseDSA**: Civicioglu, P. (2012). Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm. Computers & Geosciences, 46, 229-247. +* **DO - Dragonfly Optimization** + * **OriginalDO**: Mirjalili, S. (2016). Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Computing and Applications, 27(4), 1053-1073. +* **DMOA - Dwarf Mongoose Optimization Algorithm** + * **OriginalDMOA**: Agushaka, J. O., Ezugwu, A. E., & Abualigah, L. (2022). Dwarf mongoose optimization algorithm. Computer methods in applied mechanics and engineering, 391, 114570. + * **DevDMOA**: The developed version +### E +* **ES - Evolution Strategies** . + * **OriginalES**: Schwefel, H. P. (1984). Evolution strategies: A family of non-linear optimization techniques based on imitating some principles of organic evolution. Annals of Operations Research, 1(2), 165-167. + * **LevyES**: Zhang, S., & Salari, E. (2005). Competitive learning vector quantization with evolution strategies for image compression. Optical Engineering, 44(2), 027006. +* **EP - Evolutionary programming** . + * **OriginalEP**: Fogel, L. J. (1994). Evolutionary programming in perspective: The top-down view. Computational intelligence: Imitating life. + * **LevyEP**: Lee, C.Y. and Yao, X., 2001, May. Evolutionary algorithms with adaptive lévy mutations. In Proceedings of the 2001 congress on evolutionary computation (IEEE Cat. No. 01TH8546) (Vol. 1, pp. 568-575). IEEE. +* **EHO - Elephant Herding Optimization** . + * **OriginalEHO**: Wang, G. G., Deb, S., & Coelho, L. D. S. (2015, December). Elephant herding optimization. In 2015 3rd International Symposium on Computational and Business Intelligence (ISCBI) (pp. 1-5). IEEE. +* **EFO - Electromagnetic Field Optimization** . + * **OriginalEFO**:Abedinpourshotorban, H., Shamsuddin, S. M., Beheshti, Z., & Jawawi, D. N. (2016). Electromagnetic field optimization: A physics-inspired metaheuristic optimization algorithm. Swarm and Evolutionary Computation, 26, 8-22. + * **BaseEFO**: The developed version +* **EOA - Earthworm Optimisation Algorithm** . + * **OriginalEOA**: Wang, G. G., Deb, S., & dos Santos Coelho, L. (2018). Earthworm optimisation algorithm: a bio-inspired metaheuristic algorithm for global optimisation problems. IJBIC, 12(1), 1-22. +* **EO - Equilibrium Optimizer** . + * **OriginalEO**: Faramarzi, A., Heidarinejad, M., Stephens, B., & Mirjalili, S. (2019). Equilibrium optimizer: A novel optimization algorithm. Knowledge-Based Systems. + * **ModifiedEO**: Gupta, S., Deep, K., & Mirjalili, S. (2020). An efficient equilibrium optimizer with mutation strategy for numerical optimization. Applied Soft Computing, 96, 106542. + * **AdaptiveEO**: Wunnava, A., Naik, M. K., Panda, R., Jena, B., & Abraham, A. (2020). A novel interdependence based multilevel thresholding technique using adaptive equilibrium optimizer. Engineering Applications of Artificial Intelligence, 94, 103836. +### F +* **FFA - Firefly Algorithm** + * **OriginalFFA**: Łukasik, S., & Żak, S. (2009, October). Firefly algorithm for continuous constrained optimization tasks. In International conference on computational collective intelligence (pp. 97-106). Springer, Berlin, Heidelberg. +* **FA - Fireworks algorithm** + * **OriginalFA**: Tan, Y., & Zhu, Y. (2010, June). Fireworks algorithm for optimization. In International conference in swarm intelligence (pp. 355-364). Springer, Berlin, Heidelberg. +* **FPA - Flower Pollination Algorithm** + * **OriginalFPA**: Yang, X. S. (2012, September). Flower pollination algorithm for global optimization. In International conference on unconventional computing and natural computation (pp. 240-249). Springer, Berlin, Heidelberg. +* **FOA - Fruit-fly Optimization Algorithm** + * **OriginalFOA**: Pan, W. T. (2012). A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowledge-Based Systems, 26, 69-74. + * **BaseFOA**: The developed version + * **WhaleFOA**: Fan, Y., Wang, P., Heidari, A. A., Wang, M., Zhao, X., Chen, H., & Li, C. (2020). Boosted hunting-based fruit fly optimization and advances in real-world problems. Expert Systems with Applications, 159, 113502. +* **FBIO - Forensic-Based Investigation Optimization** + * **OriginalFBIO**: Chou, J.S. and Nguyen, N.M., 2020. FBI inspired meta-optimization. Applied Soft Computing, p.106339. + * **BaseFBIO**: Fathy, A., Rezk, H. and Alanazi, T.M., 2021. Recent approach of forensic-based investigation algorithm for optimizing fractional order PID-based MPPT with proton exchange membrane fuel cell.IEEE Access,9, pp.18974-18992. +* **FHO - Fire Hawk Optimization** + * **OriginalFHO**: Azizi, M., Talatahari, S., & Gandomi, A. H. (2022). Fire Hawk Optimizer: a novel metaheuristic algorithm. Artificial Intelligence Review, 1-77. +### G +* **GA - Genetic Algorithm** + * **BaseGA**: Holland, J. H. (1992). Genetic algorithms. Scientific american, 267(1), 66-73. + * **SingleGA**: De Falco, I., Della Cioppa, A. and Tarantino, E., 2002. Mutation-based genetic algorithm: performance evaluation. Applied Soft Computing, 1(4), pp.285-299. + * **MultiGA**: De Jong, K.A. and Spears, W.M., 1992. A formal analysis of the role of multi-point crossover in genetic algorithms. Annals of mathematics and Artificial intelligence, 5(1), pp.1-26. + * **EliteSingleGA**: Elite version of Single-point mutation GA + * **EliteMultiGA**: Elite version of Multiple-point mutation GA +* **GWO - Grey Wolf Optimizer** + * **OriginalGWO**: Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in engineering software, 69, 46-61. + * **RW_GWO**: Gupta, S., & Deep, K. (2019). A novel random walk grey wolf optimizer. Swarm and evolutionary computation, 44, 101-112. + * **GWO_WOA**: Obadina, O. O., Thaha, M. A., Althoefer, K., & Shaheed, M. H. (2022). Dynamic characterization of a master–slave robotic manipulator using a hybrid grey wolf–whale optimization algorithm. Journal of Vibration and Control, 28(15-16), 1992-2003. +* **GOA - Grasshopper Optimisation Algorithm** + * **OriginalGOA**: Saremi, S., Mirjalili, S., & Lewis, A. (2017). Grasshopper optimisation algorithm: theory and application. Advances in Engineering Software, 105, 30-47. +* **GCO - Germinal Center Optimization** + * **OriginalGCO**: Villaseñor, C., Arana-Daniel, N., Alanis, A. Y., López-Franco, C., & Hernandez-Vargas, E. A. (2018). Germinal center optimization algorithm. International Journal of Computational Intelligence Systems, 12(1), 13-27. + * **BaseGCO**: The developed version +* **GSKA - Gaining Sharing Knowledge-based Algorithm** + * **OriginalGSKA**: Mohamed, A. W., Hadi, A. A., & Mohamed, A. K. (2019). Gaining-sharing knowledge based algorithm for solving optimization problems: a novel nature-inspired algorithm. International Journal of Machine Learning and Cybernetics, 1-29. + * **BaseGSKA**: Mohamed, A.W., Hadi, A.A., Mohamed, A.K. and Awad, N.H., 2020, July. Evaluating the performance of adaptive GainingSharing knowledge based algorithm on CEC 2020 benchmark problems. In 2020 IEEE Congress on Evolutionary Computation (CEC) (pp. 1-8). IEEE. +* **GBO - Gradient-Based Optimizer** + * **OriginalGBO**: Ahmadianfar, I., Bozorg-Haddad, O., & Chu, X. (2020). Gradient-based optimizer: A new metaheuristic optimization algorithm. Information Sciences, 540, 131-159. +### H +* **HC - Hill Climbing** . + * **OriginalHC**: Talbi, E. G., & Muntean, T. (1993, January). Hill-climbing, simulated annealing and genetic algorithms: a comparative study and application to the mapping problem. In [1993] Proceedings of the Twenty-sixth Hawaii International Conference on System Sciences (Vol. 2, pp. 565-573). IEEE. + * **SwarmHC**: The developed version based on swarm-based idea (Original is single-solution based method) +* **HS - Harmony Search** . + * **OriginalHS**: Geem, Z. W., Kim, J. H., & Loganathan, G. V. (2001). A new heuristic optimization algorithm:harmony search. simulation, 76(2), 60-68. + * **BaseHS**: The developed version +* **HHO - Harris Hawks Optimization** . + * **OriginalHHO**: Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems, 97, 849-872. +* **HGSO - Henry Gas Solubility Optimization** . + * **OriginalHGSO**: Hashim, F. A., Houssein, E. H., Mabrouk, M. S., Al-Atabany, W., & Mirjalili, S. (2019). Henry gas solubility optimization: A novel physics-based algorithm. Future Generation Computer Systems, 101, 646-667. +* **HGS - Hunger Games Search** . + * **OriginalHGS**: Yang, Y., Chen, H., Heidari, A. A., & Gandomi, A. H. (2021). Hunger games search:Visions, conception, implementation, deep analysis, perspectives, and towards performance shifts. Expert Systems with Applications, 177, 114864. +* **HHOA - Horse Herd Optimization Algorithm (not done)** . + * **BaseHHOA**: MiarNaeimi, F., Azizyan, G., & Rashki, M. (2021). Horse herd optimization algorithm: A nature-inspired algorithm for high-dimensional optimization problems. Knowledge-Based Systems, 213, 106711. +* **HBA - Honey Badger Algorithm**: + * **OriginalHBA**: Hashim, F. A., Houssein, E. H., Hussain, K., Mabrouk, M. S., & Al-Atabany, W. (2022). Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems. Mathematics and Computers in Simulation, 192, 84-110. +### I +* **IWO - Invasive Weed Optimization** . + * **OriginalIWO**: Mehrabian, A. R., & Lucas, C. (2006). A novel numerical optimization algorithm inspired from weed colonization. Ecological informatics, 1(4), 355-366. +* **ICA - Imperialist Competitive Algorithm** + * **OriginalICA**: Atashpaz-Gargari, E., & Lucas, C. (2007, September). Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In 2007 IEEE congress on evolutionary computation (pp. 4661-4667). Ieee. +* **INFO - weIghted meaN oF vectOrs**: + * **OriginalINFO**: Ahmadianfar, I., Heidari, A. A., Gandomi, A. H., Chu, X., & Chen, H. (2021). RUN beyond the metaphor: An efficient optimization algorithm based on Runge Kutta method. Expert Systems with Applications, 181, 115079. +### J +* **JA - Jaya Algorithm** + * **OriginalJA**: Rao, R. (2016). Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. International Journal of Industrial Engineering Computations, 7(1), 19-34. + * **BaseJA**: The developed version + * **LevyJA**: Iacca, G., dos Santos Junior, V. C., & de Melo, V. V. (2021). An improved Jaya optimization algorithm with Levy flight. Expert Systems with Applications, 165, 113902. +### K +### L +* **LCO - Life Choice-based Optimization** + * **OriginalLCO**: Khatri, A., Gaba, A., Rana, K. P. S., & Kumar, V. (2019). A novel life choice-based optimizer. Soft Computing, 1-21. + * **BaseLCO**: The developed version + * **ImprovedLCO**: The improved version using Gaussian distribution and Mutation Mechanism +### M +* **MA - Memetic Algorithm** + * **OriginalMA**: Moscato, P. (1989). On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms. Caltech concurrent computation program, C3P Report, 826, 1989. +* **MFO - Moth Flame Optimization** + * **OriginalMFO**: Mirjalili, S. (2015). Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-based systems, 89, 228-249. + * **BaseMFO**: The developed version +* **MVO - Multi-Verse Optimizer** + * **OriginalMVO**: Mirjalili, S., Mirjalili, S. M., & Hatamlou, A. (2016). Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Computing and Applications, 27(2), 495-513. + * **BaseMVO**: The developed version +* **MSA - Moth Search Algorithm** + * **OriginalMSA**: Wang, G. G. (2018). Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems. Memetic Computing, 10(2), 151-164. +* **MRFO - Manta Ray Foraging Optimization** + * **OriginalMRFO**: Zhao, W., Zhang, Z., & Wang, L. (2020). Manta ray foraging optimization: An effective bio-inspired optimizer for engineering applications. Engineering Applications of Artificial Intelligence, 87, 103300. +* **MPA - Marine Predators Algorithm**: + * **OriginalMPA**: Faramarzi, A., Heidarinejad, M., Mirjalili, S., & Gandomi, A. H. (2020). Marine Predators Algorithm: A nature-inspired metaheuristic. Expert systems with applications, 152, 113377. +### N +* **NRO - Nuclear Reaction Optimization** + * **OriginalNRO**: Wei, Z., Huang, C., Wang, X., Han, T., & Li, Y. (2019). Nuclear Reaction Optimization: A novel and powerful physics-based algorithm for global optimization. IEEE Access. +* **NMRA - Nake Mole-Rat Algorithm** + * **OriginalNMRA**: Salgotra, R., & Singh, U. (2019). The naked mole-rat algorithm. Neural Computing and Applications, 31(12), 8837-8857. + * **ImprovedNMRA**: Singh, P., Mittal, N., Singh, U. and Salgotra, R., 2021. Naked mole-rat algorithm with improved exploration and exploitation capabilities to determine 2D and 3D coordinates of sensor nodes in WSNs. Arabian Journal for Science and Engineering, 46(2), pp.1155-1178. +### O +### P +* **PSO - Particle Swarm Optimization** + * **OriginalPSO**: Eberhart, R., & Kennedy, J. (1995, October). A new optimizer using particle swarm theory. In MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science (pp. 39-43). Ieee. + * **PPSO**: Ghasemi, M., Akbari, E., Rahimnejad, A., Razavi, S. E., Ghavidel, S., & Li, L. (2019). Phasor particle swarm optimization: a simple and efficient variant of PSO. Soft Computing, 23(19), 9701-9718. + * **HPSO_TVAC**: Ghasemi, M., Aghaei, J., & Hadipour, M. (2017). New self-organising hierarchical PSO with jumping time-varying acceleration coefficients. Electronics Letters, 53(20), 1360-1362. + * **C_PSO**: Liu, B., Wang, L., Jin, Y. H., Tang, F., & Huang, D. X. (2005). Improved particle swarm optimization combined with chaos. Chaos, Solitons & Fractals, 25(5), 1261-1271. + * **CL_PSO**: Liang, J. J., Qin, A. K., Suganthan, P. N., & Baskar, S. (2006). Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE transactions on evolutionary computation, 10(3), 281-295. +* **PFA - Pathfinder Algorithm** + * **OriginalPFA**: Yapici, H., & Cetinkaya, N. (2019). A new meta-heuristic optimizer: Pathfinder algorithm. Applied Soft Computing, 78, 545-568. +* **PSS - Pareto-like Sequential Sampling** + * **OriginalPSS**: Shaqfa, M., & Beyer, K. (2021). Pareto-like sequential sampling heuristic for global optimisation. Soft Computing, 25(14), 9077-9096. +### Q +* **QSA - Queuing Search Algorithm** + * **OriginalQSA**: Zhang, J., Xiao, M., Gao, L., & Pan, Q. (2018). Queuing search algorithm: A novel metaheuristic algorithm for solving engineering optimization problems. Applied Mathematical Modelling, 63, 464-490. + * **BaseQSA**: The developed version + * **OppoQSA**: Zheng, X. and Nguyen, H., 2022. A novel artificial intelligent model for predicting water treatment efficiency of various biochar systems based on artificial neural network and queuing search algorithm. Chemosphere, 287, p.132251. + * **LevyQSA**: Abderazek, H., Hamza, F., Yildiz, A.R., Gao, L. and Sait, S.M., 2021. A comparative analysis of the queuing search algorithm, the sine-cosine algorithm, the ant lion algorithm to determine the optimal weight design problem of a spur gear drive system. Materials Testing, 63(5), pp.442-447. + * **ImprovedQSA**: Nguyen, B.M., Hoang, B., Nguyen, T. and Nguyen, G., 2021. nQSV-Net: a novel queuing search variant for global space search and workload modeling. Journal of Ambient Intelligence and Humanized Computing, 12(1), pp.27-46. +### R +* **RUN - RUNge Kutta optimizer**: + * **OriginalRUN**: Ahmadianfar, I., Heidari, A. A., Gandomi, A. H., Chu, X., & Chen, H. (2021). RUN beyond the metaphor: An efficient optimization algorithm based on Runge Kutta method. Expert Systems with Applications, 181, 115079. +### S +* **SA - Simulated Annealling** + * **OriginalSA**: . Van Laarhoven, P. J., & Aarts, E. H. (1987). Simulated annealing. In Simulated annealing: Theory and applications (pp. 7-15). Springer, Dordrecht. +* **SSpiderO - Social Spider Optimization** + * **OriginalSSpiderO**: Cuevas, E., Cienfuegos, M., ZaldíVar, D., & Pérez-Cisneros, M. (2013). A swarm optimization algorithm inspired in the behavior of the social-spider. Expert Systems with Applications, 40(16), 6374-6384. +* **SOS - Symbiotic Organisms Search**: + * **OriginalSOS**: Cheng, M. Y., & Prayogo, D. (2014). Symbiotic organisms search: a new metaheuristic optimization algorithm. Computers & Structures, 139, 98-112. +* **SSpiderA - Social Spider Algorithm** + * **OriginalSSpiderA**: James, J. Q., & Li, V. O. (2015). A social spider algorithm for global optimization. Applied Soft Computing, 30, 614-627. +* **SCA - Sine Cosine Algorithm** + * **OriginalSCA**: Mirjalili, S. (2016). SCA: a sine cosine algorithm for solving optimization problems. Knowledge-Based Systems, 96, 120-133. + * **BaseSCA**: Attia, A.F., El Sehiemy, R.A. and Hasanien, H.M., 2018. Optimal power flow solution in power systems using a novel Sine-Cosine algorithm. International Journal of Electrical Power & Energy Systems, 99, pp.331-343. +* **SRSR - Swarm Robotics Search And Rescue** + * **OriginalSRSR**: Bakhshipour, M., Ghadi, M. J., & Namdari, F. (2017). Swarm robotics search & rescue: A novel artificial intelligence-inspired optimization approach. Applied Soft Computing, 57, 708-726. +* **SBO - Satin Bowerbird Optimizer** + * **OriginalSBO**: Moosavi, S. H. S., & Bardsiri, V. K. (2017). Satin bowerbird optimizer: a new optimization algorithm to optimize ANFIS for software development effort estimation. Engineering Applications of Artificial Intelligence, 60, 1-15. + * **BaseSBO**: The developed version +* **SHO - Spotted Hyena Optimizer** + * **OriginalSHO**: Dhiman, G., & Kumar, V. (2017). Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications. Advances in Engineering Software, 114, 48-70. +* **SSO - Salp Swarm Optimization** + * **OriginalSSO**: Mirjalili, S., Gandomi, A. H., Mirjalili, S. Z., Saremi, S., Faris, H., & Mirjalili, S. M. (2017). Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Software, 114, 163-191. +* **SFO - Sailfish Optimizer** + * **OriginalSFO**: Shadravan, S., Naji, H. R., & Bardsiri, V. K. (2019). The Sailfish Optimizer: A novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems. Engineering Applications of Artificial Intelligence, 80, 20-34. + * **ImprovedSFO**: Li, L.L., Shen, Q., Tseng, M.L. and Luo, S., 2021. Power system hybrid dynamic economic emission dispatch with wind energy based on improved sailfish algorithm. Journal of Cleaner Production, 316, p.128318. +* **SARO - Search And Rescue Optimization** + * **OriginalSARO**: Shabani, A., Asgarian, B., Gharebaghi, S. A., Salido, M. A., & Giret, A. (2019). A New Optimization Algorithm Based on Search and Rescue Operations. Mathematical Problems in Engineering, 2019. + * **BaseSARO**: The developed version using Levy-flight +* **SSDO - Social Ski-Driver Optimization** + * **OriginalSSDO**: Tharwat, A., & Gabel, T. (2019). Parameters optimization of support vector machines for imbalanced data using social ski driver algorithm. Neural Computing and Applications, 1-14. +* **SLO - Sea Lion Optimization** + * **OriginalSLO**: Masadeh, R., Mahafzah, B. A., & Sharieh, A. (2019). Sea Lion Optimization Algorithm. Sea, 10(5). + * **ImprovedSLO**: The developed version + * **ModifiedSLO**: Masadeh, R., Alsharman, N., Sharieh, A., Mahafzah, B.A. and Abdulrahman, A., 2021. Task scheduling on cloud computing based on sea lion optimization algorithm. International Journal of Web Information Systems. +* **Seagull Optimization Algorithm** + * **OriginalSOA**: Dhiman, G., & Kumar, V. (2019). Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems. Knowledge-based systems, 165, 169-196. + * **DevSOA**: The developed version +* **SMA - Slime Mould Algorithm** + * **OriginalSMA**: Li, S., Chen, H., Wang, M., Heidari, A. A., & Mirjalili, S. (2020). Slime mould algorithm: A new method for stochastic optimization. Future Generation Computer Systems. + * **BaseSMA**: The developed version +* **SSA - Sparrow Search Algorithm** + * **OriginalSSA**: Jiankai Xue & Bo Shen (2020) A novel swarm intelligence optimization approach: sparrow search algorithm, Systems Science & Control Engineering, 8:1, 22-34, DOI: 10.1080/21642583.2019.1708830 + * **BaseSSA**: The developed version +* **SPBO - Student Psychology Based Optimization** + * **OriginalSPBO**: Das, B., Mukherjee, V., & Das, D. (2020). Student psychology based optimization algorithm: A new population based optimization algorithm for solving optimization problems. Advances in Engineering software, 146, 102804. + * **DevSPBO**: The developed version +* **SCSO - Sand Cat Swarm Optimization** + * **OriginalSCSO**: Seyyedabbasi, A., & Kiani, F. (2022). Sand Cat swarm optimization: a nature-inspired algorithm to solve global optimization problems. Engineering with Computers, 1-25. +### T +* **TLO - Teaching Learning Optimization** + * **OriginalTLO**: Rao, R. V., Savsani, V. J., & Vakharia, D. P. (2011). Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-Aided Design, 43(3), 303-315. + * **BaseTLO**: Rao, R., & Patel, V. (2012). An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems. International Journal of Industrial Engineering Computations, 3(4), 535-560. + * **ImprovedTLO**: Rao, R. V., & Patel, V. (2013). An improved teaching-learning-based optimization algorithm for solving unconstrained optimization problems. Scientia Iranica, 20(3), 710-720. +* **TWO - Tug of War Optimization** + * **OriginalTWO**: Kaveh, A., & Zolghadr, A. (2016). A novel meta-heuristic algorithm: tug of war optimization. Iran University of Science & Technology, 6(4), 469-492. + * **OppoTWO**: Kaveh, A., Almasi, P. and Khodagholi, A., 2022. Optimum Design of Castellated Beams Using Four Recently Developed Meta-heuristic Algorithms. Iranian Journal of Science and Technology, Transactions of Civil Engineering, pp.1-13. + * **LevyTWO**: The developed version using Levy-flight + * **ImprovedTWO**: Nguyen, T., Hoang, B., Nguyen, G., & Nguyen, B. M. (2020). A new workload prediction model using extreme learning machine and enhanced tug of war optimization. Procedia Computer Science, 170, 362-369. +* **TSA - Tunicate Swarm Algorithm** + * **OriginalTSA**: Kaur, S., Awasthi, L. K., Sangal, A. L., & Dhiman, G. (2020). Tunicate Swarm Algorithm: A new bio-inspired based metaheuristic paradigm for global optimization. Engineering Applications of Artificial Intelligence, 90, 103541. +* **TSO - Tuna Swarm Optimization** + * **OriginalTSO**: Xie, L., Han, T., Zhou, H., Zhang, Z. R., Han, B., & Tang, A. (2021). Tuna swarm optimization: a novel swarm-based metaheuristic algorithm for global optimization. Computational intelligence and Neuroscience, 2021. +### U +### V +* **VCS - Virus Colony Search** + * **OriginalVCS**: Li, M. D., Zhao, H., Weng, X. W., & Han, T. (2016). A novel nature-inspired algorithm for optimization: Virus colony search. Advances in Engineering Software, 92, 65-88. + * **BaseVCS**: The developed version +### W +* **WCA - Water Cycle Algorithm** + * **OriginalWCA**: Eskandar, H., Sadollah, A., Bahreininejad, A., & Hamdi, M. (2012). Water cycle algorithm–A novel metaheuristic optimization method for solving constrained engineering optimization problems. Computers & Structures, 110, 151-166. +* **WOA - Whale Optimization Algorithm** + * **OriginalWOA**: Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in engineering software, 95, 51-67. + * **HI_WOA**: Tang, C., Sun, W., Wu, W., & Xue, M. (2019, July). A hybrid improved whale optimization algorithm. In 2019 IEEE 15th International Conference on Control and Automation (ICCA) (pp. 362-367). IEEE. +* **WHO - Wildebeest Herd Optimization** + * **OriginalWHO**: Amali, D., & Dinakaran, M. (2019). Wildebeest herd optimization: A new global optimization algorithm inspired by wildebeest herding behaviour. Journal of Intelligent & Fuzzy Systems, (Preprint), 1-14. +* **WDO - Wind Driven Optimization** + * **OriginalWDO**: Bayraktar, Z., Komurcu, M., Bossard, J.A. and Werner, D.H., 2013. The wind driven optimization technique and its application in electromagnetics. IEEE transactions on antennas and propagation, 61(5), pp.2745-2757. +### X +### Y +### Z + +%package -n python3-mealpy +Summary: MEALPY: An Open-source Library for Latest Meta-heuristic Algorithms in Python +Provides: python-mealpy +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-mealpy +[](https://github.com/thieu1995/mealpy/releases) +[](https://pypi.python.org/pypi/mealpy) +[](https://badge.fury.io/py/mealpy) + + + +[](https://pepy.tech/project/mealpy) +[](https://github.com/thieu1995/mealpy/actions/workflows/publish-package.yaml) + +[](https://mealpy.readthedocs.io/en/latest/?badge=latest) +[](https://t.me/+fRVCJGuGJg1mNDg1) +[](http://isitmaintained.com/project/thieu1995/mealpy "Average time to resolve an issue") +[](http://isitmaintained.com/project/thieu1995/mealpy "Percentage of issues still open") + +[](https://git-scm.com/book/en/v2/GitHub-Contributing-to-a-Project) +[](https://doi.org/10.5281/zenodo.3711948) +[](https://www.gnu.org/licenses/gpl-3.0) +MEALPY is the largest python library for most of the cutting-edge nature-inspired meta-heuristic algorithms (population-based). Population meta-heuristic algorithms (PMA) are the most popular algorithms in the field of +approximate optimization. +* **Free software:** GNU General Public License (GPL) V3 license +* **Total algorithms**: 174 (102 original, 45 official variants, 27 developed variants) +* **Documentation:** https://mealpy.readthedocs.io/en/latest/ +* **Python versions:** 3.7.x, 3.8.x, 3.9.x, 3.10.x, 3.11.x +* **Dependencies:** numpy, scipy, pandas, matplotlib +# Goals +Our goals are to implement all of the classical as well as the state-of-the-art nature-inspired algorithms, create a simple interface that helps researchers access optimization algorithms as quickly as possible, and share knowledge of the optimization field with everyone without a fee. What you can do with mealpy: +- Analyse parameters of meta-heuristic algorithms. +- Perform Qualitative and Quantitative Analysis of algorithms. +- Analyse rate of convergence of algorithms. +- Test and Analyse the scalability and the robustness of algorithms. +- Save results in various formats (csv, json, pickle, png, pdf, jpeg) +- Export and import models can also be done with Mealpy. +# Installation +### Install with pip +Install the [current PyPI release](https://pypi.python.org/pypi/mealpy): +```sh +$ pip install mealpy==2.5.3 +``` +### Install from source +In case you want to install directly from the source code, use: +```sh +$ git clone https://github.com/thieu1995/mealpy.git +$ cd mealpy +$ python setup.py install +``` +# Usage +After installation, you can import Mealpy as any other Python module: +```sh +$ python +>>> import mealpy +>>> mealpy.__version__ +``` +Let's go through a basic and advanced example. +## Examples +### Simple Benchmark Function +```python +from mealpy.bio_based import SMA +import numpy as np +def fitness_function(solution): + return np.sum(solution**2) +problem = { + "fit_func": fitness_function, + "lb": [-100, ] * 30, + "ub": [100, ] * 30, + "minmax": "min", + "log_to": None, + "save_population": False, +} +## Run the algorithm +model = SMA.BaseSMA(epoch=100, pop_size=50, pr=0.03) +best_position, best_fitness = model.solve(problem) +print(f"Best solution: {best_position}, Best fitness: {best_fitness}") +``` +### Constrained Benchmark Function +* [The Constrained Benchmark Function](https://github.com/thieu1995/mealpy/tree/master/examples/applications/run_constraint_functions.py) +### Multi-objective Benchmark Function +* [Multi-objective benchmark functions](https://github.com/thieu1995/mealpy/tree/master/examples/applications/run_multi_objective_functions.py) +### Custom Problem +For our custom problem, we can create a class and inherit from the Problem class, named the child class the +'Squared' class. In the initialization method of the 'Squared' class, we have to set the *lb*, *ub*, and *minmax* +of the problem (lb: a list of lower bound values, ub: a list of upper bound values, and minmax: a string specifying +whether the problem is a 'min' or 'max' problem). +Afterwards, we have to override the abstract method 'fit_func()', which takes a parameter 'solution' (the solution +to be evaluated) and returns the function value. The resulting code should look something like the code snippet +below. 'Name' is an additional parameter we want to include in this class, and you can include any other additional +parameters you need. +```python +import numpy as np +from mealpy.bio_based import BBO +from mealpy.utils.problem import Problem +# Our custom problem class +class Squared(Problem): + def __init__(self, lb=(-5, -5, -5, -5, -5, -5), ub=(5, 5, 5, 5, 5, 5), minmax="min", name="Squared", **kwargs): + super().__init__(lb, ub, minmax, **kwargs) + self.name = name + def fit_func(self, solution): + return np.sum(solution ** 2) +``` +Now, we define an algorithm, and pass an instance of our *Squared* class as the problem argument. +```python +problem = Squared(lb=[-10] * 20, ub=[10] * 20, minmax="min") +model = BBO.BaseBBO(epoch=10, pop_size=50) +best_position, best_fitness = model.solve(problem) +print(best_position) +print(best_fitness) +print(model.get_parameters()) +print(model.get_name()) +print(model.get_attributes()["solution"]) +print(model.problem.get_name()) +print(model.problem.n_dims) +``` +### Tuner class (GridSearchCV/ParameterSearch, Hyper-parameter tuning) +We build a dedicated class, Tuner, that can help you tune your algorithm's parameters. +```python +import numpy as np +from mealpy.bio_based import BBO +from mealpy.tuner import Tuner # Remember this +def fitness(solution): + return np.sum(solution**2) +problem = { + "lb": [-100, ]*50, + "ub": [100, ]*50, + "minmax": "min", + "fit_func": fitness, + "name": "Squared Problem", + "log_to": None, +} +paras_bbo_grid = { + "epoch": [100], + "pop_size": [50], + "elites": [2, 3, 4, 5], + "p_m": [0.01, 0.02, 0.05, 0.1, 0.15, 0.2] +} +term = { + "max_fe": 10000 +} +if __name__ == "__main__": + model = BBO.BaseBBO() + tuner = Tuner(model, paras_bbo_grid) + tuner.execute(problem=problem, termination=term, n_trials=5, n_jobs=5, mode="thread", n_workers=4, verbose=True) + ## Solve this problem 5 times (n_trials) using 5 processes (n_jobs), each process will handle 1 trial. + ## The mode to run the solver is thread (mode), we will calculate the fitness of 4 solutions (n_workers) at the same time + print(tuner.best_score) + print(tuner.best_params) + print(tuner.best_algorithm) + print(tuner.best_algorithm.get_name()) + ## Save results to csv file + tuner.export_results(save_path="history/tuning", save_as="csv") + ## Re-solve the best model on your problem + best_position, best_fitness = tuner.resolve() + print(best_position, best_fitness) + print(tuner.problem.get_name()) +``` +### Multitask class (Multitask solving) +We also build a dedicated class, Multitask, that can help you run several scenarios. For example: +1. Run 1 algorithm with 1 problem, and multiple trials +2. Run 1 algorithm with multiple problems, and multiple trials +3. Run multiple algorithms with 1 problem, and multiple trials +4. Run multiple algorithms with multiple problems, and multiple trials +```python +#### Using multiple algorithm to solve multiple problems with multiple trials +## Import libraries +## For example, we want to solve F5, F10, F29 problem in CEC-2017 +from opfunu.cec_based.cec2017 import F52017, F102017, F292017 +from mealpy.bio_based import BBO +from mealpy.evolutionary_based import DE +from mealpy.multitask import Multitask # Remember this +## You can define your own problems +f1 = F52017(30, f_bias=0) +f2 = F102017(30, f_bias=0) +f3 = F292017(30, f_bias=0) +p1 = { + "lb": f1.lb.tolist(), + "ub": f1.ub.tolist(), + "minmax": "min", + "fit_func": f1.evaluate, + "name": "F5-CEC2017", + "log_to": None, +} +p2 = { + "lb": f2.lb.tolist(), + "ub": f2.ub.tolist(), + "minmax": "min", + "fit_func": f2.evaluate, + "name": "F10-CEC2017", + "log_to": None, +} +p3 = { + "lb": f3.lb.tolist(), + "ub": f3.ub.tolist(), + "minmax": "min", + "fit_func": f3.evaluate, + "name": "F29-CEC2017", + "log_to": None, +} +## Define models +model1 = BBO.BaseBBO(epoch=10, pop_size=50) +model2 = BBO.OriginalBBO(epoch=10, pop_size=50) +model3 = DE.BaseDE(epoch=10, pop_size=50) +## Define termination if needed +term = { + "max_fe": 10000 +} +## Define and run Multitask +if __name__ == "__main__": + multitask = Multitask(algorithms=(model1, model2, model3), problems=(p1, p2, p3), terminations=(term, ), modes=("thread", )) + # default modes = "single", default termination = epoch (as defined in problem dictionary) + multitask.execute(n_trials=5, n_jobs=5, save_path="history", save_as="csv", save_convergence=False, verbose=False) + ## Check the directory: history/, you will see list of .csv result files +``` +For more usage examples please look at [examples](/examples) folder. +More advanced examples can also be found in the [Mealpy-examples repository](https://github.com/thieu1995/mealpy_examples). +### Get Visualize Figures +* [Tutorials](/examples/utils/visualize/all_charts.py) +<p align="center"><img src="https://thieu1995.github.io/post/2022-04/19-mealpy-tutorials/mealpy2.png" alt="MEALPY"/> +</p> +## Mealpy Application +### Mealpy + Neural Network (Replace the Gradient Descent Optimizer) +* Time-series Problem: + * Traditional MLP + code: [Link](https://github.com/thieu1995/mealpy/tree/master/examples/applications/keras/traditional-mlp-time-series.py) + * Hybrid code (Mealpy + + MLP): [Link](https://github.com/thieu1995/mealpy/tree/master/examples/applications/keras/mha-hybrid-mlp-time-series.py) +* Classification Problem: + * Traditional MLP + code: [Link](https://github.com/thieu1995/mealpy/blob/master/examples/applications/keras/traditional-mlp-classification.py) + * Hybrid code (Mealpy + + MLP): [Link](https://github.com/thieu1995/mealpy/blob/master/examples/applications/keras/mha-hybrid-mlp-classification.py) +### Mealpy + Neural Network (Optimize Neural Network Hyper-parameter) +Code: [Link](https://github.com/thieu1995/mealpy/blob/master/examples/applications/keras/mha-hyper-parameter-mlp-time-series.py) +### Other Applications +* Solving Knapsack Problem (Discrete + problems): [Link](https://github.com/thieu1995/mealpy/blob/master/examples/applications/discrete-problems/knapsack-problem.py) +* Optimize SVM (SVC) + model: [Link](https://github.com/thieu1995/mealpy/blob/master/examples/applications/sklearn/svm_classification.py) +* Optimize Linear Regression + Model: [Link](https://github.com/thieu1995/mealpy/blob/master/examples/applications/pytorch/linear_regression.py) +* Travelling Salesman Problem: https://github.com/thieu1995/MHA-TSP +* Feature selection problem: https://github.com/thieu1995/MHA-FS +## Tutorial Videos +All tutorial videos: [Link](https://mealpy.readthedocs.io/en/latest/pages/general/video_tutorials.html) +All code examples: [Link](https://github.com/thieu1995/mealpy/tree/master/examples) +All visualization examples: [Link](https://mealpy.readthedocs.io/en/latest/pages/visualization.html) +### Get helps (questions, problems) +* Official source code repo: https://github.com/thieu1995/mealpy +* Official document: https://mealpy.readthedocs.io/ +* Download releases: https://pypi.org/project/mealpy/ +* Issue tracker: https://github.com/thieu1995/mealpy/issues +* Notable changes log: https://github.com/thieu1995/mealpy/blob/master/ChangeLog.md +* Examples with different meapy version: https://github.com/thieu1995/mealpy/blob/master/EXAMPLES.md +* This project also related to our another projects which are "meta-heuristics" and "neural-network", check it here + * https://github.com/thieu1995/opfunu + * https://github.com/thieu1995/metaheuristics + * https://github.com/aiir-team +**Want to have an instant assistant? Join our telegram community at [link](https://t.me/+fRVCJGuGJg1mNDg1)** +We share lots of information, questions, and answers there. You will get more support and knowledge there. +### Cite Us +If you are using mealpy in your project, we would appreciate citations: +```bibtex +@article{van2023mealpy, + title={MEALPY: An open-source library for latest meta-heuristic algorithms in Python}, + author={Van Thieu, Nguyen and Mirjalili, Seyedali}, + journal={Journal of Systems Architecture}, + year={2023}, + publisher={Elsevier} +} +@article{van2023groundwater, + title={Groundwater level modeling using Augmented Artificial Ecosystem Optimization}, + author={Van Thieu, Nguyen and Barma, Surajit Deb and Van Lam, To and Kisi, Ozgur and Mahesha, Amai}, + journal={Journal of Hydrology}, + volume={617}, + pages={129034}, + year={2023}, + publisher={Elsevier} +} +``` +# List of papers used MEALPY +- Min, J., Oh, M., Kim, W., Seo, H., & Paek, J. (2022, October). Evaluation of Metaheuristic Algorithms for TAS Scheduling in Time-Sensitive Networking. In 2022 13th International Conference on Information and Communication Technology Convergence (ICTC) (pp. 809-812). IEEE. +- Khozeimeh, F., Sharifrazi, D., Izadi, N. H., Joloudari, J. H., Shoeibi, A., Alizadehsani, R., ... & Islam, S. M. S. (2021). Combining a convolutional neural network with autoencoders to predict the survival chance of COVID-19 patients. Scientific Reports, 11(1), 15343. +- Rajesh, K., Jain, E., & Kotecha, P. (2022). A Multi-Objective approach to the Electric Vehicle Routing Problem. arXiv preprint arXiv:2208.12440. +- Sánchez, A. J. H., & Upegui, F. R. (2022). Una herramienta para el diseño de redes MSMN de banda ancha en líneas de transmisión basada en algoritmos heurísticos de optimización comparados. Revista Ingeniería UC, 29(2), 106-123. +- Khanmohammadi, M., Armaghani, D. J., & Sabri Sabri, M. M. (2022). Prediction and Optimization of Pile Bearing Capacity Considering Effects of Time. Mathematics, 10(19), 3563. +- Kudela, J. (2023). The Evolutionary Computation Methods No One Should Use. arXiv preprint arXiv:2301.01984. +- Vieira, M., Faia, R., Pinto, T., & Vale, Z. (2022, September). Schedule Peer-to-Peer Transactions of an Energy Community Using Particle Swarm. In 2022 18th International Conference on the European Energy Market (EEM) (pp. 1-6). IEEE. +- Bui, X. N., Nguyen, H., Le, Q. T., & Le, T. N. Forecasting PM. MINING SCIENCE ANDTECHNOLOGY (Russia), 111. +- Bui, X. N., Nguyen, H., Le, Q. T., & Le, T. N. (2022). Forecasting PM 2.5 emissions in open-pit minesusing a functional link neural network optimized by various optimization algorithms. Gornye nauki i tekhnologii= Mining Science and Technology (Russia), 7(2), 111-125. +- Doğan, E., & Yörükeren, N. (2022). Enhancement of Transmission System Security with Archimedes Optimization Algorithm. +- Ayub, N., Aurangzeb, K., Awais, M., & Ali, U. (2020, November). Electricity theft detection using CNN-GRU and manta ray foraging optimization algorithm. In 2020 IEEE 23Rd international multitopic conference (INMIC) (pp. 1-6). IEEE. +- Pintilie, L., Nechita, M. T., Suditu, G. D., Dafinescu, V., & Drăgoi, E. N. (2022). Photo-decolorization of Eriochrome Black T: process optimization with Differential Evolution algorithm. In PASEW-22, MESSH-22 & CABES-22 April 19–21, 2022 Paris (France). Eminent Association of Pioneers. +- LaTorre, A., Molina, D., Osaba, E., Poyatos, J., Del Ser, J., & Herrera, F. (2021). A prescription of methodological guidelines for comparing bio-inspired optimization algorithms. Swarm and Evolutionary Computation, 67, 100973. +- Gottam, S., Nanda, S. J., & Maddila, R. K. (2021, December). A CNN-LSTM Model Trained with Grey Wolf Optimizer for Prediction of Household Power Consumption. In 2021 IEEE International Symposium on Smart Electronic Systems (iSES)(Formerly iNiS) (pp. 355-360). IEEE. +- Darius, P. S., Devadason, J., & Solomon, D. G. (2022, December). Prospects of Ant Colony Optimization (ACO) in Various Domains. In 2022 4th International Conference on Circuits, Control, Communication and Computing (I4C) (pp. 79-84). IEEE. +- Ayub, N., Irfan, M., Awais, M., Ali, U., Ali, T., Hamdi, M., ... & Muhammad, F. (2020). Big data analytics for short and medium-term electricity load forecasting using an AI techniques ensembler. Energies, 13(19), 5193. +- Biundini, I. Z., Melo, A. G., Coelho, F. O., Honório, L. M., Marcato, A. L., & Pinto, M. F. (2022). Experimentation and Simulation with Autonomous Coverage Path Planning for UAVs. Journal of Intelligent & Robotic Systems, 105(2), 46. +- Yousaf, I., Anwar, F., Imtiaz, S., Almadhor, A. S., Ishmanov, F., & Kim, S. W. (2022). An Optimized Hyperparameter of Convolutional Neural Network Algorithm for Bug Severity Prediction in Alzheimer’s-Based IoT System. Computational Intelligence and Neuroscience, 2022. +- Xu, L., Yan, W., & Ji, J. (2023). The research of a novel WOG-YOLO algorithm for autonomous driving object detection. Scientific reports, 13(1), 3699. +- Costache, R. D., Arabameri, A., Islam, A. R. M. T., Abba, S. I., Pandey, M., Ajin, R. S., & Pham, B. T. (2022). Flood susceptibility computation using state-of-the-art machine learning and optimization algorithms. +- Del Ser, J., Osaba, E., Martinez, A. D., Bilbao, M. N., Poyatos, J., Molina, D., & Herrera, F. (2021, December). More is not always better: insights from a massive comparison of meta-heuristic algorithms over real-parameter optimization problems. In 2021 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 1-7). IEEE. +- Rustam, F., Aslam, N., De La Torre Díez, I., Khan, Y. D., Mazón, J. L. V., Rodríguez, C. L., & Ashraf, I. (2022, November). White Blood Cell Classification Using Texture and RGB Features of Oversampled Microscopic Images. In Healthcare (Vol. 10, No. 11, p. 2230). MDPI. +- Neupane, D., Kafle, S., Gurung, S., Neupane, S., & Bhattarai, N. (2021). Optimal sizing and financial analysis of a stand-alone SPV-micro-hydropower hybrid system considering generation uncertainty. International Journal of Low-Carbon Technologies, 16(4), 1479-1491. +- Liang, R., Le-Hung, T., & Nguyen-Thoi, T. (2022). Energy consumption prediction of air-conditioning systems in eco-buildings using hunger games search optimization-based artificial neural network model. Journal of Building Engineering, 59, 105087. +- He, Z., Nguyen, H., Vu, T. H., Zhou, J., Asteris, P. G., & Mammou, A. (2022). Novel integrated approaches for predicting the compressibility of clay using cascade forward neural networks optimized by swarm-and evolution-based algorithms. Acta Geotechnica, 1-16. +- Xu, L., Yan, W., & Ji, J. (2022). The research of a novel WOG-YOLO algorithm forautonomous driving object detection. +- Nasir Ayub, M. I., Awais, M., Ali, U., Ali, T., Hamdi, M., Alghamdi, A., & Muhammad, F. Big Data Analytics for Short and Medium Term Electricity Load Forecasting using AI Techniques Ensembler. +- Xie, C., Nguyen, H., Choi, Y., & Armaghani, D. J. (2022). Optimized functional linked neural network for predicting diaphragm wall deflection induced by braced excavations in clays. Geoscience Frontiers, 13(2), 101313. +- Hakemi, S., Houshmand, M., & Hosseini, S. A. (2022). A Dynamic Quantum-Inspired Genetic Algorithm with Lengthening Chromosome Size. +- Kashifi, M. T. City-Wide Crash Risk Prediction and Interpretation Using Deep Learning Model with Multi-Source Big Data. Available at SSRN 4329686. +- Nguyen, H., & Hoang, N. D. (2022). Computer vision-based classification of concrete spall severity using metaheuristic-optimized Extreme Gradient Boosting Machine and Deep Convolutional Neural Network. Automation in Construction, 140, 104371. +- Zheng, J., Lu, Z., Wu, K., Ning, G. H., & Li, D. (2020). Coinage-metal-based cyclic trinuclear complexes with metal–metal interactions: Theories to experiments and structures to functions. Chemical Reviews, 120(17), 9675-9742. +- Van Thieu, N., Barma, S. D., Van Lam, T., Kisi, O., & Mahesha, A. (2023). Groundwater level modeling using Augmented Artificial Ecosystem Optimization. Journal of Hydrology, 617, 129034. +- Mo, Z., Zhang, Z., Miao, Q., & Tsui, K. L. (2022). Intelligent Informative Frequency Band Searching Assisted by a Dynamic Bandit Tree Method for Machine Fault Diagnosis. IEEE/ASME Transactions on Mechatronics. +- Dangi, D., Chandel, S. T., Dixit, D. K., Sharma, S., & Bhagat, A. (2023). An Efficient Model for Sentiment Analysis using Artificial Rabbits Optimized Vector Functional Link Network. Expert Systems with Applications, 119849. +- Dey, S., Roychoudhury, R., Malakar, S., & Sarkar, R. (2022). An optimized fuzzy ensemble of convolutional neural networks for detecting tuberculosis from Chest X-ray images. Applied Soft Computing, 114, 108094. +- Mousavirad, S. J., & Alexandre, L. A. (2022). Population-based JPEG Image Compression: Problem Re-Formulation. arXiv preprint arXiv:2212.06313. +- Tsui, K. L. Intelligent Informative Frequency Band Searching Assisted by A Dynamic Bandit Tree Method for Machine Fault Diagnosis. +- Neupane, D. (2020). Optimal Sizing and Performance Analysis of Solar PV-Micro hydropower Hybrid System in the Context of Rural Area of Nepal (Doctoral dissertation, Pulchowk Campus). +- LaTorre, A., Molina, D., Osaba, E., Poyatos, J., Del Ser, J., & Herrera, F. Swarm and Evolutionary Computation. +- Vieira, M. A. (2022). Otimização dos custos operacionais de uma comunidade energética considerando transações locais em “peer-to-peer” (Doctoral dissertation). +- Toğaçar, M. (2022). Using DarkNet models and metaheuristic optimization methods together to detect weeds growing along with seedlings. Ecological Informatics, 68, 101519. +- Toğaçar, M. (2021). Detection of segmented uterine cancer images by Hotspot Detection method using deep learning models, Pigeon-Inspired Optimization, types-based dominant activation selection approaches. Computers in Biology and Medicine, 136, 104659. +- Khan, N. A Short Term Electricity Load and Price Forecasting Model Based on BAT Algorithm in Logistic Regression and CNN-GRU with WOA. +- Yelisetti, S., Saini, V. K., Kumar, R., & Lamba, R. (2022, May). Energy Consumption Cost Benefits through Smart Home Energy Management in Residential Buildings: An Indian Case Study. In 2022 IEEE IAS Global Conference on Emerging Technologies (GlobConET) (pp. 930-935). IEEE. +- Nguyen, H., Cao, M. T., Tran, X. L., Tran, T. H., & Hoang, N. D. (2022). A novel whale optimization algorithm optimized XGBoost regression for estimating bearing capacity of concrete piles. Neural Computing and Applications, 1-28. +- Hirsching, C., de Jongh, S., Eser, D., Suriyah, M., & Leibfried, T. (2022). Meta-heuristic optimization of control structure and design for MMC-HVdc applications. Electric Power Systems Research, 213, 108371. +- Amelin, V., Gatiyatullin, E., Romanov, N., Samarkhanov, R., Vasilyev, R., & Yanovich, Y. (2022). Black-Box for Blockchain Parameters Adjustment. IEEE Access, 10, 101795-101802. +- Ngo, T. Q., Nguyen, L. Q., & Tran, V. Q. (2022). Novel hybrid machine learning models including support vector machine with meta-heuristic algorithms in predicting unconfined compressive strength of organic soils stabilised with cement and lime. International Journal of Pavement Engineering, 1-18. +- Zhu, Y., & Iiduka, H. (2021). Unified Algorithm Framework for Nonconvex Stochastic Optimization in Deep Neural Networks. IEEE Access, 9, 143807-143823. +- Hakemi, S., Houshmand, M., KheirKhah, E., & Hosseini, S. A. (2022). A review of recent advances in quantum-inspired metaheuristics. Evolutionary Intelligence, 1-16. +- Das, A., Das, S. R., Panda, J. P., Dey, A., Gajrani, K. K., Somani, N., & Gupta, N. (2022). Machine learning based modelling and optimization in hard turning of AISI D6 steel with newly developed AlTiSiN coated carbide tool. arXiv preprint arXiv:2202.00596. +- Yelisetti, S., Saini, V. K., Kumar, R., Lamba, R., & Saxena, A. (2022). Optimal energy management system for residential buildings considering the time of use price with swarm intelligence algorithms. Journal of Building Engineering, 59, 105062. +- Valdés, G. T. (2022). Algoritmo para la detección de vehículos y peatones combinando CNN´ sy técnicas de búsqueda. +- Sallam, N. M., Saleh, A. I., Ali, H. A., & Abdelsalam, M. M. (2023). An efficient EGWO algorithm as feature selection for B-ALL diagnoses and its subtypes classification using peripheral blood smear images. Alexandria Engineering Journal, 68, 39-66. +# Documents +* Meta-heuristic Categories: (Based on this article: [link](https://doi.org/10.1016/j.procs.2020.09.075)) + + Evolutionary-based: Idea from Darwin's law of natural selection, evolutionary computing + + Swarm-based: Idea from movement, interaction of birds, organization of social ... + + Physics-based: Idea from physics law such as Newton's law of universal gravitation, black hole, multiverse + + Human-based: Idea from human interaction such as queuing search, teaching learning, ... + + Biology-based: Idea from biology creature (or microorganism),... + + System-based: Idea from eco-system, immune-system, network-system, ... + + Math-based: Idea from mathematical form or mathematical law such as sin-cosin + + Music-based: Idea from music instrument +* Difficulty - Difficulty Level (Personal Opinion): **Objective observation from author**. Depend on the number of + parameters, number of equations, the original ideas, time spend for coding, source lines of code (SLOC). + + Easy: A few paras, few equations, SLOC very short + + Medium: more equations than Easy level, SLOC longer than Easy level + + Hard: Lots of equations, SLOC longer than Medium level, the paper hard to read. + + Hard* - Very hard: Lots of equations, SLOC too long, the paper is very hard to read. +** For newbie, we recommend to read the paper of algorithms which difficulty is "easy" or "medium" difficulty level. +| **Group** | **Name** | **Module** | **Class** | **Year** | **Paras** | **Difficulty** | +|--------------|-------------------------------------------------|------------|------------------|----------|-----------|----------------| +| Evolutionary | Evolutionary Programming | EP | OriginalEP | 1964 | 3 | easy | +| Evolutionary | - | - | LevyEP | - | 3 | easy | +| Evolutionary | Evolution Strategies | ES | OriginalES | 1971 | 3 | easy | +| Evolutionary | - | - | LevyES | - | 3 | easy | +| Evolutionary | Memetic Algorithm | MA | OriginalMA | 1989 | 7 | easy | +| Evolutionary | Genetic Algorithm | GA | BaseGA | 1992 | 4 | easy | +| Evolutionary | - | - | SingleGA | - | 7 | easy | +| Evolutionary | - | - | MultiGA | - | 7 | easy | +| Evolutionary | - | - | EliteSingleGA | - | 10 | easy | +| Evolutionary | - | - | EliteMultiGA | - | 10 | easy | +| Evolutionary | Differential Evolution | DE | BaseDE | 1997 | 5 | easy | +| Evolutionary | - | - | JADE | 2009 | 6 | medium | +| Evolutionary | - | - | SADE | 2005 | 2 | medium | +| Evolutionary | - | - | SHADE | 2013 | 4 | medium | +| Evolutionary | - | - | L_SHADE | 2014 | 4 | medium | +| Evolutionary | - | - | SAP_DE | 2006 | 3 | medium | +| Evolutionary | Flower Pollination Algorithm | FPA | OriginalFPA | 2014 | 4 | medium | +| Evolutionary | Coral Reefs Optimization | CRO | OriginalCRO | 2014 | 11 | medium | +| Evolutionary | - | - | OCRO | 2019 | 12 | medium | +| - | - | - | - | - | - | - | +| Swarm | Particle Swarm Optimization | PSO | OriginalPSO | 1995 | 6 | easy | +| Swarm | - | - | PPSO | 2019 | 2 | medium | +| Swarm | - | - | HPSO_TVAC | 2017 | 4 | medium | +| Swarm | - | - | C_PSO | 2015 | 6 | medium | +| Swarm | - | - | CL_PSO | 2006 | 6 | medium | +| Swarm | Bacterial Foraging Optimization | BFO | OriginalBFO | 2002 | 10 | hard | +| Swarm | - | - | ABFO | 2019 | 8 | medium | +| Swarm | Bees Algorithm | BeesA | OriginalBeesA | 2005 | 8 | medium | +| Swarm | - | - | ProbBeesA | 2015 | 5 | medium | +| Swarm | Cat Swarm Optimization | CSO | OriginalCSO | 2006 | 11 | hard | +| Swarm | Artificial Bee Colony | ABC | OriginalABC | 2007 | 8 | medium | +| Swarm | Ant Colony Optimization | ACO-R | OriginalACOR | 2008 | 5 | easy | +| Swarm | Cuckoo Search Algorithm | CSA | OriginalCSA | 2009 | 3 | medium | +| Swarm | Firefly Algorithm | FFA | OriginalFFA | 2009 | 8 | easy | +| Swarm | Fireworks Algorithm | FA | OriginalFA | 2010 | 7 | medium | +| Swarm | Bat Algorithm | BA | OriginalBA | 2010 | 6 | medium | +| Swarm | - | - | AdaptiveBA | - | 8 | medium | +| Swarm | - | - | ModifiedBA | - | 5 | medium | +| Swarm | Fruit-fly Optimization Algorithm | FOA | OriginalFOA | 2012 | 2 | easy | +| Swarm | - | - | BaseFOA | - | 2 | easy | +| Swarm | - | - | WhaleFOA | 2020 | 2 | medium | +| Swarm | Social Spider Optimization | SSpiderO | OriginalSSpiderO | 2018 | 4 | hard* | +| Swarm | Grey Wolf Optimizer | GWO | OriginalGWO | 2014 | 2 | easy | +| Swarm | - | - | RW_GWO | 2019 | 2 | easy | +| Swarm | Social Spider Algorithm | SSpiderA | OriginalSSpiderA | 2015 | 5 | medium | +| Swarm | Ant Lion Optimizer | ALO | OriginalALO | 2015 | 2 | easy | +| Swarm | - | - | BaseALO | - | 2 | easy | +| Swarm | Moth Flame Optimization | MFO | OriginalMFO | 2015 | 2 | easy | +| Swarm | - | - | BaseMFO | - | 2 | easy | +| Swarm | Elephant Herding Optimization | EHO | OriginalEHO | 2015 | 5 | easy | +| Swarm | Jaya Algorithm | JA | OriginalJA | 2016 | 2 | easy | +| Swarm | - | - | BaseJA | - | 2 | easy | +| Swarm | - | - | LevyJA | 2021 | 2 | easy | +| Swarm | Whale Optimization Algorithm | WOA | OriginalWOA | 2016 | 2 | medium | +| Swarm | - | - | HI_WOA | 2019 | 3 | medium | +| Swarm | Dragonfly Optimization | DO | OriginalDO | 2016 | 2 | medium | +| Swarm | Bird Swarm Algorithm | BSA | OriginalBSA | 2016 | 9 | medium | +| Swarm | Spotted Hyena Optimizer | SHO | OriginalSHO | 2017 | 4 | medium | +| Swarm | Salp Swarm Optimization | SSO | OriginalSSO | 2017 | 2 | easy | +| Swarm | Swarm Robotics Search And Rescue | SRSR | OriginalSRSR | 2017 | 2 | hard* | +| Swarm | Grasshopper Optimisation Algorithm | GOA | OriginalGOA | 2017 | 4 | easy | +| Swarm | Coyote Optimization Algorithm | COA | OriginalCOA | 2018 | 3 | medium | +| Swarm | Moth Search Algorithm | MSA | OriginalMSA | 2018 | 5 | easy | +| Swarm | Sea Lion Optimization | SLO | OriginalSLO | 2019 | 2 | medium | +| Swarm | - | - | ModifiedSLO | - | 2 | medium | +| Swarm | - | - | ImprovedSLO | - | 4 | medium | +| Swarm | Nake Mole-Rat Algorithm | NMRA | OriginalNMRA | 2019 | 3 | easy | +| Swarm | - | - | ImprovedNMRA | - | 4 | medium | +| Swarm | Pathfinder Algorithm | PFA | OriginalPFA | 2019 | 2 | medium | +| Swarm | Sailfish Optimizer | SFO | OriginalSFO | 2019 | 5 | easy | +| Swarm | - | - | ImprovedSFO | - | 3 | medium | +| Swarm | Harris Hawks Optimization | HHO | OriginalHHO | 2019 | 2 | medium | +| Swarm | Manta Ray Foraging Optimization | MRFO | OriginalMRFO | 2020 | 3 | medium | +| Swarm | Bald Eagle Search | BES | OriginalBES | 2020 | 7 | easy | +| Swarm | Sparrow Search Algorithm | SSA | OriginalSSA | 2020 | 5 | medium | +| Swarm | - | - | BaseSSA | - | 5 | medium | +| Swarm | Hunger Games Search | HGS | OriginalHGS | 2021 | 4 | medium | +| Swarm | Aquila Optimizer | AO | OriginalAO | 2021 | 2 | easy | +| Swarm | Hybrid Grey Wolf - Whale Optimization Algorithm | GWO | GWO_WOA | 2022 | 2 | easy | +| Swarm | Marine Predators Algorithm | MPA | OriginalMPA | 2020 | 2 | medium | +| Swarm | Honey Badger Algorithm | HBA | OriginalHBA | 2022 | 2 | easy | +| Swarm | Sand Cat Swarm Optimization | SCSO | OriginalSCSO | 2022 | 2 | easy | +| Swarm | Tuna Swarm Optimization | TSO | OriginalTSO | 2021 | 2 | medium | +| Swarm | African Vultures Optimization Algorithm | AVOA | OriginalAVOA | 2022 | 7 | medium | +| Swarm | Artificial Gorilla Troops Optimization | AGTO | OriginalAGTO | 2021 | 5 | medium | +| Swarm | Artificial Rabbits Optimization | ARO | OriginalARO | 2022 | 2 | easy | +| Swarm | Dwarf Mongoose Optimization Algorithm | DMOA | OriginalDMOA | 2022 | 4 | medium | +| Swarm | - | - | DevDMOA | - | 3 | medium | +| - | - | - | - | - | - | - | +| Physics | Simulated Annealling | SA | OriginalSA | 1987 | 9 | medium | +| Physics | Wind Driven Optimization | WDO | OriginalWDO | 2013 | 7 | easy | +| Physics | Multi-Verse Optimizer | MVO | OriginalMVO | 2016 | 4 | easy | +| Physics | - | - | BaseMVO | - | 4 | easy | +| Physics | Tug of War Optimization | TWO | OriginalTWO | 2016 | 2 | easy | +| Physics | - | - | OppoTWO | - | 2 | medium | +| Physics | - | - | LevyTWO | - | 2 | medium | +| Physics | - | - | EnhancedTWO | 2020 | 2 | medium | +| Physics | Electromagnetic Field Optimization | EFO | OriginalEFO | 2016 | 6 | easy | +| Physics | - | - | BaseEFO | - | 6 | medium | +| Physics | Nuclear Reaction Optimization | NRO | OriginalNRO | 2019 | 2 | hard* | +| Physics | Henry Gas Solubility Optimization | HGSO | OriginalHGSO | 2019 | 3 | medium | +| Physics | Atom Search Optimization | ASO | OriginalASO | 2019 | 4 | medium | +| Physics | Equilibrium Optimizer | EO | OriginalEO | 2019 | 2 | easy | +| Physics | - | - | ModifiedEO | 2020 | 2 | medium | +| Physics | - | - | AdaptiveEO | 2020 | 2 | medium | +| Physics | Archimedes Optimization Algorithm | ArchOA | OriginalArchOA | 2021 | 8 | medium | +| - | - | - | - | - | - | - | +| Human | Culture Algorithm | CA | OriginalCA | 1994 | 3 | easy | +| Human | Imperialist Competitive Algorithm | ICA | OriginalICA | 2007 | 8 | hard* | +| Human | Teaching Learning-based Optimization | TLO | OriginalTLO | 2011 | 2 | easy | +| Human | - | - | BaseTLO | 2012 | 2 | easy | +| Human | - | - | ITLO | 2013 | 3 | medium | +| Human | Brain Storm Optimization | BSO | OriginalBSO | 2011 | 8 | medium | +| Human | - | - | ImprovedBSO | 2017 | 7 | medium | +| Human | Queuing Search Algorithm | QSA | OriginalQSA | 2019 | 2 | hard | +| Human | - | - | BaseQSA | - | 2 | hard | +| Human | - | - | OppoQSA | - | 2 | hard | +| Human | - | - | LevyQSA | - | 2 | hard | +| Human | - | - | ImprovedQSA | 2021 | 2 | hard | +| Human | Search And Rescue Optimization | SARO | OriginalSARO | 2019 | 4 | medium | +| Human | - | - | BaseSARO | - | 4 | medium | +| Human | Life Choice-Based Optimization | LCO | OriginalLCO | 2019 | 3 | easy | +| Human | - | - | BaseLCO | - | 3 | easy | +| Human | - | - | ImprovedLCO | - | 2 | easy | +| Human | Social Ski-Driver Optimization | SSDO | OriginalSSDO | 2019 | 2 | easy | +| Human | Gaining Sharing Knowledge-based Algorithm | GSKA | OriginalGSKA | 2019 | 6 | medium | +| Human | - | - | BaseGSKA | - | 4 | medium | +| Human | Coronavirus Herd Immunity Optimization | CHIO | OriginalCHIO | 2020 | 4 | medium | +| Human | - | - | BaseCHIO | - | 4 | medium | +| Human | Forensic-Based Investigation Optimization | FBIO | OriginalFBIO | 2020 | 2 | medium | +| Human | - | - | BaseFBIO | - | 2 | medium | +| Human | Battle Royale Optimization | BRO | OriginalBRO | 2020 | 3 | medium | +| Human | - | - | BaseBRO | - | 3 | medium | +| Human | Student Psychology Based Optimization | SPBO | OriginalSPBO | 2020 | 2 | medium | +| Human | - | - | DevSPBO | | 2 | medium | +| - | - | - | - | - | - | - | +| Bio | Invasive Weed Optimization | IWO | OriginalIWO | 2006 | 7 | easy | +| Bio | Biogeography-Based Optimization | BBO | OriginalBBO | 2008 | 4 | easy | +| Bio | - | - | BaseBBO | - | 4 | easy | +| Bio | Virus Colony Search | VCS | OriginalVCS | 2016 | 4 | hard* | +| Bio | - | - | BaseVCS | - | 4 | hard* | +| Bio | Satin Bowerbird Optimizer | SBO | OriginalSBO | 2017 | 5 | easy | +| Bio | - | - | BaseSBO | - | 5 | easy | +| Bio | Earthworm Optimisation Algorithm | EOA | OriginalEOA | 2018 | 8 | medium | +| Bio | Wildebeest Herd Optimization | WHO | OriginalWHO | 2019 | 12 | hard | +| Bio | Slime Mould Algorithm | SMA | OriginalSMA | 2020 | 3 | easy | +| Bio | - | - | BaseSMA | - | 3 | easy | +| Bio | Barnacles Mating Optimizer | BMO | OriginalBMO | 2018 | 3 | easy | +| Bio | Tunicate Swarm Algorithm | TSA | OriginalTSA | 2020 | 2 | easy | +| Bio | Symbiotic Organisms Search | SOS | OriginalSOS | 2014 | 2 | medium | +| Bio | Seagull Optimization Algorithm | SOA | OriginalSOA | 2019 | 3 | easy | +| Bio | - | - | DevSOA | - | 3 | easy | +| - | - | - | - | - | - | - | +| System | Germinal Center Optimization | GCO | OriginalGCO | 2018 | 4 | medium | +| System | - | - | BaseGCO | - | 4 | medium | +| System | Water Cycle Algorithm | WCA | OriginalWCA | 2012 | 5 | medium | +| System | Artificial Ecosystem-based Optimization | AEO | OriginalAEO | 2019 | 2 | easy | +| System | - | - | EnhancedAEO | 2020 | 2 | medium | +| System | - | - | ModifiedAEO | 2020 | 2 | medium | +| System | - | - | ImprovedAEO | 2021 | 2 | medium | +| System | - | - | AugmentedAEO | 2022 | 2 | medium | +| - | - | - | - | - | - | - | +| Math | Hill Climbing | HC | OriginalHC | 1993 | 3 | easy | +| Math | - | - | SwarmHC | - | 3 | easy | +| Math | Cross-Entropy Method | CEM | OriginalCEM | 1997 | 4 | easy | +| Math | Sine Cosine Algorithm | SCA | OriginalSCA | 2016 | 2 | easy | +| Math | - | - | BaseSCA | - | 2 | easy | +| Math | Gradient-Based Optimizer | GBO | OriginalGBO | 2020 | 5 | medium | +| Math | Arithmetic Optimization Algorithm | AOA | OrginalAOA | 2021 | 6 | easy | +| Math | Chaos Game Optimization | CGO | OriginalCGO | 2021 | 2 | easy | +| Math | Pareto-like Sequential Sampling | PSS | OriginalPSS | 2021 | 4 | medium | +| Math | weIghted meaN oF vectOrs | INFO | OriginalINFO | 2022 | 2 | medium | +| Math | RUNge Kutta optimizer | RUN | OriginalRUN | 2021 | 2 | hard | +| Math | Circle Search Algorithm | CircleSA | OriginalCircleSA | 2022 | 3 | easy | +| - | - | - | - | - | - | - | +| Music | Harmony Search | HS | OriginalHS | 2001 | 4 | easy | +| Music | - | - | BaseHS | - | 4 | easy | +### A +* **ABC - Artificial Bee Colony** + * **OriginalABC**: Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization (Vol. 200, pp. 1-10). Technical report-tr06, Erciyes university, engineering faculty, computer engineering department. +* **ACOR - Ant Colony Optimization**. + * **OriginalACOR**: Socha, K., & Dorigo, M. (2008). Ant colony optimization for continuous domains. European journal of operational research, 185(3), 1155-1173. +* **ALO - Ant Lion Optimizer** + * **OriginalALO**: Mirjalili S (2015). “The Ant Lion Optimizer.” Advances in Engineering Software, 83, 80-98. doi: [10.1016/j.advengsoft.2015.01.010](https://doi.org/10.1016/j.advengsoft.2015.01.010) + * **BaseALO**: The developed version +* **AEO - Artificial Ecosystem-based Optimization** + * **OriginalAEO**: Zhao, W., Wang, L., & Zhang, Z. (2019). Artificial ecosystem-based optimization: a novel nature-inspired meta-heuristic algorithm. Neural Computing and Applications, 1-43. + * **AugmentedAEO**: Van Thieu, N., Barma, S. D., Van Lam, T., Kisi, O., & Mahesha, A. (2022). Groundwater level modeling using Augmented Artificial Ecosystem Optimization. Journal of Hydrology, 129034. + * **ImprovedAEO**: Rizk-Allah, R. M., & El-Fergany, A. A. (2020). Artificial ecosystem optimizer for parameters identification of proton exchange membrane fuel cells model. International Journal of Hydrogen Energy. + * **EnhancedAEO**: Eid, A., Kamel, S., Korashy, A., & Khurshaid, T. (2020). An Enhanced Artificial Ecosystem-Based Optimization for Optimal Allocation of Multiple Distributed Generations. IEEE Access, 8, 178493-178513. + * **ModifiedAEO**: Menesy, A. S., Sultan, H. M., Korashy, A., Banakhr, F. A., Ashmawy, M. G., & Kamel, S. (2020). Effective parameter extraction of different polymer electrolyte membrane fuel cell stack models using a modified artificial ecosystem optimization algorithm. IEEE Access, 8, 31892-31909. +* **ASO - Atom Search Optimization** + * **OriginalASO**: Zhao, W., Wang, L., & Zhang, Z. (2019). Atom search optimization and its application to solve a hydrogeologic parameter estimation problem. Knowledge-Based Systems, 163, 283-304. +* **ArchOA - Archimedes Optimization Algorithm** + * **OriginalArchOA**: Hashim, F. A., Hussain, K., Houssein, E. H., Mabrouk, M. S., & Al-Atabany, W. (2021). Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems. Applied Intelligence, 51(3), 1531-1551. +* **AOA - Arithmetic Optimization Algorithm** + * **OriginalAOA**: Abualigah, L., Diabat, A., Mirjalili, S., Abd Elaziz, M., & Gandomi, A. H. (2021). The arithmetic optimization algorithm. Computer methods in applied mechanics and engineering, 376, 113609. +* **AO - Aquila Optimizer** + * **OriginalAO**: Abualigah, L., Yousri, D., Abd Elaziz, M., Ewees, A. A., Al-qaness, M. A., & Gandomi, A. H. (2021). Aquila Optimizer: A novel meta-heuristic optimization Algorithm. Computers & Industrial Engineering, 157, 107250. +* **AVOA - African Vultures Optimization Algorithm** + * **OriginalAVOA**: Abdollahzadeh, B., Gharehchopogh, F. S., & Mirjalili, S. (2021). African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems. Computers & Industrial Engineering, 158, 107408. +* **AGTO - Artificial Gorilla Troops Optimization** + * **OriginalAGTO**: Abdollahzadeh, B., Soleimanian Gharehchopogh, F., & Mirjalili, S. (2021). Artificial gorilla troops optimizer: a new nature‐inspired metaheuristic algorithm for global optimization problems. International Journal of Intelligent Systems, 36(10), 5887-5958. +* **ARO - Artificial Rabbits Optimization**: + * **OriginalARO**: Wang, L., Cao, Q., Zhang, Z., Mirjalili, S., & Zhao, W. (2022). Artificial rabbits optimization: A new bio-inspired meta-heuristic algorithm for solving engineering optimization problems. Engineering Applications of Artificial Intelligence, 114, 105082. +### B +* **BFO - Bacterial Foraging Optimization** + * **OriginalBFO**: Passino, K. M. (2002). Biomimicry of bacterial foraging for distributed optimization and control. IEEE control systems magazine, 22(3), 52-67. + * **ABFO**: Nguyen, T., Nguyen, B. M., & Nguyen, G. (2019, April). Building resource auto-scaler with functional-link neural network and adaptive bacterial foraging optimization. In International Conference on Theory and Applications of Models of Computation (pp. 501-517). Springer, Cham. +* **BeesA - Bees Algorithm** + * **OriginalBeesA**: Pham, D. T., Ghanbarzadeh, A., Koc, E., Otri, S., Rahim, S., & Zaidi, M. (2005). The bees algorithm. Technical Note, Manufacturing Engineering Centre, Cardiff University, UK. + * **ProbBeesA**: The probabilitic version of: Pham, D. T., Ghanbarzadeh, A., Koç, E., Otri, S., Rahim, S., & Zaidi, M. (2006). The bees algorithm—a novel tool for complex optimisation problems. In Intelligent production machines and systems (pp. 454-459). Elsevier Science Ltd. +* **BBO - Biogeography-Based Optimization** + * **OriginalBBO**: Simon, D. (2008). Biogeography-based optimization. IEEE transactions on evolutionary computation, 12(6), 702-713. + * **BaseBBO**: The developed version +* **BA - Bat Algorithm** + * **OriginalBA**: Yang, X. S. (2010). A new metaheuristic bat-inspired algorithm. In Nature inspired cooperative strategies for optimization (NICSO 2010) (pp. 65-74). Springer, Berlin, Heidelberg. + * **AdaptiveBA**: Wang, X., Wang, W. and Wang, Y., 2013, July. An adaptive bat algorithm. In International Conference on Intelligent Computing(pp. 216-223). Springer, Berlin, Heidelberg. + * **ModifiedBA**: Dong, H., Li, T., Ding, R. and Sun, J., 2018. A novel hybrid genetic algorithm with granular information for feature selection and optimization. Applied Soft Computing, 65, pp.33-46. +* **BSO - Brain Storm Optimization** + * **OriginalBSO**: . Shi, Y. (2011, June). Brain storm optimization algorithm. In International conference in swarm intelligence (pp. 303-309). Springer, Berlin, Heidelberg. + * **ImprovedBSO**: El-Abd, M., 2017. Global-best brain storm optimization algorithm. Swarm and evolutionary computation, 37, pp.27-44. +* **BSA - Bird Swarm Algorithm** + * **OriginalBSA**: Meng, X. B., Gao, X. Z., Lu, L., Liu, Y., & Zhang, H. (2016). A new bio-inspired optimisation algorithm:Bird Swarm Algorithm. Journal of Experimental & Theoretical Artificial Intelligence, 28(4), 673-687. +* **BMO - Barnacles Mating Optimizer**: + * **OriginalBMO**: Sulaiman, M. H., Mustaffa, Z., Saari, M. M., Daniyal, H., Daud, M. R., Razali, S., & Mohamed, A. I. (2018, June). Barnacles mating optimizer: a bio-inspired algorithm for solving optimization problems. In 2018 19th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) (pp. 265-270). IEEE. +* **BES - Bald Eagle Search** + * **OriginalBES**: Alsattar, H. A., Zaidan, A. A., & Zaidan, B. B. (2019). Novel meta-heuristic bald eagle search optimisation algorithm. Artificial Intelligence Review, 1-28. +* **BRO - Battle Royale Optimization** + * **OriginalBRO**: Rahkar Farshi, T. (2020). Battle royale optimization algorithm. Neural Computing and Applications, 1-19. + * **BaseBRO**: The developed version +### C +* **CA - Culture Algorithm** + * **OriginalCA**: Reynolds, R.G., 1994, February. An introduction to cultural algorithms. In Proceedings of the third annual conference on evolutionary programming (Vol. 24, pp. 131-139). River Edge, NJ: World Scientific. +* **CEM - Cross Entropy Method** + * **OriginalCEM**: Rubinstein, R. (1999). The cross-entropy method for combinatorial and continuous optimization. Methodology and computing in applied probability, 1(2), 127-190. +* **CSO - Cat Swarm Optimization** + * **OriginalCSO**: Chu, S. C., Tsai, P. W., & Pan, J. S. (2006, August). Cat swarm optimization. In Pacific Rim international conference on artificial intelligence (pp. 854-858). Springer, Berlin, Heidelberg. +* **CSA - Cuckoo Search Algorithm** + * **OriginalCSA**: Yang, X. S., & Deb, S. (2009, December). Cuckoo search via Lévy flights. In 2009 World congress on nature & biologically inspired computing (NaBIC) (pp. 210-214). Ieee. +* **CRO - Coral Reefs Optimization** + * **OriginalCRO**: Salcedo-Sanz, S., Del Ser, J., Landa-Torres, I., Gil-López, S., & Portilla-Figueras, J. A. (2014). The coral reefs optimization algorithm: a novel metaheuristic for efficiently solving optimization problems. The Scientific World Journal, 2014. + * **OCRO**: Nguyen, T., Nguyen, T., Nguyen, B. M., & Nguyen, G. (2019). Efficient time-series forecasting using neural network and opposition-based coral reefs optimization. International Journal of Computational Intelligence Systems, 12(2), 1144-1161. +* **COA - Coyote Optimization Algorithm** + * **OriginalCOA**: Pierezan, J., & Coelho, L. D. S. (2018, July). Coyote optimization algorithm: a new metaheuristic for global optimization problems. In 2018 IEEE congress on evolutionary computation (CEC) (pp. 1-8). IEEE. +* **CHIO - Coronavirus Herd Immunity Optimization** + * **OriginalCHIO**: Al-Betar, M. A., Alyasseri, Z. A. A., Awadallah, M. A., & Abu Doush, I. (2021). Coronavirus herd immunity optimizer (CHIO). Neural Computing and Applications, 33(10), 5011-5042. + * **BaseCHIO**: The developed version +* **CGO - Chaos Game Optimization** + * **OriginalCGO**: Talatahari, S., & Azizi, M. (2021). Chaos Game Optimization: a novel metaheuristic algorithm. Artificial Intelligence Review, 54(2), 917-1004. +* **CSA - Circle Search Algorithm** + * **OriginalCSA**: Qais, M. H., Hasanien, H. M., Turky, R. A., Alghuwainem, S., Tostado-Véliz, M., & Jurado, F. (2022). Circle Search Algorithm: A Geometry-Based Metaheuristic Optimization Algorithm. Mathematics, 10(10), 1626. +### D +* **DE - Differential Evolution** + * **BaseDE**: Storn, R., & Price, K. (1997). Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization, 11(4), 341-359. + * **JADE**: Zhang, J., & Sanderson, A. C. (2009). JADE: adaptive differential evolution with optional external archive. IEEE Transactions on evolutionary computation, 13(5), 945-958. + * **SADE**: Qin, A. K., & Suganthan, P. N. (2005, September). Self-adaptive differential evolution algorithm for numerical optimization. In 2005 IEEE congress on evolutionary computation (Vol. 2, pp. 1785-1791). IEEE. + * **SHADE**: Tanabe, R., & Fukunaga, A. (2013, June). Success-history based parameter adaptation for differential evolution. In 2013 IEEE congress on evolutionary computation (pp. 71-78). IEEE. + * **L_SHADE**: Tanabe, R., & Fukunaga, A. S. (2014, July). Improving the search performance of SHADE using linear population size reduction. In 2014 IEEE congress on evolutionary computation (CEC) (pp. 1658-1665). IEEE. + * **SAP_DE**: Teo, J. (2006). Exploring dynamic self-adaptive populations in differential evolution. Soft Computing, 10(8), 673-686. +* **DSA - Differential Search Algorithm (not done)** + * **BaseDSA**: Civicioglu, P. (2012). Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm. Computers & Geosciences, 46, 229-247. +* **DO - Dragonfly Optimization** + * **OriginalDO**: Mirjalili, S. (2016). Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Computing and Applications, 27(4), 1053-1073. +* **DMOA - Dwarf Mongoose Optimization Algorithm** + * **OriginalDMOA**: Agushaka, J. O., Ezugwu, A. E., & Abualigah, L. (2022). Dwarf mongoose optimization algorithm. Computer methods in applied mechanics and engineering, 391, 114570. + * **DevDMOA**: The developed version +### E +* **ES - Evolution Strategies** . + * **OriginalES**: Schwefel, H. P. (1984). Evolution strategies: A family of non-linear optimization techniques based on imitating some principles of organic evolution. Annals of Operations Research, 1(2), 165-167. + * **LevyES**: Zhang, S., & Salari, E. (2005). Competitive learning vector quantization with evolution strategies for image compression. Optical Engineering, 44(2), 027006. +* **EP - Evolutionary programming** . + * **OriginalEP**: Fogel, L. J. (1994). Evolutionary programming in perspective: The top-down view. Computational intelligence: Imitating life. + * **LevyEP**: Lee, C.Y. and Yao, X., 2001, May. Evolutionary algorithms with adaptive lévy mutations. In Proceedings of the 2001 congress on evolutionary computation (IEEE Cat. No. 01TH8546) (Vol. 1, pp. 568-575). IEEE. +* **EHO - Elephant Herding Optimization** . + * **OriginalEHO**: Wang, G. G., Deb, S., & Coelho, L. D. S. (2015, December). Elephant herding optimization. In 2015 3rd International Symposium on Computational and Business Intelligence (ISCBI) (pp. 1-5). IEEE. +* **EFO - Electromagnetic Field Optimization** . + * **OriginalEFO**:Abedinpourshotorban, H., Shamsuddin, S. M., Beheshti, Z., & Jawawi, D. N. (2016). Electromagnetic field optimization: A physics-inspired metaheuristic optimization algorithm. Swarm and Evolutionary Computation, 26, 8-22. + * **BaseEFO**: The developed version +* **EOA - Earthworm Optimisation Algorithm** . + * **OriginalEOA**: Wang, G. G., Deb, S., & dos Santos Coelho, L. (2018). Earthworm optimisation algorithm: a bio-inspired metaheuristic algorithm for global optimisation problems. IJBIC, 12(1), 1-22. +* **EO - Equilibrium Optimizer** . + * **OriginalEO**: Faramarzi, A., Heidarinejad, M., Stephens, B., & Mirjalili, S. (2019). Equilibrium optimizer: A novel optimization algorithm. Knowledge-Based Systems. + * **ModifiedEO**: Gupta, S., Deep, K., & Mirjalili, S. (2020). An efficient equilibrium optimizer with mutation strategy for numerical optimization. Applied Soft Computing, 96, 106542. + * **AdaptiveEO**: Wunnava, A., Naik, M. K., Panda, R., Jena, B., & Abraham, A. (2020). A novel interdependence based multilevel thresholding technique using adaptive equilibrium optimizer. Engineering Applications of Artificial Intelligence, 94, 103836. +### F +* **FFA - Firefly Algorithm** + * **OriginalFFA**: Łukasik, S., & Żak, S. (2009, October). Firefly algorithm for continuous constrained optimization tasks. In International conference on computational collective intelligence (pp. 97-106). Springer, Berlin, Heidelberg. +* **FA - Fireworks algorithm** + * **OriginalFA**: Tan, Y., & Zhu, Y. (2010, June). Fireworks algorithm for optimization. In International conference in swarm intelligence (pp. 355-364). Springer, Berlin, Heidelberg. +* **FPA - Flower Pollination Algorithm** + * **OriginalFPA**: Yang, X. S. (2012, September). Flower pollination algorithm for global optimization. In International conference on unconventional computing and natural computation (pp. 240-249). Springer, Berlin, Heidelberg. +* **FOA - Fruit-fly Optimization Algorithm** + * **OriginalFOA**: Pan, W. T. (2012). A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowledge-Based Systems, 26, 69-74. + * **BaseFOA**: The developed version + * **WhaleFOA**: Fan, Y., Wang, P., Heidari, A. A., Wang, M., Zhao, X., Chen, H., & Li, C. (2020). Boosted hunting-based fruit fly optimization and advances in real-world problems. Expert Systems with Applications, 159, 113502. +* **FBIO - Forensic-Based Investigation Optimization** + * **OriginalFBIO**: Chou, J.S. and Nguyen, N.M., 2020. FBI inspired meta-optimization. Applied Soft Computing, p.106339. + * **BaseFBIO**: Fathy, A., Rezk, H. and Alanazi, T.M., 2021. Recent approach of forensic-based investigation algorithm for optimizing fractional order PID-based MPPT with proton exchange membrane fuel cell.IEEE Access,9, pp.18974-18992. +* **FHO - Fire Hawk Optimization** + * **OriginalFHO**: Azizi, M., Talatahari, S., & Gandomi, A. H. (2022). Fire Hawk Optimizer: a novel metaheuristic algorithm. Artificial Intelligence Review, 1-77. +### G +* **GA - Genetic Algorithm** + * **BaseGA**: Holland, J. H. (1992). Genetic algorithms. Scientific american, 267(1), 66-73. + * **SingleGA**: De Falco, I., Della Cioppa, A. and Tarantino, E., 2002. Mutation-based genetic algorithm: performance evaluation. Applied Soft Computing, 1(4), pp.285-299. + * **MultiGA**: De Jong, K.A. and Spears, W.M., 1992. A formal analysis of the role of multi-point crossover in genetic algorithms. Annals of mathematics and Artificial intelligence, 5(1), pp.1-26. + * **EliteSingleGA**: Elite version of Single-point mutation GA + * **EliteMultiGA**: Elite version of Multiple-point mutation GA +* **GWO - Grey Wolf Optimizer** + * **OriginalGWO**: Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in engineering software, 69, 46-61. + * **RW_GWO**: Gupta, S., & Deep, K. (2019). A novel random walk grey wolf optimizer. Swarm and evolutionary computation, 44, 101-112. + * **GWO_WOA**: Obadina, O. O., Thaha, M. A., Althoefer, K., & Shaheed, M. H. (2022). Dynamic characterization of a master–slave robotic manipulator using a hybrid grey wolf–whale optimization algorithm. Journal of Vibration and Control, 28(15-16), 1992-2003. +* **GOA - Grasshopper Optimisation Algorithm** + * **OriginalGOA**: Saremi, S., Mirjalili, S., & Lewis, A. (2017). Grasshopper optimisation algorithm: theory and application. Advances in Engineering Software, 105, 30-47. +* **GCO - Germinal Center Optimization** + * **OriginalGCO**: Villaseñor, C., Arana-Daniel, N., Alanis, A. Y., López-Franco, C., & Hernandez-Vargas, E. A. (2018). Germinal center optimization algorithm. International Journal of Computational Intelligence Systems, 12(1), 13-27. + * **BaseGCO**: The developed version +* **GSKA - Gaining Sharing Knowledge-based Algorithm** + * **OriginalGSKA**: Mohamed, A. W., Hadi, A. A., & Mohamed, A. K. (2019). Gaining-sharing knowledge based algorithm for solving optimization problems: a novel nature-inspired algorithm. International Journal of Machine Learning and Cybernetics, 1-29. + * **BaseGSKA**: Mohamed, A.W., Hadi, A.A., Mohamed, A.K. and Awad, N.H., 2020, July. Evaluating the performance of adaptive GainingSharing knowledge based algorithm on CEC 2020 benchmark problems. In 2020 IEEE Congress on Evolutionary Computation (CEC) (pp. 1-8). IEEE. +* **GBO - Gradient-Based Optimizer** + * **OriginalGBO**: Ahmadianfar, I., Bozorg-Haddad, O., & Chu, X. (2020). Gradient-based optimizer: A new metaheuristic optimization algorithm. Information Sciences, 540, 131-159. +### H +* **HC - Hill Climbing** . + * **OriginalHC**: Talbi, E. G., & Muntean, T. (1993, January). Hill-climbing, simulated annealing and genetic algorithms: a comparative study and application to the mapping problem. In [1993] Proceedings of the Twenty-sixth Hawaii International Conference on System Sciences (Vol. 2, pp. 565-573). IEEE. + * **SwarmHC**: The developed version based on swarm-based idea (Original is single-solution based method) +* **HS - Harmony Search** . + * **OriginalHS**: Geem, Z. W., Kim, J. H., & Loganathan, G. V. (2001). A new heuristic optimization algorithm:harmony search. simulation, 76(2), 60-68. + * **BaseHS**: The developed version +* **HHO - Harris Hawks Optimization** . + * **OriginalHHO**: Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems, 97, 849-872. +* **HGSO - Henry Gas Solubility Optimization** . + * **OriginalHGSO**: Hashim, F. A., Houssein, E. H., Mabrouk, M. S., Al-Atabany, W., & Mirjalili, S. (2019). Henry gas solubility optimization: A novel physics-based algorithm. Future Generation Computer Systems, 101, 646-667. +* **HGS - Hunger Games Search** . + * **OriginalHGS**: Yang, Y., Chen, H., Heidari, A. A., & Gandomi, A. H. (2021). Hunger games search:Visions, conception, implementation, deep analysis, perspectives, and towards performance shifts. Expert Systems with Applications, 177, 114864. +* **HHOA - Horse Herd Optimization Algorithm (not done)** . + * **BaseHHOA**: MiarNaeimi, F., Azizyan, G., & Rashki, M. (2021). Horse herd optimization algorithm: A nature-inspired algorithm for high-dimensional optimization problems. Knowledge-Based Systems, 213, 106711. +* **HBA - Honey Badger Algorithm**: + * **OriginalHBA**: Hashim, F. A., Houssein, E. H., Hussain, K., Mabrouk, M. S., & Al-Atabany, W. (2022). Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems. Mathematics and Computers in Simulation, 192, 84-110. +### I +* **IWO - Invasive Weed Optimization** . + * **OriginalIWO**: Mehrabian, A. R., & Lucas, C. (2006). A novel numerical optimization algorithm inspired from weed colonization. Ecological informatics, 1(4), 355-366. +* **ICA - Imperialist Competitive Algorithm** + * **OriginalICA**: Atashpaz-Gargari, E., & Lucas, C. (2007, September). Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In 2007 IEEE congress on evolutionary computation (pp. 4661-4667). Ieee. +* **INFO - weIghted meaN oF vectOrs**: + * **OriginalINFO**: Ahmadianfar, I., Heidari, A. A., Gandomi, A. H., Chu, X., & Chen, H. (2021). RUN beyond the metaphor: An efficient optimization algorithm based on Runge Kutta method. Expert Systems with Applications, 181, 115079. +### J +* **JA - Jaya Algorithm** + * **OriginalJA**: Rao, R. (2016). Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. International Journal of Industrial Engineering Computations, 7(1), 19-34. + * **BaseJA**: The developed version + * **LevyJA**: Iacca, G., dos Santos Junior, V. C., & de Melo, V. V. (2021). An improved Jaya optimization algorithm with Levy flight. Expert Systems with Applications, 165, 113902. +### K +### L +* **LCO - Life Choice-based Optimization** + * **OriginalLCO**: Khatri, A., Gaba, A., Rana, K. P. S., & Kumar, V. (2019). A novel life choice-based optimizer. Soft Computing, 1-21. + * **BaseLCO**: The developed version + * **ImprovedLCO**: The improved version using Gaussian distribution and Mutation Mechanism +### M +* **MA - Memetic Algorithm** + * **OriginalMA**: Moscato, P. (1989). On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms. Caltech concurrent computation program, C3P Report, 826, 1989. +* **MFO - Moth Flame Optimization** + * **OriginalMFO**: Mirjalili, S. (2015). Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-based systems, 89, 228-249. + * **BaseMFO**: The developed version +* **MVO - Multi-Verse Optimizer** + * **OriginalMVO**: Mirjalili, S., Mirjalili, S. M., & Hatamlou, A. (2016). Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Computing and Applications, 27(2), 495-513. + * **BaseMVO**: The developed version +* **MSA - Moth Search Algorithm** + * **OriginalMSA**: Wang, G. G. (2018). Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems. Memetic Computing, 10(2), 151-164. +* **MRFO - Manta Ray Foraging Optimization** + * **OriginalMRFO**: Zhao, W., Zhang, Z., & Wang, L. (2020). Manta ray foraging optimization: An effective bio-inspired optimizer for engineering applications. Engineering Applications of Artificial Intelligence, 87, 103300. +* **MPA - Marine Predators Algorithm**: + * **OriginalMPA**: Faramarzi, A., Heidarinejad, M., Mirjalili, S., & Gandomi, A. H. (2020). Marine Predators Algorithm: A nature-inspired metaheuristic. Expert systems with applications, 152, 113377. +### N +* **NRO - Nuclear Reaction Optimization** + * **OriginalNRO**: Wei, Z., Huang, C., Wang, X., Han, T., & Li, Y. (2019). Nuclear Reaction Optimization: A novel and powerful physics-based algorithm for global optimization. IEEE Access. +* **NMRA - Nake Mole-Rat Algorithm** + * **OriginalNMRA**: Salgotra, R., & Singh, U. (2019). The naked mole-rat algorithm. Neural Computing and Applications, 31(12), 8837-8857. + * **ImprovedNMRA**: Singh, P., Mittal, N., Singh, U. and Salgotra, R., 2021. Naked mole-rat algorithm with improved exploration and exploitation capabilities to determine 2D and 3D coordinates of sensor nodes in WSNs. Arabian Journal for Science and Engineering, 46(2), pp.1155-1178. +### O +### P +* **PSO - Particle Swarm Optimization** + * **OriginalPSO**: Eberhart, R., & Kennedy, J. (1995, October). A new optimizer using particle swarm theory. In MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science (pp. 39-43). Ieee. + * **PPSO**: Ghasemi, M., Akbari, E., Rahimnejad, A., Razavi, S. E., Ghavidel, S., & Li, L. (2019). Phasor particle swarm optimization: a simple and efficient variant of PSO. Soft Computing, 23(19), 9701-9718. + * **HPSO_TVAC**: Ghasemi, M., Aghaei, J., & Hadipour, M. (2017). New self-organising hierarchical PSO with jumping time-varying acceleration coefficients. Electronics Letters, 53(20), 1360-1362. + * **C_PSO**: Liu, B., Wang, L., Jin, Y. H., Tang, F., & Huang, D. X. (2005). Improved particle swarm optimization combined with chaos. Chaos, Solitons & Fractals, 25(5), 1261-1271. + * **CL_PSO**: Liang, J. J., Qin, A. K., Suganthan, P. N., & Baskar, S. (2006). Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE transactions on evolutionary computation, 10(3), 281-295. +* **PFA - Pathfinder Algorithm** + * **OriginalPFA**: Yapici, H., & Cetinkaya, N. (2019). A new meta-heuristic optimizer: Pathfinder algorithm. Applied Soft Computing, 78, 545-568. +* **PSS - Pareto-like Sequential Sampling** + * **OriginalPSS**: Shaqfa, M., & Beyer, K. (2021). Pareto-like sequential sampling heuristic for global optimisation. Soft Computing, 25(14), 9077-9096. +### Q +* **QSA - Queuing Search Algorithm** + * **OriginalQSA**: Zhang, J., Xiao, M., Gao, L., & Pan, Q. (2018). Queuing search algorithm: A novel metaheuristic algorithm for solving engineering optimization problems. Applied Mathematical Modelling, 63, 464-490. + * **BaseQSA**: The developed version + * **OppoQSA**: Zheng, X. and Nguyen, H., 2022. A novel artificial intelligent model for predicting water treatment efficiency of various biochar systems based on artificial neural network and queuing search algorithm. Chemosphere, 287, p.132251. + * **LevyQSA**: Abderazek, H., Hamza, F., Yildiz, A.R., Gao, L. and Sait, S.M., 2021. A comparative analysis of the queuing search algorithm, the sine-cosine algorithm, the ant lion algorithm to determine the optimal weight design problem of a spur gear drive system. Materials Testing, 63(5), pp.442-447. + * **ImprovedQSA**: Nguyen, B.M., Hoang, B., Nguyen, T. and Nguyen, G., 2021. nQSV-Net: a novel queuing search variant for global space search and workload modeling. Journal of Ambient Intelligence and Humanized Computing, 12(1), pp.27-46. +### R +* **RUN - RUNge Kutta optimizer**: + * **OriginalRUN**: Ahmadianfar, I., Heidari, A. A., Gandomi, A. H., Chu, X., & Chen, H. (2021). RUN beyond the metaphor: An efficient optimization algorithm based on Runge Kutta method. Expert Systems with Applications, 181, 115079. +### S +* **SA - Simulated Annealling** + * **OriginalSA**: . Van Laarhoven, P. J., & Aarts, E. H. (1987). Simulated annealing. In Simulated annealing: Theory and applications (pp. 7-15). Springer, Dordrecht. +* **SSpiderO - Social Spider Optimization** + * **OriginalSSpiderO**: Cuevas, E., Cienfuegos, M., ZaldíVar, D., & Pérez-Cisneros, M. (2013). A swarm optimization algorithm inspired in the behavior of the social-spider. Expert Systems with Applications, 40(16), 6374-6384. +* **SOS - Symbiotic Organisms Search**: + * **OriginalSOS**: Cheng, M. Y., & Prayogo, D. (2014). Symbiotic organisms search: a new metaheuristic optimization algorithm. Computers & Structures, 139, 98-112. +* **SSpiderA - Social Spider Algorithm** + * **OriginalSSpiderA**: James, J. Q., & Li, V. O. (2015). A social spider algorithm for global optimization. Applied Soft Computing, 30, 614-627. +* **SCA - Sine Cosine Algorithm** + * **OriginalSCA**: Mirjalili, S. (2016). SCA: a sine cosine algorithm for solving optimization problems. Knowledge-Based Systems, 96, 120-133. + * **BaseSCA**: Attia, A.F., El Sehiemy, R.A. and Hasanien, H.M., 2018. Optimal power flow solution in power systems using a novel Sine-Cosine algorithm. International Journal of Electrical Power & Energy Systems, 99, pp.331-343. +* **SRSR - Swarm Robotics Search And Rescue** + * **OriginalSRSR**: Bakhshipour, M., Ghadi, M. J., & Namdari, F. (2017). Swarm robotics search & rescue: A novel artificial intelligence-inspired optimization approach. Applied Soft Computing, 57, 708-726. +* **SBO - Satin Bowerbird Optimizer** + * **OriginalSBO**: Moosavi, S. H. S., & Bardsiri, V. K. (2017). Satin bowerbird optimizer: a new optimization algorithm to optimize ANFIS for software development effort estimation. Engineering Applications of Artificial Intelligence, 60, 1-15. + * **BaseSBO**: The developed version +* **SHO - Spotted Hyena Optimizer** + * **OriginalSHO**: Dhiman, G., & Kumar, V. (2017). Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications. Advances in Engineering Software, 114, 48-70. +* **SSO - Salp Swarm Optimization** + * **OriginalSSO**: Mirjalili, S., Gandomi, A. H., Mirjalili, S. Z., Saremi, S., Faris, H., & Mirjalili, S. M. (2017). Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Software, 114, 163-191. +* **SFO - Sailfish Optimizer** + * **OriginalSFO**: Shadravan, S., Naji, H. R., & Bardsiri, V. K. (2019). The Sailfish Optimizer: A novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems. Engineering Applications of Artificial Intelligence, 80, 20-34. + * **ImprovedSFO**: Li, L.L., Shen, Q., Tseng, M.L. and Luo, S., 2021. Power system hybrid dynamic economic emission dispatch with wind energy based on improved sailfish algorithm. Journal of Cleaner Production, 316, p.128318. +* **SARO - Search And Rescue Optimization** + * **OriginalSARO**: Shabani, A., Asgarian, B., Gharebaghi, S. A., Salido, M. A., & Giret, A. (2019). A New Optimization Algorithm Based on Search and Rescue Operations. Mathematical Problems in Engineering, 2019. + * **BaseSARO**: The developed version using Levy-flight +* **SSDO - Social Ski-Driver Optimization** + * **OriginalSSDO**: Tharwat, A., & Gabel, T. (2019). Parameters optimization of support vector machines for imbalanced data using social ski driver algorithm. Neural Computing and Applications, 1-14. +* **SLO - Sea Lion Optimization** + * **OriginalSLO**: Masadeh, R., Mahafzah, B. A., & Sharieh, A. (2019). Sea Lion Optimization Algorithm. Sea, 10(5). + * **ImprovedSLO**: The developed version + * **ModifiedSLO**: Masadeh, R., Alsharman, N., Sharieh, A., Mahafzah, B.A. and Abdulrahman, A., 2021. Task scheduling on cloud computing based on sea lion optimization algorithm. International Journal of Web Information Systems. +* **Seagull Optimization Algorithm** + * **OriginalSOA**: Dhiman, G., & Kumar, V. (2019). Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems. Knowledge-based systems, 165, 169-196. + * **DevSOA**: The developed version +* **SMA - Slime Mould Algorithm** + * **OriginalSMA**: Li, S., Chen, H., Wang, M., Heidari, A. A., & Mirjalili, S. (2020). Slime mould algorithm: A new method for stochastic optimization. Future Generation Computer Systems. + * **BaseSMA**: The developed version +* **SSA - Sparrow Search Algorithm** + * **OriginalSSA**: Jiankai Xue & Bo Shen (2020) A novel swarm intelligence optimization approach: sparrow search algorithm, Systems Science & Control Engineering, 8:1, 22-34, DOI: 10.1080/21642583.2019.1708830 + * **BaseSSA**: The developed version +* **SPBO - Student Psychology Based Optimization** + * **OriginalSPBO**: Das, B., Mukherjee, V., & Das, D. (2020). Student psychology based optimization algorithm: A new population based optimization algorithm for solving optimization problems. Advances in Engineering software, 146, 102804. + * **DevSPBO**: The developed version +* **SCSO - Sand Cat Swarm Optimization** + * **OriginalSCSO**: Seyyedabbasi, A., & Kiani, F. (2022). Sand Cat swarm optimization: a nature-inspired algorithm to solve global optimization problems. Engineering with Computers, 1-25. +### T +* **TLO - Teaching Learning Optimization** + * **OriginalTLO**: Rao, R. V., Savsani, V. J., & Vakharia, D. P. (2011). Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-Aided Design, 43(3), 303-315. + * **BaseTLO**: Rao, R., & Patel, V. (2012). An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems. International Journal of Industrial Engineering Computations, 3(4), 535-560. + * **ImprovedTLO**: Rao, R. V., & Patel, V. (2013). An improved teaching-learning-based optimization algorithm for solving unconstrained optimization problems. Scientia Iranica, 20(3), 710-720. +* **TWO - Tug of War Optimization** + * **OriginalTWO**: Kaveh, A., & Zolghadr, A. (2016). A novel meta-heuristic algorithm: tug of war optimization. Iran University of Science & Technology, 6(4), 469-492. + * **OppoTWO**: Kaveh, A., Almasi, P. and Khodagholi, A., 2022. Optimum Design of Castellated Beams Using Four Recently Developed Meta-heuristic Algorithms. Iranian Journal of Science and Technology, Transactions of Civil Engineering, pp.1-13. + * **LevyTWO**: The developed version using Levy-flight + * **ImprovedTWO**: Nguyen, T., Hoang, B., Nguyen, G., & Nguyen, B. M. (2020). A new workload prediction model using extreme learning machine and enhanced tug of war optimization. Procedia Computer Science, 170, 362-369. +* **TSA - Tunicate Swarm Algorithm** + * **OriginalTSA**: Kaur, S., Awasthi, L. K., Sangal, A. L., & Dhiman, G. (2020). Tunicate Swarm Algorithm: A new bio-inspired based metaheuristic paradigm for global optimization. Engineering Applications of Artificial Intelligence, 90, 103541. +* **TSO - Tuna Swarm Optimization** + * **OriginalTSO**: Xie, L., Han, T., Zhou, H., Zhang, Z. R., Han, B., & Tang, A. (2021). Tuna swarm optimization: a novel swarm-based metaheuristic algorithm for global optimization. Computational intelligence and Neuroscience, 2021. +### U +### V +* **VCS - Virus Colony Search** + * **OriginalVCS**: Li, M. D., Zhao, H., Weng, X. W., & Han, T. (2016). A novel nature-inspired algorithm for optimization: Virus colony search. Advances in Engineering Software, 92, 65-88. + * **BaseVCS**: The developed version +### W +* **WCA - Water Cycle Algorithm** + * **OriginalWCA**: Eskandar, H., Sadollah, A., Bahreininejad, A., & Hamdi, M. (2012). Water cycle algorithm–A novel metaheuristic optimization method for solving constrained engineering optimization problems. Computers & Structures, 110, 151-166. +* **WOA - Whale Optimization Algorithm** + * **OriginalWOA**: Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in engineering software, 95, 51-67. + * **HI_WOA**: Tang, C., Sun, W., Wu, W., & Xue, M. (2019, July). A hybrid improved whale optimization algorithm. In 2019 IEEE 15th International Conference on Control and Automation (ICCA) (pp. 362-367). IEEE. +* **WHO - Wildebeest Herd Optimization** + * **OriginalWHO**: Amali, D., & Dinakaran, M. (2019). Wildebeest herd optimization: A new global optimization algorithm inspired by wildebeest herding behaviour. Journal of Intelligent & Fuzzy Systems, (Preprint), 1-14. +* **WDO - Wind Driven Optimization** + * **OriginalWDO**: Bayraktar, Z., Komurcu, M., Bossard, J.A. and Werner, D.H., 2013. The wind driven optimization technique and its application in electromagnetics. IEEE transactions on antennas and propagation, 61(5), pp.2745-2757. +### X +### Y +### Z + +%package help +Summary: Development documents and examples for mealpy +Provides: python3-mealpy-doc +%description help +[](https://github.com/thieu1995/mealpy/releases) +[](https://pypi.python.org/pypi/mealpy) +[](https://badge.fury.io/py/mealpy) + + + +[](https://pepy.tech/project/mealpy) +[](https://github.com/thieu1995/mealpy/actions/workflows/publish-package.yaml) + +[](https://mealpy.readthedocs.io/en/latest/?badge=latest) +[](https://t.me/+fRVCJGuGJg1mNDg1) +[](http://isitmaintained.com/project/thieu1995/mealpy "Average time to resolve an issue") +[](http://isitmaintained.com/project/thieu1995/mealpy "Percentage of issues still open") + +[](https://git-scm.com/book/en/v2/GitHub-Contributing-to-a-Project) +[](https://doi.org/10.5281/zenodo.3711948) +[](https://www.gnu.org/licenses/gpl-3.0) +MEALPY is the largest python library for most of the cutting-edge nature-inspired meta-heuristic algorithms (population-based). Population meta-heuristic algorithms (PMA) are the most popular algorithms in the field of +approximate optimization. +* **Free software:** GNU General Public License (GPL) V3 license +* **Total algorithms**: 174 (102 original, 45 official variants, 27 developed variants) +* **Documentation:** https://mealpy.readthedocs.io/en/latest/ +* **Python versions:** 3.7.x, 3.8.x, 3.9.x, 3.10.x, 3.11.x +* **Dependencies:** numpy, scipy, pandas, matplotlib +# Goals +Our goals are to implement all of the classical as well as the state-of-the-art nature-inspired algorithms, create a simple interface that helps researchers access optimization algorithms as quickly as possible, and share knowledge of the optimization field with everyone without a fee. What you can do with mealpy: +- Analyse parameters of meta-heuristic algorithms. +- Perform Qualitative and Quantitative Analysis of algorithms. +- Analyse rate of convergence of algorithms. +- Test and Analyse the scalability and the robustness of algorithms. +- Save results in various formats (csv, json, pickle, png, pdf, jpeg) +- Export and import models can also be done with Mealpy. +# Installation +### Install with pip +Install the [current PyPI release](https://pypi.python.org/pypi/mealpy): +```sh +$ pip install mealpy==2.5.3 +``` +### Install from source +In case you want to install directly from the source code, use: +```sh +$ git clone https://github.com/thieu1995/mealpy.git +$ cd mealpy +$ python setup.py install +``` +# Usage +After installation, you can import Mealpy as any other Python module: +```sh +$ python +>>> import mealpy +>>> mealpy.__version__ +``` +Let's go through a basic and advanced example. +## Examples +### Simple Benchmark Function +```python +from mealpy.bio_based import SMA +import numpy as np +def fitness_function(solution): + return np.sum(solution**2) +problem = { + "fit_func": fitness_function, + "lb": [-100, ] * 30, + "ub": [100, ] * 30, + "minmax": "min", + "log_to": None, + "save_population": False, +} +## Run the algorithm +model = SMA.BaseSMA(epoch=100, pop_size=50, pr=0.03) +best_position, best_fitness = model.solve(problem) +print(f"Best solution: {best_position}, Best fitness: {best_fitness}") +``` +### Constrained Benchmark Function +* [The Constrained Benchmark Function](https://github.com/thieu1995/mealpy/tree/master/examples/applications/run_constraint_functions.py) +### Multi-objective Benchmark Function +* [Multi-objective benchmark functions](https://github.com/thieu1995/mealpy/tree/master/examples/applications/run_multi_objective_functions.py) +### Custom Problem +For our custom problem, we can create a class and inherit from the Problem class, named the child class the +'Squared' class. In the initialization method of the 'Squared' class, we have to set the *lb*, *ub*, and *minmax* +of the problem (lb: a list of lower bound values, ub: a list of upper bound values, and minmax: a string specifying +whether the problem is a 'min' or 'max' problem). +Afterwards, we have to override the abstract method 'fit_func()', which takes a parameter 'solution' (the solution +to be evaluated) and returns the function value. The resulting code should look something like the code snippet +below. 'Name' is an additional parameter we want to include in this class, and you can include any other additional +parameters you need. +```python +import numpy as np +from mealpy.bio_based import BBO +from mealpy.utils.problem import Problem +# Our custom problem class +class Squared(Problem): + def __init__(self, lb=(-5, -5, -5, -5, -5, -5), ub=(5, 5, 5, 5, 5, 5), minmax="min", name="Squared", **kwargs): + super().__init__(lb, ub, minmax, **kwargs) + self.name = name + def fit_func(self, solution): + return np.sum(solution ** 2) +``` +Now, we define an algorithm, and pass an instance of our *Squared* class as the problem argument. +```python +problem = Squared(lb=[-10] * 20, ub=[10] * 20, minmax="min") +model = BBO.BaseBBO(epoch=10, pop_size=50) +best_position, best_fitness = model.solve(problem) +print(best_position) +print(best_fitness) +print(model.get_parameters()) +print(model.get_name()) +print(model.get_attributes()["solution"]) +print(model.problem.get_name()) +print(model.problem.n_dims) +``` +### Tuner class (GridSearchCV/ParameterSearch, Hyper-parameter tuning) +We build a dedicated class, Tuner, that can help you tune your algorithm's parameters. +```python +import numpy as np +from mealpy.bio_based import BBO +from mealpy.tuner import Tuner # Remember this +def fitness(solution): + return np.sum(solution**2) +problem = { + "lb": [-100, ]*50, + "ub": [100, ]*50, + "minmax": "min", + "fit_func": fitness, + "name": "Squared Problem", + "log_to": None, +} +paras_bbo_grid = { + "epoch": [100], + "pop_size": [50], + "elites": [2, 3, 4, 5], + "p_m": [0.01, 0.02, 0.05, 0.1, 0.15, 0.2] +} +term = { + "max_fe": 10000 +} +if __name__ == "__main__": + model = BBO.BaseBBO() + tuner = Tuner(model, paras_bbo_grid) + tuner.execute(problem=problem, termination=term, n_trials=5, n_jobs=5, mode="thread", n_workers=4, verbose=True) + ## Solve this problem 5 times (n_trials) using 5 processes (n_jobs), each process will handle 1 trial. + ## The mode to run the solver is thread (mode), we will calculate the fitness of 4 solutions (n_workers) at the same time + print(tuner.best_score) + print(tuner.best_params) + print(tuner.best_algorithm) + print(tuner.best_algorithm.get_name()) + ## Save results to csv file + tuner.export_results(save_path="history/tuning", save_as="csv") + ## Re-solve the best model on your problem + best_position, best_fitness = tuner.resolve() + print(best_position, best_fitness) + print(tuner.problem.get_name()) +``` +### Multitask class (Multitask solving) +We also build a dedicated class, Multitask, that can help you run several scenarios. For example: +1. Run 1 algorithm with 1 problem, and multiple trials +2. Run 1 algorithm with multiple problems, and multiple trials +3. Run multiple algorithms with 1 problem, and multiple trials +4. Run multiple algorithms with multiple problems, and multiple trials +```python +#### Using multiple algorithm to solve multiple problems with multiple trials +## Import libraries +## For example, we want to solve F5, F10, F29 problem in CEC-2017 +from opfunu.cec_based.cec2017 import F52017, F102017, F292017 +from mealpy.bio_based import BBO +from mealpy.evolutionary_based import DE +from mealpy.multitask import Multitask # Remember this +## You can define your own problems +f1 = F52017(30, f_bias=0) +f2 = F102017(30, f_bias=0) +f3 = F292017(30, f_bias=0) +p1 = { + "lb": f1.lb.tolist(), + "ub": f1.ub.tolist(), + "minmax": "min", + "fit_func": f1.evaluate, + "name": "F5-CEC2017", + "log_to": None, +} +p2 = { + "lb": f2.lb.tolist(), + "ub": f2.ub.tolist(), + "minmax": "min", + "fit_func": f2.evaluate, + "name": "F10-CEC2017", + "log_to": None, +} +p3 = { + "lb": f3.lb.tolist(), + "ub": f3.ub.tolist(), + "minmax": "min", + "fit_func": f3.evaluate, + "name": "F29-CEC2017", + "log_to": None, +} +## Define models +model1 = BBO.BaseBBO(epoch=10, pop_size=50) +model2 = BBO.OriginalBBO(epoch=10, pop_size=50) +model3 = DE.BaseDE(epoch=10, pop_size=50) +## Define termination if needed +term = { + "max_fe": 10000 +} +## Define and run Multitask +if __name__ == "__main__": + multitask = Multitask(algorithms=(model1, model2, model3), problems=(p1, p2, p3), terminations=(term, ), modes=("thread", )) + # default modes = "single", default termination = epoch (as defined in problem dictionary) + multitask.execute(n_trials=5, n_jobs=5, save_path="history", save_as="csv", save_convergence=False, verbose=False) + ## Check the directory: history/, you will see list of .csv result files +``` +For more usage examples please look at [examples](/examples) folder. +More advanced examples can also be found in the [Mealpy-examples repository](https://github.com/thieu1995/mealpy_examples). +### Get Visualize Figures +* [Tutorials](/examples/utils/visualize/all_charts.py) +<p align="center"><img src="https://thieu1995.github.io/post/2022-04/19-mealpy-tutorials/mealpy2.png" alt="MEALPY"/> +</p> +## Mealpy Application +### Mealpy + Neural Network (Replace the Gradient Descent Optimizer) +* Time-series Problem: + * Traditional MLP + code: [Link](https://github.com/thieu1995/mealpy/tree/master/examples/applications/keras/traditional-mlp-time-series.py) + * Hybrid code (Mealpy + + MLP): [Link](https://github.com/thieu1995/mealpy/tree/master/examples/applications/keras/mha-hybrid-mlp-time-series.py) +* Classification Problem: + * Traditional MLP + code: [Link](https://github.com/thieu1995/mealpy/blob/master/examples/applications/keras/traditional-mlp-classification.py) + * Hybrid code (Mealpy + + MLP): [Link](https://github.com/thieu1995/mealpy/blob/master/examples/applications/keras/mha-hybrid-mlp-classification.py) +### Mealpy + Neural Network (Optimize Neural Network Hyper-parameter) +Code: [Link](https://github.com/thieu1995/mealpy/blob/master/examples/applications/keras/mha-hyper-parameter-mlp-time-series.py) +### Other Applications +* Solving Knapsack Problem (Discrete + problems): [Link](https://github.com/thieu1995/mealpy/blob/master/examples/applications/discrete-problems/knapsack-problem.py) +* Optimize SVM (SVC) + model: [Link](https://github.com/thieu1995/mealpy/blob/master/examples/applications/sklearn/svm_classification.py) +* Optimize Linear Regression + Model: [Link](https://github.com/thieu1995/mealpy/blob/master/examples/applications/pytorch/linear_regression.py) +* Travelling Salesman Problem: https://github.com/thieu1995/MHA-TSP +* Feature selection problem: https://github.com/thieu1995/MHA-FS +## Tutorial Videos +All tutorial videos: [Link](https://mealpy.readthedocs.io/en/latest/pages/general/video_tutorials.html) +All code examples: [Link](https://github.com/thieu1995/mealpy/tree/master/examples) +All visualization examples: [Link](https://mealpy.readthedocs.io/en/latest/pages/visualization.html) +### Get helps (questions, problems) +* Official source code repo: https://github.com/thieu1995/mealpy +* Official document: https://mealpy.readthedocs.io/ +* Download releases: https://pypi.org/project/mealpy/ +* Issue tracker: https://github.com/thieu1995/mealpy/issues +* Notable changes log: https://github.com/thieu1995/mealpy/blob/master/ChangeLog.md +* Examples with different meapy version: https://github.com/thieu1995/mealpy/blob/master/EXAMPLES.md +* This project also related to our another projects which are "meta-heuristics" and "neural-network", check it here + * https://github.com/thieu1995/opfunu + * https://github.com/thieu1995/metaheuristics + * https://github.com/aiir-team +**Want to have an instant assistant? Join our telegram community at [link](https://t.me/+fRVCJGuGJg1mNDg1)** +We share lots of information, questions, and answers there. You will get more support and knowledge there. +### Cite Us +If you are using mealpy in your project, we would appreciate citations: +```bibtex +@article{van2023mealpy, + title={MEALPY: An open-source library for latest meta-heuristic algorithms in Python}, + author={Van Thieu, Nguyen and Mirjalili, Seyedali}, + journal={Journal of Systems Architecture}, + year={2023}, + publisher={Elsevier} +} +@article{van2023groundwater, + title={Groundwater level modeling using Augmented Artificial Ecosystem Optimization}, + author={Van Thieu, Nguyen and Barma, Surajit Deb and Van Lam, To and Kisi, Ozgur and Mahesha, Amai}, + journal={Journal of Hydrology}, + volume={617}, + pages={129034}, + year={2023}, + publisher={Elsevier} +} +``` +# List of papers used MEALPY +- Min, J., Oh, M., Kim, W., Seo, H., & Paek, J. (2022, October). Evaluation of Metaheuristic Algorithms for TAS Scheduling in Time-Sensitive Networking. In 2022 13th International Conference on Information and Communication Technology Convergence (ICTC) (pp. 809-812). IEEE. +- Khozeimeh, F., Sharifrazi, D., Izadi, N. H., Joloudari, J. H., Shoeibi, A., Alizadehsani, R., ... & Islam, S. M. S. (2021). Combining a convolutional neural network with autoencoders to predict the survival chance of COVID-19 patients. Scientific Reports, 11(1), 15343. +- Rajesh, K., Jain, E., & Kotecha, P. (2022). A Multi-Objective approach to the Electric Vehicle Routing Problem. arXiv preprint arXiv:2208.12440. +- Sánchez, A. J. H., & Upegui, F. R. (2022). Una herramienta para el diseño de redes MSMN de banda ancha en líneas de transmisión basada en algoritmos heurísticos de optimización comparados. Revista Ingeniería UC, 29(2), 106-123. +- Khanmohammadi, M., Armaghani, D. J., & Sabri Sabri, M. M. (2022). Prediction and Optimization of Pile Bearing Capacity Considering Effects of Time. Mathematics, 10(19), 3563. +- Kudela, J. (2023). The Evolutionary Computation Methods No One Should Use. arXiv preprint arXiv:2301.01984. +- Vieira, M., Faia, R., Pinto, T., & Vale, Z. (2022, September). Schedule Peer-to-Peer Transactions of an Energy Community Using Particle Swarm. In 2022 18th International Conference on the European Energy Market (EEM) (pp. 1-6). IEEE. +- Bui, X. N., Nguyen, H., Le, Q. T., & Le, T. N. Forecasting PM. MINING SCIENCE ANDTECHNOLOGY (Russia), 111. +- Bui, X. N., Nguyen, H., Le, Q. T., & Le, T. N. (2022). Forecasting PM 2.5 emissions in open-pit minesusing a functional link neural network optimized by various optimization algorithms. Gornye nauki i tekhnologii= Mining Science and Technology (Russia), 7(2), 111-125. +- Doğan, E., & Yörükeren, N. (2022). Enhancement of Transmission System Security with Archimedes Optimization Algorithm. +- Ayub, N., Aurangzeb, K., Awais, M., & Ali, U. (2020, November). Electricity theft detection using CNN-GRU and manta ray foraging optimization algorithm. In 2020 IEEE 23Rd international multitopic conference (INMIC) (pp. 1-6). IEEE. +- Pintilie, L., Nechita, M. T., Suditu, G. D., Dafinescu, V., & Drăgoi, E. N. (2022). Photo-decolorization of Eriochrome Black T: process optimization with Differential Evolution algorithm. In PASEW-22, MESSH-22 & CABES-22 April 19–21, 2022 Paris (France). Eminent Association of Pioneers. +- LaTorre, A., Molina, D., Osaba, E., Poyatos, J., Del Ser, J., & Herrera, F. (2021). A prescription of methodological guidelines for comparing bio-inspired optimization algorithms. Swarm and Evolutionary Computation, 67, 100973. +- Gottam, S., Nanda, S. J., & Maddila, R. K. (2021, December). A CNN-LSTM Model Trained with Grey Wolf Optimizer for Prediction of Household Power Consumption. In 2021 IEEE International Symposium on Smart Electronic Systems (iSES)(Formerly iNiS) (pp. 355-360). IEEE. +- Darius, P. S., Devadason, J., & Solomon, D. G. (2022, December). Prospects of Ant Colony Optimization (ACO) in Various Domains. In 2022 4th International Conference on Circuits, Control, Communication and Computing (I4C) (pp. 79-84). IEEE. +- Ayub, N., Irfan, M., Awais, M., Ali, U., Ali, T., Hamdi, M., ... & Muhammad, F. (2020). Big data analytics for short and medium-term electricity load forecasting using an AI techniques ensembler. Energies, 13(19), 5193. +- Biundini, I. Z., Melo, A. G., Coelho, F. O., Honório, L. M., Marcato, A. L., & Pinto, M. F. (2022). Experimentation and Simulation with Autonomous Coverage Path Planning for UAVs. Journal of Intelligent & Robotic Systems, 105(2), 46. +- Yousaf, I., Anwar, F., Imtiaz, S., Almadhor, A. S., Ishmanov, F., & Kim, S. W. (2022). An Optimized Hyperparameter of Convolutional Neural Network Algorithm for Bug Severity Prediction in Alzheimer’s-Based IoT System. Computational Intelligence and Neuroscience, 2022. +- Xu, L., Yan, W., & Ji, J. (2023). The research of a novel WOG-YOLO algorithm for autonomous driving object detection. Scientific reports, 13(1), 3699. +- Costache, R. D., Arabameri, A., Islam, A. R. M. T., Abba, S. I., Pandey, M., Ajin, R. S., & Pham, B. T. (2022). Flood susceptibility computation using state-of-the-art machine learning and optimization algorithms. +- Del Ser, J., Osaba, E., Martinez, A. D., Bilbao, M. N., Poyatos, J., Molina, D., & Herrera, F. (2021, December). More is not always better: insights from a massive comparison of meta-heuristic algorithms over real-parameter optimization problems. In 2021 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 1-7). IEEE. +- Rustam, F., Aslam, N., De La Torre Díez, I., Khan, Y. D., Mazón, J. L. V., Rodríguez, C. L., & Ashraf, I. (2022, November). White Blood Cell Classification Using Texture and RGB Features of Oversampled Microscopic Images. In Healthcare (Vol. 10, No. 11, p. 2230). MDPI. +- Neupane, D., Kafle, S., Gurung, S., Neupane, S., & Bhattarai, N. (2021). Optimal sizing and financial analysis of a stand-alone SPV-micro-hydropower hybrid system considering generation uncertainty. International Journal of Low-Carbon Technologies, 16(4), 1479-1491. +- Liang, R., Le-Hung, T., & Nguyen-Thoi, T. (2022). Energy consumption prediction of air-conditioning systems in eco-buildings using hunger games search optimization-based artificial neural network model. Journal of Building Engineering, 59, 105087. +- He, Z., Nguyen, H., Vu, T. H., Zhou, J., Asteris, P. G., & Mammou, A. (2022). Novel integrated approaches for predicting the compressibility of clay using cascade forward neural networks optimized by swarm-and evolution-based algorithms. Acta Geotechnica, 1-16. +- Xu, L., Yan, W., & Ji, J. (2022). The research of a novel WOG-YOLO algorithm forautonomous driving object detection. +- Nasir Ayub, M. I., Awais, M., Ali, U., Ali, T., Hamdi, M., Alghamdi, A., & Muhammad, F. Big Data Analytics for Short and Medium Term Electricity Load Forecasting using AI Techniques Ensembler. +- Xie, C., Nguyen, H., Choi, Y., & Armaghani, D. J. (2022). Optimized functional linked neural network for predicting diaphragm wall deflection induced by braced excavations in clays. Geoscience Frontiers, 13(2), 101313. +- Hakemi, S., Houshmand, M., & Hosseini, S. A. (2022). A Dynamic Quantum-Inspired Genetic Algorithm with Lengthening Chromosome Size. +- Kashifi, M. T. City-Wide Crash Risk Prediction and Interpretation Using Deep Learning Model with Multi-Source Big Data. Available at SSRN 4329686. +- Nguyen, H., & Hoang, N. D. (2022). Computer vision-based classification of concrete spall severity using metaheuristic-optimized Extreme Gradient Boosting Machine and Deep Convolutional Neural Network. Automation in Construction, 140, 104371. +- Zheng, J., Lu, Z., Wu, K., Ning, G. H., & Li, D. (2020). Coinage-metal-based cyclic trinuclear complexes with metal–metal interactions: Theories to experiments and structures to functions. Chemical Reviews, 120(17), 9675-9742. +- Van Thieu, N., Barma, S. D., Van Lam, T., Kisi, O., & Mahesha, A. (2023). Groundwater level modeling using Augmented Artificial Ecosystem Optimization. Journal of Hydrology, 617, 129034. +- Mo, Z., Zhang, Z., Miao, Q., & Tsui, K. L. (2022). Intelligent Informative Frequency Band Searching Assisted by a Dynamic Bandit Tree Method for Machine Fault Diagnosis. IEEE/ASME Transactions on Mechatronics. +- Dangi, D., Chandel, S. T., Dixit, D. K., Sharma, S., & Bhagat, A. (2023). An Efficient Model for Sentiment Analysis using Artificial Rabbits Optimized Vector Functional Link Network. Expert Systems with Applications, 119849. +- Dey, S., Roychoudhury, R., Malakar, S., & Sarkar, R. (2022). An optimized fuzzy ensemble of convolutional neural networks for detecting tuberculosis from Chest X-ray images. Applied Soft Computing, 114, 108094. +- Mousavirad, S. J., & Alexandre, L. A. (2022). Population-based JPEG Image Compression: Problem Re-Formulation. arXiv preprint arXiv:2212.06313. +- Tsui, K. L. Intelligent Informative Frequency Band Searching Assisted by A Dynamic Bandit Tree Method for Machine Fault Diagnosis. +- Neupane, D. (2020). Optimal Sizing and Performance Analysis of Solar PV-Micro hydropower Hybrid System in the Context of Rural Area of Nepal (Doctoral dissertation, Pulchowk Campus). +- LaTorre, A., Molina, D., Osaba, E., Poyatos, J., Del Ser, J., & Herrera, F. Swarm and Evolutionary Computation. +- Vieira, M. A. (2022). Otimização dos custos operacionais de uma comunidade energética considerando transações locais em “peer-to-peer” (Doctoral dissertation). +- Toğaçar, M. (2022). Using DarkNet models and metaheuristic optimization methods together to detect weeds growing along with seedlings. Ecological Informatics, 68, 101519. +- Toğaçar, M. (2021). Detection of segmented uterine cancer images by Hotspot Detection method using deep learning models, Pigeon-Inspired Optimization, types-based dominant activation selection approaches. Computers in Biology and Medicine, 136, 104659. +- Khan, N. A Short Term Electricity Load and Price Forecasting Model Based on BAT Algorithm in Logistic Regression and CNN-GRU with WOA. +- Yelisetti, S., Saini, V. K., Kumar, R., & Lamba, R. (2022, May). Energy Consumption Cost Benefits through Smart Home Energy Management in Residential Buildings: An Indian Case Study. In 2022 IEEE IAS Global Conference on Emerging Technologies (GlobConET) (pp. 930-935). IEEE. +- Nguyen, H., Cao, M. T., Tran, X. L., Tran, T. H., & Hoang, N. D. (2022). A novel whale optimization algorithm optimized XGBoost regression for estimating bearing capacity of concrete piles. Neural Computing and Applications, 1-28. +- Hirsching, C., de Jongh, S., Eser, D., Suriyah, M., & Leibfried, T. (2022). Meta-heuristic optimization of control structure and design for MMC-HVdc applications. Electric Power Systems Research, 213, 108371. +- Amelin, V., Gatiyatullin, E., Romanov, N., Samarkhanov, R., Vasilyev, R., & Yanovich, Y. (2022). Black-Box for Blockchain Parameters Adjustment. IEEE Access, 10, 101795-101802. +- Ngo, T. Q., Nguyen, L. Q., & Tran, V. Q. (2022). Novel hybrid machine learning models including support vector machine with meta-heuristic algorithms in predicting unconfined compressive strength of organic soils stabilised with cement and lime. International Journal of Pavement Engineering, 1-18. +- Zhu, Y., & Iiduka, H. (2021). Unified Algorithm Framework for Nonconvex Stochastic Optimization in Deep Neural Networks. IEEE Access, 9, 143807-143823. +- Hakemi, S., Houshmand, M., KheirKhah, E., & Hosseini, S. A. (2022). A review of recent advances in quantum-inspired metaheuristics. Evolutionary Intelligence, 1-16. +- Das, A., Das, S. R., Panda, J. P., Dey, A., Gajrani, K. K., Somani, N., & Gupta, N. (2022). Machine learning based modelling and optimization in hard turning of AISI D6 steel with newly developed AlTiSiN coated carbide tool. arXiv preprint arXiv:2202.00596. +- Yelisetti, S., Saini, V. K., Kumar, R., Lamba, R., & Saxena, A. (2022). Optimal energy management system for residential buildings considering the time of use price with swarm intelligence algorithms. Journal of Building Engineering, 59, 105062. +- Valdés, G. T. (2022). Algoritmo para la detección de vehículos y peatones combinando CNN´ sy técnicas de búsqueda. +- Sallam, N. M., Saleh, A. I., Ali, H. A., & Abdelsalam, M. M. (2023). An efficient EGWO algorithm as feature selection for B-ALL diagnoses and its subtypes classification using peripheral blood smear images. Alexandria Engineering Journal, 68, 39-66. +# Documents +* Meta-heuristic Categories: (Based on this article: [link](https://doi.org/10.1016/j.procs.2020.09.075)) + + Evolutionary-based: Idea from Darwin's law of natural selection, evolutionary computing + + Swarm-based: Idea from movement, interaction of birds, organization of social ... + + Physics-based: Idea from physics law such as Newton's law of universal gravitation, black hole, multiverse + + Human-based: Idea from human interaction such as queuing search, teaching learning, ... + + Biology-based: Idea from biology creature (or microorganism),... + + System-based: Idea from eco-system, immune-system, network-system, ... + + Math-based: Idea from mathematical form or mathematical law such as sin-cosin + + Music-based: Idea from music instrument +* Difficulty - Difficulty Level (Personal Opinion): **Objective observation from author**. Depend on the number of + parameters, number of equations, the original ideas, time spend for coding, source lines of code (SLOC). + + Easy: A few paras, few equations, SLOC very short + + Medium: more equations than Easy level, SLOC longer than Easy level + + Hard: Lots of equations, SLOC longer than Medium level, the paper hard to read. + + Hard* - Very hard: Lots of equations, SLOC too long, the paper is very hard to read. +** For newbie, we recommend to read the paper of algorithms which difficulty is "easy" or "medium" difficulty level. +| **Group** | **Name** | **Module** | **Class** | **Year** | **Paras** | **Difficulty** | +|--------------|-------------------------------------------------|------------|------------------|----------|-----------|----------------| +| Evolutionary | Evolutionary Programming | EP | OriginalEP | 1964 | 3 | easy | +| Evolutionary | - | - | LevyEP | - | 3 | easy | +| Evolutionary | Evolution Strategies | ES | OriginalES | 1971 | 3 | easy | +| Evolutionary | - | - | LevyES | - | 3 | easy | +| Evolutionary | Memetic Algorithm | MA | OriginalMA | 1989 | 7 | easy | +| Evolutionary | Genetic Algorithm | GA | BaseGA | 1992 | 4 | easy | +| Evolutionary | - | - | SingleGA | - | 7 | easy | +| Evolutionary | - | - | MultiGA | - | 7 | easy | +| Evolutionary | - | - | EliteSingleGA | - | 10 | easy | +| Evolutionary | - | - | EliteMultiGA | - | 10 | easy | +| Evolutionary | Differential Evolution | DE | BaseDE | 1997 | 5 | easy | +| Evolutionary | - | - | JADE | 2009 | 6 | medium | +| Evolutionary | - | - | SADE | 2005 | 2 | medium | +| Evolutionary | - | - | SHADE | 2013 | 4 | medium | +| Evolutionary | - | - | L_SHADE | 2014 | 4 | medium | +| Evolutionary | - | - | SAP_DE | 2006 | 3 | medium | +| Evolutionary | Flower Pollination Algorithm | FPA | OriginalFPA | 2014 | 4 | medium | +| Evolutionary | Coral Reefs Optimization | CRO | OriginalCRO | 2014 | 11 | medium | +| Evolutionary | - | - | OCRO | 2019 | 12 | medium | +| - | - | - | - | - | - | - | +| Swarm | Particle Swarm Optimization | PSO | OriginalPSO | 1995 | 6 | easy | +| Swarm | - | - | PPSO | 2019 | 2 | medium | +| Swarm | - | - | HPSO_TVAC | 2017 | 4 | medium | +| Swarm | - | - | C_PSO | 2015 | 6 | medium | +| Swarm | - | - | CL_PSO | 2006 | 6 | medium | +| Swarm | Bacterial Foraging Optimization | BFO | OriginalBFO | 2002 | 10 | hard | +| Swarm | - | - | ABFO | 2019 | 8 | medium | +| Swarm | Bees Algorithm | BeesA | OriginalBeesA | 2005 | 8 | medium | +| Swarm | - | - | ProbBeesA | 2015 | 5 | medium | +| Swarm | Cat Swarm Optimization | CSO | OriginalCSO | 2006 | 11 | hard | +| Swarm | Artificial Bee Colony | ABC | OriginalABC | 2007 | 8 | medium | +| Swarm | Ant Colony Optimization | ACO-R | OriginalACOR | 2008 | 5 | easy | +| Swarm | Cuckoo Search Algorithm | CSA | OriginalCSA | 2009 | 3 | medium | +| Swarm | Firefly Algorithm | FFA | OriginalFFA | 2009 | 8 | easy | +| Swarm | Fireworks Algorithm | FA | OriginalFA | 2010 | 7 | medium | +| Swarm | Bat Algorithm | BA | OriginalBA | 2010 | 6 | medium | +| Swarm | - | - | AdaptiveBA | - | 8 | medium | +| Swarm | - | - | ModifiedBA | - | 5 | medium | +| Swarm | Fruit-fly Optimization Algorithm | FOA | OriginalFOA | 2012 | 2 | easy | +| Swarm | - | - | BaseFOA | - | 2 | easy | +| Swarm | - | - | WhaleFOA | 2020 | 2 | medium | +| Swarm | Social Spider Optimization | SSpiderO | OriginalSSpiderO | 2018 | 4 | hard* | +| Swarm | Grey Wolf Optimizer | GWO | OriginalGWO | 2014 | 2 | easy | +| Swarm | - | - | RW_GWO | 2019 | 2 | easy | +| Swarm | Social Spider Algorithm | SSpiderA | OriginalSSpiderA | 2015 | 5 | medium | +| Swarm | Ant Lion Optimizer | ALO | OriginalALO | 2015 | 2 | easy | +| Swarm | - | - | BaseALO | - | 2 | easy | +| Swarm | Moth Flame Optimization | MFO | OriginalMFO | 2015 | 2 | easy | +| Swarm | - | - | BaseMFO | - | 2 | easy | +| Swarm | Elephant Herding Optimization | EHO | OriginalEHO | 2015 | 5 | easy | +| Swarm | Jaya Algorithm | JA | OriginalJA | 2016 | 2 | easy | +| Swarm | - | - | BaseJA | - | 2 | easy | +| Swarm | - | - | LevyJA | 2021 | 2 | easy | +| Swarm | Whale Optimization Algorithm | WOA | OriginalWOA | 2016 | 2 | medium | +| Swarm | - | - | HI_WOA | 2019 | 3 | medium | +| Swarm | Dragonfly Optimization | DO | OriginalDO | 2016 | 2 | medium | +| Swarm | Bird Swarm Algorithm | BSA | OriginalBSA | 2016 | 9 | medium | +| Swarm | Spotted Hyena Optimizer | SHO | OriginalSHO | 2017 | 4 | medium | +| Swarm | Salp Swarm Optimization | SSO | OriginalSSO | 2017 | 2 | easy | +| Swarm | Swarm Robotics Search And Rescue | SRSR | OriginalSRSR | 2017 | 2 | hard* | +| Swarm | Grasshopper Optimisation Algorithm | GOA | OriginalGOA | 2017 | 4 | easy | +| Swarm | Coyote Optimization Algorithm | COA | OriginalCOA | 2018 | 3 | medium | +| Swarm | Moth Search Algorithm | MSA | OriginalMSA | 2018 | 5 | easy | +| Swarm | Sea Lion Optimization | SLO | OriginalSLO | 2019 | 2 | medium | +| Swarm | - | - | ModifiedSLO | - | 2 | medium | +| Swarm | - | - | ImprovedSLO | - | 4 | medium | +| Swarm | Nake Mole-Rat Algorithm | NMRA | OriginalNMRA | 2019 | 3 | easy | +| Swarm | - | - | ImprovedNMRA | - | 4 | medium | +| Swarm | Pathfinder Algorithm | PFA | OriginalPFA | 2019 | 2 | medium | +| Swarm | Sailfish Optimizer | SFO | OriginalSFO | 2019 | 5 | easy | +| Swarm | - | - | ImprovedSFO | - | 3 | medium | +| Swarm | Harris Hawks Optimization | HHO | OriginalHHO | 2019 | 2 | medium | +| Swarm | Manta Ray Foraging Optimization | MRFO | OriginalMRFO | 2020 | 3 | medium | +| Swarm | Bald Eagle Search | BES | OriginalBES | 2020 | 7 | easy | +| Swarm | Sparrow Search Algorithm | SSA | OriginalSSA | 2020 | 5 | medium | +| Swarm | - | - | BaseSSA | - | 5 | medium | +| Swarm | Hunger Games Search | HGS | OriginalHGS | 2021 | 4 | medium | +| Swarm | Aquila Optimizer | AO | OriginalAO | 2021 | 2 | easy | +| Swarm | Hybrid Grey Wolf - Whale Optimization Algorithm | GWO | GWO_WOA | 2022 | 2 | easy | +| Swarm | Marine Predators Algorithm | MPA | OriginalMPA | 2020 | 2 | medium | +| Swarm | Honey Badger Algorithm | HBA | OriginalHBA | 2022 | 2 | easy | +| Swarm | Sand Cat Swarm Optimization | SCSO | OriginalSCSO | 2022 | 2 | easy | +| Swarm | Tuna Swarm Optimization | TSO | OriginalTSO | 2021 | 2 | medium | +| Swarm | African Vultures Optimization Algorithm | AVOA | OriginalAVOA | 2022 | 7 | medium | +| Swarm | Artificial Gorilla Troops Optimization | AGTO | OriginalAGTO | 2021 | 5 | medium | +| Swarm | Artificial Rabbits Optimization | ARO | OriginalARO | 2022 | 2 | easy | +| Swarm | Dwarf Mongoose Optimization Algorithm | DMOA | OriginalDMOA | 2022 | 4 | medium | +| Swarm | - | - | DevDMOA | - | 3 | medium | +| - | - | - | - | - | - | - | +| Physics | Simulated Annealling | SA | OriginalSA | 1987 | 9 | medium | +| Physics | Wind Driven Optimization | WDO | OriginalWDO | 2013 | 7 | easy | +| Physics | Multi-Verse Optimizer | MVO | OriginalMVO | 2016 | 4 | easy | +| Physics | - | - | BaseMVO | - | 4 | easy | +| Physics | Tug of War Optimization | TWO | OriginalTWO | 2016 | 2 | easy | +| Physics | - | - | OppoTWO | - | 2 | medium | +| Physics | - | - | LevyTWO | - | 2 | medium | +| Physics | - | - | EnhancedTWO | 2020 | 2 | medium | +| Physics | Electromagnetic Field Optimization | EFO | OriginalEFO | 2016 | 6 | easy | +| Physics | - | - | BaseEFO | - | 6 | medium | +| Physics | Nuclear Reaction Optimization | NRO | OriginalNRO | 2019 | 2 | hard* | +| Physics | Henry Gas Solubility Optimization | HGSO | OriginalHGSO | 2019 | 3 | medium | +| Physics | Atom Search Optimization | ASO | OriginalASO | 2019 | 4 | medium | +| Physics | Equilibrium Optimizer | EO | OriginalEO | 2019 | 2 | easy | +| Physics | - | - | ModifiedEO | 2020 | 2 | medium | +| Physics | - | - | AdaptiveEO | 2020 | 2 | medium | +| Physics | Archimedes Optimization Algorithm | ArchOA | OriginalArchOA | 2021 | 8 | medium | +| - | - | - | - | - | - | - | +| Human | Culture Algorithm | CA | OriginalCA | 1994 | 3 | easy | +| Human | Imperialist Competitive Algorithm | ICA | OriginalICA | 2007 | 8 | hard* | +| Human | Teaching Learning-based Optimization | TLO | OriginalTLO | 2011 | 2 | easy | +| Human | - | - | BaseTLO | 2012 | 2 | easy | +| Human | - | - | ITLO | 2013 | 3 | medium | +| Human | Brain Storm Optimization | BSO | OriginalBSO | 2011 | 8 | medium | +| Human | - | - | ImprovedBSO | 2017 | 7 | medium | +| Human | Queuing Search Algorithm | QSA | OriginalQSA | 2019 | 2 | hard | +| Human | - | - | BaseQSA | - | 2 | hard | +| Human | - | - | OppoQSA | - | 2 | hard | +| Human | - | - | LevyQSA | - | 2 | hard | +| Human | - | - | ImprovedQSA | 2021 | 2 | hard | +| Human | Search And Rescue Optimization | SARO | OriginalSARO | 2019 | 4 | medium | +| Human | - | - | BaseSARO | - | 4 | medium | +| Human | Life Choice-Based Optimization | LCO | OriginalLCO | 2019 | 3 | easy | +| Human | - | - | BaseLCO | - | 3 | easy | +| Human | - | - | ImprovedLCO | - | 2 | easy | +| Human | Social Ski-Driver Optimization | SSDO | OriginalSSDO | 2019 | 2 | easy | +| Human | Gaining Sharing Knowledge-based Algorithm | GSKA | OriginalGSKA | 2019 | 6 | medium | +| Human | - | - | BaseGSKA | - | 4 | medium | +| Human | Coronavirus Herd Immunity Optimization | CHIO | OriginalCHIO | 2020 | 4 | medium | +| Human | - | - | BaseCHIO | - | 4 | medium | +| Human | Forensic-Based Investigation Optimization | FBIO | OriginalFBIO | 2020 | 2 | medium | +| Human | - | - | BaseFBIO | - | 2 | medium | +| Human | Battle Royale Optimization | BRO | OriginalBRO | 2020 | 3 | medium | +| Human | - | - | BaseBRO | - | 3 | medium | +| Human | Student Psychology Based Optimization | SPBO | OriginalSPBO | 2020 | 2 | medium | +| Human | - | - | DevSPBO | | 2 | medium | +| - | - | - | - | - | - | - | +| Bio | Invasive Weed Optimization | IWO | OriginalIWO | 2006 | 7 | easy | +| Bio | Biogeography-Based Optimization | BBO | OriginalBBO | 2008 | 4 | easy | +| Bio | - | - | BaseBBO | - | 4 | easy | +| Bio | Virus Colony Search | VCS | OriginalVCS | 2016 | 4 | hard* | +| Bio | - | - | BaseVCS | - | 4 | hard* | +| Bio | Satin Bowerbird Optimizer | SBO | OriginalSBO | 2017 | 5 | easy | +| Bio | - | - | BaseSBO | - | 5 | easy | +| Bio | Earthworm Optimisation Algorithm | EOA | OriginalEOA | 2018 | 8 | medium | +| Bio | Wildebeest Herd Optimization | WHO | OriginalWHO | 2019 | 12 | hard | +| Bio | Slime Mould Algorithm | SMA | OriginalSMA | 2020 | 3 | easy | +| Bio | - | - | BaseSMA | - | 3 | easy | +| Bio | Barnacles Mating Optimizer | BMO | OriginalBMO | 2018 | 3 | easy | +| Bio | Tunicate Swarm Algorithm | TSA | OriginalTSA | 2020 | 2 | easy | +| Bio | Symbiotic Organisms Search | SOS | OriginalSOS | 2014 | 2 | medium | +| Bio | Seagull Optimization Algorithm | SOA | OriginalSOA | 2019 | 3 | easy | +| Bio | - | - | DevSOA | - | 3 | easy | +| - | - | - | - | - | - | - | +| System | Germinal Center Optimization | GCO | OriginalGCO | 2018 | 4 | medium | +| System | - | - | BaseGCO | - | 4 | medium | +| System | Water Cycle Algorithm | WCA | OriginalWCA | 2012 | 5 | medium | +| System | Artificial Ecosystem-based Optimization | AEO | OriginalAEO | 2019 | 2 | easy | +| System | - | - | EnhancedAEO | 2020 | 2 | medium | +| System | - | - | ModifiedAEO | 2020 | 2 | medium | +| System | - | - | ImprovedAEO | 2021 | 2 | medium | +| System | - | - | AugmentedAEO | 2022 | 2 | medium | +| - | - | - | - | - | - | - | +| Math | Hill Climbing | HC | OriginalHC | 1993 | 3 | easy | +| Math | - | - | SwarmHC | - | 3 | easy | +| Math | Cross-Entropy Method | CEM | OriginalCEM | 1997 | 4 | easy | +| Math | Sine Cosine Algorithm | SCA | OriginalSCA | 2016 | 2 | easy | +| Math | - | - | BaseSCA | - | 2 | easy | +| Math | Gradient-Based Optimizer | GBO | OriginalGBO | 2020 | 5 | medium | +| Math | Arithmetic Optimization Algorithm | AOA | OrginalAOA | 2021 | 6 | easy | +| Math | Chaos Game Optimization | CGO | OriginalCGO | 2021 | 2 | easy | +| Math | Pareto-like Sequential Sampling | PSS | OriginalPSS | 2021 | 4 | medium | +| Math | weIghted meaN oF vectOrs | INFO | OriginalINFO | 2022 | 2 | medium | +| Math | RUNge Kutta optimizer | RUN | OriginalRUN | 2021 | 2 | hard | +| Math | Circle Search Algorithm | CircleSA | OriginalCircleSA | 2022 | 3 | easy | +| - | - | - | - | - | - | - | +| Music | Harmony Search | HS | OriginalHS | 2001 | 4 | easy | +| Music | - | - | BaseHS | - | 4 | easy | +### A +* **ABC - Artificial Bee Colony** + * **OriginalABC**: Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization (Vol. 200, pp. 1-10). Technical report-tr06, Erciyes university, engineering faculty, computer engineering department. +* **ACOR - Ant Colony Optimization**. + * **OriginalACOR**: Socha, K., & Dorigo, M. (2008). Ant colony optimization for continuous domains. European journal of operational research, 185(3), 1155-1173. +* **ALO - Ant Lion Optimizer** + * **OriginalALO**: Mirjalili S (2015). “The Ant Lion Optimizer.” Advances in Engineering Software, 83, 80-98. doi: [10.1016/j.advengsoft.2015.01.010](https://doi.org/10.1016/j.advengsoft.2015.01.010) + * **BaseALO**: The developed version +* **AEO - Artificial Ecosystem-based Optimization** + * **OriginalAEO**: Zhao, W., Wang, L., & Zhang, Z. (2019). Artificial ecosystem-based optimization: a novel nature-inspired meta-heuristic algorithm. Neural Computing and Applications, 1-43. + * **AugmentedAEO**: Van Thieu, N., Barma, S. D., Van Lam, T., Kisi, O., & Mahesha, A. (2022). Groundwater level modeling using Augmented Artificial Ecosystem Optimization. Journal of Hydrology, 129034. + * **ImprovedAEO**: Rizk-Allah, R. M., & El-Fergany, A. A. (2020). Artificial ecosystem optimizer for parameters identification of proton exchange membrane fuel cells model. International Journal of Hydrogen Energy. + * **EnhancedAEO**: Eid, A., Kamel, S., Korashy, A., & Khurshaid, T. (2020). An Enhanced Artificial Ecosystem-Based Optimization for Optimal Allocation of Multiple Distributed Generations. IEEE Access, 8, 178493-178513. + * **ModifiedAEO**: Menesy, A. S., Sultan, H. M., Korashy, A., Banakhr, F. A., Ashmawy, M. G., & Kamel, S. (2020). Effective parameter extraction of different polymer electrolyte membrane fuel cell stack models using a modified artificial ecosystem optimization algorithm. IEEE Access, 8, 31892-31909. +* **ASO - Atom Search Optimization** + * **OriginalASO**: Zhao, W., Wang, L., & Zhang, Z. (2019). Atom search optimization and its application to solve a hydrogeologic parameter estimation problem. Knowledge-Based Systems, 163, 283-304. +* **ArchOA - Archimedes Optimization Algorithm** + * **OriginalArchOA**: Hashim, F. A., Hussain, K., Houssein, E. H., Mabrouk, M. S., & Al-Atabany, W. (2021). Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems. Applied Intelligence, 51(3), 1531-1551. +* **AOA - Arithmetic Optimization Algorithm** + * **OriginalAOA**: Abualigah, L., Diabat, A., Mirjalili, S., Abd Elaziz, M., & Gandomi, A. H. (2021). The arithmetic optimization algorithm. Computer methods in applied mechanics and engineering, 376, 113609. +* **AO - Aquila Optimizer** + * **OriginalAO**: Abualigah, L., Yousri, D., Abd Elaziz, M., Ewees, A. A., Al-qaness, M. A., & Gandomi, A. H. (2021). Aquila Optimizer: A novel meta-heuristic optimization Algorithm. Computers & Industrial Engineering, 157, 107250. +* **AVOA - African Vultures Optimization Algorithm** + * **OriginalAVOA**: Abdollahzadeh, B., Gharehchopogh, F. S., & Mirjalili, S. (2021). African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems. Computers & Industrial Engineering, 158, 107408. +* **AGTO - Artificial Gorilla Troops Optimization** + * **OriginalAGTO**: Abdollahzadeh, B., Soleimanian Gharehchopogh, F., & Mirjalili, S. (2021). Artificial gorilla troops optimizer: a new nature‐inspired metaheuristic algorithm for global optimization problems. International Journal of Intelligent Systems, 36(10), 5887-5958. +* **ARO - Artificial Rabbits Optimization**: + * **OriginalARO**: Wang, L., Cao, Q., Zhang, Z., Mirjalili, S., & Zhao, W. (2022). Artificial rabbits optimization: A new bio-inspired meta-heuristic algorithm for solving engineering optimization problems. Engineering Applications of Artificial Intelligence, 114, 105082. +### B +* **BFO - Bacterial Foraging Optimization** + * **OriginalBFO**: Passino, K. M. (2002). Biomimicry of bacterial foraging for distributed optimization and control. IEEE control systems magazine, 22(3), 52-67. + * **ABFO**: Nguyen, T., Nguyen, B. M., & Nguyen, G. (2019, April). Building resource auto-scaler with functional-link neural network and adaptive bacterial foraging optimization. In International Conference on Theory and Applications of Models of Computation (pp. 501-517). Springer, Cham. +* **BeesA - Bees Algorithm** + * **OriginalBeesA**: Pham, D. T., Ghanbarzadeh, A., Koc, E., Otri, S., Rahim, S., & Zaidi, M. (2005). The bees algorithm. Technical Note, Manufacturing Engineering Centre, Cardiff University, UK. + * **ProbBeesA**: The probabilitic version of: Pham, D. T., Ghanbarzadeh, A., Koç, E., Otri, S., Rahim, S., & Zaidi, M. (2006). The bees algorithm—a novel tool for complex optimisation problems. In Intelligent production machines and systems (pp. 454-459). Elsevier Science Ltd. +* **BBO - Biogeography-Based Optimization** + * **OriginalBBO**: Simon, D. (2008). Biogeography-based optimization. IEEE transactions on evolutionary computation, 12(6), 702-713. + * **BaseBBO**: The developed version +* **BA - Bat Algorithm** + * **OriginalBA**: Yang, X. S. (2010). A new metaheuristic bat-inspired algorithm. In Nature inspired cooperative strategies for optimization (NICSO 2010) (pp. 65-74). Springer, Berlin, Heidelberg. + * **AdaptiveBA**: Wang, X., Wang, W. and Wang, Y., 2013, July. An adaptive bat algorithm. In International Conference on Intelligent Computing(pp. 216-223). Springer, Berlin, Heidelberg. + * **ModifiedBA**: Dong, H., Li, T., Ding, R. and Sun, J., 2018. A novel hybrid genetic algorithm with granular information for feature selection and optimization. Applied Soft Computing, 65, pp.33-46. +* **BSO - Brain Storm Optimization** + * **OriginalBSO**: . Shi, Y. (2011, June). Brain storm optimization algorithm. In International conference in swarm intelligence (pp. 303-309). Springer, Berlin, Heidelberg. + * **ImprovedBSO**: El-Abd, M., 2017. Global-best brain storm optimization algorithm. Swarm and evolutionary computation, 37, pp.27-44. +* **BSA - Bird Swarm Algorithm** + * **OriginalBSA**: Meng, X. B., Gao, X. Z., Lu, L., Liu, Y., & Zhang, H. (2016). A new bio-inspired optimisation algorithm:Bird Swarm Algorithm. Journal of Experimental & Theoretical Artificial Intelligence, 28(4), 673-687. +* **BMO - Barnacles Mating Optimizer**: + * **OriginalBMO**: Sulaiman, M. H., Mustaffa, Z., Saari, M. M., Daniyal, H., Daud, M. R., Razali, S., & Mohamed, A. I. (2018, June). Barnacles mating optimizer: a bio-inspired algorithm for solving optimization problems. In 2018 19th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) (pp. 265-270). IEEE. +* **BES - Bald Eagle Search** + * **OriginalBES**: Alsattar, H. A., Zaidan, A. A., & Zaidan, B. B. (2019). Novel meta-heuristic bald eagle search optimisation algorithm. Artificial Intelligence Review, 1-28. +* **BRO - Battle Royale Optimization** + * **OriginalBRO**: Rahkar Farshi, T. (2020). Battle royale optimization algorithm. Neural Computing and Applications, 1-19. + * **BaseBRO**: The developed version +### C +* **CA - Culture Algorithm** + * **OriginalCA**: Reynolds, R.G., 1994, February. An introduction to cultural algorithms. In Proceedings of the third annual conference on evolutionary programming (Vol. 24, pp. 131-139). River Edge, NJ: World Scientific. +* **CEM - Cross Entropy Method** + * **OriginalCEM**: Rubinstein, R. (1999). The cross-entropy method for combinatorial and continuous optimization. Methodology and computing in applied probability, 1(2), 127-190. +* **CSO - Cat Swarm Optimization** + * **OriginalCSO**: Chu, S. C., Tsai, P. W., & Pan, J. S. (2006, August). Cat swarm optimization. In Pacific Rim international conference on artificial intelligence (pp. 854-858). Springer, Berlin, Heidelberg. +* **CSA - Cuckoo Search Algorithm** + * **OriginalCSA**: Yang, X. S., & Deb, S. (2009, December). Cuckoo search via Lévy flights. In 2009 World congress on nature & biologically inspired computing (NaBIC) (pp. 210-214). Ieee. +* **CRO - Coral Reefs Optimization** + * **OriginalCRO**: Salcedo-Sanz, S., Del Ser, J., Landa-Torres, I., Gil-López, S., & Portilla-Figueras, J. A. (2014). The coral reefs optimization algorithm: a novel metaheuristic for efficiently solving optimization problems. The Scientific World Journal, 2014. + * **OCRO**: Nguyen, T., Nguyen, T., Nguyen, B. M., & Nguyen, G. (2019). Efficient time-series forecasting using neural network and opposition-based coral reefs optimization. International Journal of Computational Intelligence Systems, 12(2), 1144-1161. +* **COA - Coyote Optimization Algorithm** + * **OriginalCOA**: Pierezan, J., & Coelho, L. D. S. (2018, July). Coyote optimization algorithm: a new metaheuristic for global optimization problems. In 2018 IEEE congress on evolutionary computation (CEC) (pp. 1-8). IEEE. +* **CHIO - Coronavirus Herd Immunity Optimization** + * **OriginalCHIO**: Al-Betar, M. A., Alyasseri, Z. A. A., Awadallah, M. A., & Abu Doush, I. (2021). Coronavirus herd immunity optimizer (CHIO). Neural Computing and Applications, 33(10), 5011-5042. + * **BaseCHIO**: The developed version +* **CGO - Chaos Game Optimization** + * **OriginalCGO**: Talatahari, S., & Azizi, M. (2021). Chaos Game Optimization: a novel metaheuristic algorithm. Artificial Intelligence Review, 54(2), 917-1004. +* **CSA - Circle Search Algorithm** + * **OriginalCSA**: Qais, M. H., Hasanien, H. M., Turky, R. A., Alghuwainem, S., Tostado-Véliz, M., & Jurado, F. (2022). Circle Search Algorithm: A Geometry-Based Metaheuristic Optimization Algorithm. Mathematics, 10(10), 1626. +### D +* **DE - Differential Evolution** + * **BaseDE**: Storn, R., & Price, K. (1997). Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization, 11(4), 341-359. + * **JADE**: Zhang, J., & Sanderson, A. C. (2009). JADE: adaptive differential evolution with optional external archive. IEEE Transactions on evolutionary computation, 13(5), 945-958. + * **SADE**: Qin, A. K., & Suganthan, P. N. (2005, September). Self-adaptive differential evolution algorithm for numerical optimization. In 2005 IEEE congress on evolutionary computation (Vol. 2, pp. 1785-1791). IEEE. + * **SHADE**: Tanabe, R., & Fukunaga, A. (2013, June). Success-history based parameter adaptation for differential evolution. In 2013 IEEE congress on evolutionary computation (pp. 71-78). IEEE. + * **L_SHADE**: Tanabe, R., & Fukunaga, A. S. (2014, July). Improving the search performance of SHADE using linear population size reduction. In 2014 IEEE congress on evolutionary computation (CEC) (pp. 1658-1665). IEEE. + * **SAP_DE**: Teo, J. (2006). Exploring dynamic self-adaptive populations in differential evolution. Soft Computing, 10(8), 673-686. +* **DSA - Differential Search Algorithm (not done)** + * **BaseDSA**: Civicioglu, P. (2012). Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm. Computers & Geosciences, 46, 229-247. +* **DO - Dragonfly Optimization** + * **OriginalDO**: Mirjalili, S. (2016). Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Computing and Applications, 27(4), 1053-1073. +* **DMOA - Dwarf Mongoose Optimization Algorithm** + * **OriginalDMOA**: Agushaka, J. O., Ezugwu, A. E., & Abualigah, L. (2022). Dwarf mongoose optimization algorithm. Computer methods in applied mechanics and engineering, 391, 114570. + * **DevDMOA**: The developed version +### E +* **ES - Evolution Strategies** . + * **OriginalES**: Schwefel, H. P. (1984). Evolution strategies: A family of non-linear optimization techniques based on imitating some principles of organic evolution. Annals of Operations Research, 1(2), 165-167. + * **LevyES**: Zhang, S., & Salari, E. (2005). Competitive learning vector quantization with evolution strategies for image compression. Optical Engineering, 44(2), 027006. +* **EP - Evolutionary programming** . + * **OriginalEP**: Fogel, L. J. (1994). Evolutionary programming in perspective: The top-down view. Computational intelligence: Imitating life. + * **LevyEP**: Lee, C.Y. and Yao, X., 2001, May. Evolutionary algorithms with adaptive lévy mutations. In Proceedings of the 2001 congress on evolutionary computation (IEEE Cat. No. 01TH8546) (Vol. 1, pp. 568-575). IEEE. +* **EHO - Elephant Herding Optimization** . + * **OriginalEHO**: Wang, G. G., Deb, S., & Coelho, L. D. S. (2015, December). Elephant herding optimization. In 2015 3rd International Symposium on Computational and Business Intelligence (ISCBI) (pp. 1-5). IEEE. +* **EFO - Electromagnetic Field Optimization** . + * **OriginalEFO**:Abedinpourshotorban, H., Shamsuddin, S. M., Beheshti, Z., & Jawawi, D. N. (2016). Electromagnetic field optimization: A physics-inspired metaheuristic optimization algorithm. Swarm and Evolutionary Computation, 26, 8-22. + * **BaseEFO**: The developed version +* **EOA - Earthworm Optimisation Algorithm** . + * **OriginalEOA**: Wang, G. G., Deb, S., & dos Santos Coelho, L. (2018). Earthworm optimisation algorithm: a bio-inspired metaheuristic algorithm for global optimisation problems. IJBIC, 12(1), 1-22. +* **EO - Equilibrium Optimizer** . + * **OriginalEO**: Faramarzi, A., Heidarinejad, M., Stephens, B., & Mirjalili, S. (2019). Equilibrium optimizer: A novel optimization algorithm. Knowledge-Based Systems. + * **ModifiedEO**: Gupta, S., Deep, K., & Mirjalili, S. (2020). An efficient equilibrium optimizer with mutation strategy for numerical optimization. Applied Soft Computing, 96, 106542. + * **AdaptiveEO**: Wunnava, A., Naik, M. K., Panda, R., Jena, B., & Abraham, A. (2020). A novel interdependence based multilevel thresholding technique using adaptive equilibrium optimizer. Engineering Applications of Artificial Intelligence, 94, 103836. +### F +* **FFA - Firefly Algorithm** + * **OriginalFFA**: Łukasik, S., & Żak, S. (2009, October). Firefly algorithm for continuous constrained optimization tasks. In International conference on computational collective intelligence (pp. 97-106). Springer, Berlin, Heidelberg. +* **FA - Fireworks algorithm** + * **OriginalFA**: Tan, Y., & Zhu, Y. (2010, June). Fireworks algorithm for optimization. In International conference in swarm intelligence (pp. 355-364). Springer, Berlin, Heidelberg. +* **FPA - Flower Pollination Algorithm** + * **OriginalFPA**: Yang, X. S. (2012, September). Flower pollination algorithm for global optimization. In International conference on unconventional computing and natural computation (pp. 240-249). Springer, Berlin, Heidelberg. +* **FOA - Fruit-fly Optimization Algorithm** + * **OriginalFOA**: Pan, W. T. (2012). A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowledge-Based Systems, 26, 69-74. + * **BaseFOA**: The developed version + * **WhaleFOA**: Fan, Y., Wang, P., Heidari, A. A., Wang, M., Zhao, X., Chen, H., & Li, C. (2020). Boosted hunting-based fruit fly optimization and advances in real-world problems. Expert Systems with Applications, 159, 113502. +* **FBIO - Forensic-Based Investigation Optimization** + * **OriginalFBIO**: Chou, J.S. and Nguyen, N.M., 2020. FBI inspired meta-optimization. Applied Soft Computing, p.106339. + * **BaseFBIO**: Fathy, A., Rezk, H. and Alanazi, T.M., 2021. Recent approach of forensic-based investigation algorithm for optimizing fractional order PID-based MPPT with proton exchange membrane fuel cell.IEEE Access,9, pp.18974-18992. +* **FHO - Fire Hawk Optimization** + * **OriginalFHO**: Azizi, M., Talatahari, S., & Gandomi, A. H. (2022). Fire Hawk Optimizer: a novel metaheuristic algorithm. Artificial Intelligence Review, 1-77. +### G +* **GA - Genetic Algorithm** + * **BaseGA**: Holland, J. H. (1992). Genetic algorithms. Scientific american, 267(1), 66-73. + * **SingleGA**: De Falco, I., Della Cioppa, A. and Tarantino, E., 2002. Mutation-based genetic algorithm: performance evaluation. Applied Soft Computing, 1(4), pp.285-299. + * **MultiGA**: De Jong, K.A. and Spears, W.M., 1992. A formal analysis of the role of multi-point crossover in genetic algorithms. Annals of mathematics and Artificial intelligence, 5(1), pp.1-26. + * **EliteSingleGA**: Elite version of Single-point mutation GA + * **EliteMultiGA**: Elite version of Multiple-point mutation GA +* **GWO - Grey Wolf Optimizer** + * **OriginalGWO**: Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in engineering software, 69, 46-61. + * **RW_GWO**: Gupta, S., & Deep, K. (2019). A novel random walk grey wolf optimizer. Swarm and evolutionary computation, 44, 101-112. + * **GWO_WOA**: Obadina, O. O., Thaha, M. A., Althoefer, K., & Shaheed, M. H. (2022). Dynamic characterization of a master–slave robotic manipulator using a hybrid grey wolf–whale optimization algorithm. Journal of Vibration and Control, 28(15-16), 1992-2003. +* **GOA - Grasshopper Optimisation Algorithm** + * **OriginalGOA**: Saremi, S., Mirjalili, S., & Lewis, A. (2017). Grasshopper optimisation algorithm: theory and application. Advances in Engineering Software, 105, 30-47. +* **GCO - Germinal Center Optimization** + * **OriginalGCO**: Villaseñor, C., Arana-Daniel, N., Alanis, A. Y., López-Franco, C., & Hernandez-Vargas, E. A. (2018). Germinal center optimization algorithm. International Journal of Computational Intelligence Systems, 12(1), 13-27. + * **BaseGCO**: The developed version +* **GSKA - Gaining Sharing Knowledge-based Algorithm** + * **OriginalGSKA**: Mohamed, A. W., Hadi, A. A., & Mohamed, A. K. (2019). Gaining-sharing knowledge based algorithm for solving optimization problems: a novel nature-inspired algorithm. International Journal of Machine Learning and Cybernetics, 1-29. + * **BaseGSKA**: Mohamed, A.W., Hadi, A.A., Mohamed, A.K. and Awad, N.H., 2020, July. Evaluating the performance of adaptive GainingSharing knowledge based algorithm on CEC 2020 benchmark problems. In 2020 IEEE Congress on Evolutionary Computation (CEC) (pp. 1-8). IEEE. +* **GBO - Gradient-Based Optimizer** + * **OriginalGBO**: Ahmadianfar, I., Bozorg-Haddad, O., & Chu, X. (2020). Gradient-based optimizer: A new metaheuristic optimization algorithm. Information Sciences, 540, 131-159. +### H +* **HC - Hill Climbing** . + * **OriginalHC**: Talbi, E. G., & Muntean, T. (1993, January). Hill-climbing, simulated annealing and genetic algorithms: a comparative study and application to the mapping problem. In [1993] Proceedings of the Twenty-sixth Hawaii International Conference on System Sciences (Vol. 2, pp. 565-573). IEEE. + * **SwarmHC**: The developed version based on swarm-based idea (Original is single-solution based method) +* **HS - Harmony Search** . + * **OriginalHS**: Geem, Z. W., Kim, J. H., & Loganathan, G. V. (2001). A new heuristic optimization algorithm:harmony search. simulation, 76(2), 60-68. + * **BaseHS**: The developed version +* **HHO - Harris Hawks Optimization** . + * **OriginalHHO**: Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems, 97, 849-872. +* **HGSO - Henry Gas Solubility Optimization** . + * **OriginalHGSO**: Hashim, F. A., Houssein, E. H., Mabrouk, M. S., Al-Atabany, W., & Mirjalili, S. (2019). Henry gas solubility optimization: A novel physics-based algorithm. Future Generation Computer Systems, 101, 646-667. +* **HGS - Hunger Games Search** . + * **OriginalHGS**: Yang, Y., Chen, H., Heidari, A. A., & Gandomi, A. H. (2021). Hunger games search:Visions, conception, implementation, deep analysis, perspectives, and towards performance shifts. Expert Systems with Applications, 177, 114864. +* **HHOA - Horse Herd Optimization Algorithm (not done)** . + * **BaseHHOA**: MiarNaeimi, F., Azizyan, G., & Rashki, M. (2021). Horse herd optimization algorithm: A nature-inspired algorithm for high-dimensional optimization problems. Knowledge-Based Systems, 213, 106711. +* **HBA - Honey Badger Algorithm**: + * **OriginalHBA**: Hashim, F. A., Houssein, E. H., Hussain, K., Mabrouk, M. S., & Al-Atabany, W. (2022). Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems. Mathematics and Computers in Simulation, 192, 84-110. +### I +* **IWO - Invasive Weed Optimization** . + * **OriginalIWO**: Mehrabian, A. R., & Lucas, C. (2006). A novel numerical optimization algorithm inspired from weed colonization. Ecological informatics, 1(4), 355-366. +* **ICA - Imperialist Competitive Algorithm** + * **OriginalICA**: Atashpaz-Gargari, E., & Lucas, C. (2007, September). Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In 2007 IEEE congress on evolutionary computation (pp. 4661-4667). Ieee. +* **INFO - weIghted meaN oF vectOrs**: + * **OriginalINFO**: Ahmadianfar, I., Heidari, A. A., Gandomi, A. H., Chu, X., & Chen, H. (2021). RUN beyond the metaphor: An efficient optimization algorithm based on Runge Kutta method. Expert Systems with Applications, 181, 115079. +### J +* **JA - Jaya Algorithm** + * **OriginalJA**: Rao, R. (2016). Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. International Journal of Industrial Engineering Computations, 7(1), 19-34. + * **BaseJA**: The developed version + * **LevyJA**: Iacca, G., dos Santos Junior, V. C., & de Melo, V. V. (2021). An improved Jaya optimization algorithm with Levy flight. Expert Systems with Applications, 165, 113902. +### K +### L +* **LCO - Life Choice-based Optimization** + * **OriginalLCO**: Khatri, A., Gaba, A., Rana, K. P. S., & Kumar, V. (2019). A novel life choice-based optimizer. Soft Computing, 1-21. + * **BaseLCO**: The developed version + * **ImprovedLCO**: The improved version using Gaussian distribution and Mutation Mechanism +### M +* **MA - Memetic Algorithm** + * **OriginalMA**: Moscato, P. (1989). On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms. Caltech concurrent computation program, C3P Report, 826, 1989. +* **MFO - Moth Flame Optimization** + * **OriginalMFO**: Mirjalili, S. (2015). Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-based systems, 89, 228-249. + * **BaseMFO**: The developed version +* **MVO - Multi-Verse Optimizer** + * **OriginalMVO**: Mirjalili, S., Mirjalili, S. M., & Hatamlou, A. (2016). Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Computing and Applications, 27(2), 495-513. + * **BaseMVO**: The developed version +* **MSA - Moth Search Algorithm** + * **OriginalMSA**: Wang, G. G. (2018). Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems. Memetic Computing, 10(2), 151-164. +* **MRFO - Manta Ray Foraging Optimization** + * **OriginalMRFO**: Zhao, W., Zhang, Z., & Wang, L. (2020). Manta ray foraging optimization: An effective bio-inspired optimizer for engineering applications. Engineering Applications of Artificial Intelligence, 87, 103300. +* **MPA - Marine Predators Algorithm**: + * **OriginalMPA**: Faramarzi, A., Heidarinejad, M., Mirjalili, S., & Gandomi, A. H. (2020). Marine Predators Algorithm: A nature-inspired metaheuristic. Expert systems with applications, 152, 113377. +### N +* **NRO - Nuclear Reaction Optimization** + * **OriginalNRO**: Wei, Z., Huang, C., Wang, X., Han, T., & Li, Y. (2019). Nuclear Reaction Optimization: A novel and powerful physics-based algorithm for global optimization. IEEE Access. +* **NMRA - Nake Mole-Rat Algorithm** + * **OriginalNMRA**: Salgotra, R., & Singh, U. (2019). The naked mole-rat algorithm. Neural Computing and Applications, 31(12), 8837-8857. + * **ImprovedNMRA**: Singh, P., Mittal, N., Singh, U. and Salgotra, R., 2021. Naked mole-rat algorithm with improved exploration and exploitation capabilities to determine 2D and 3D coordinates of sensor nodes in WSNs. Arabian Journal for Science and Engineering, 46(2), pp.1155-1178. +### O +### P +* **PSO - Particle Swarm Optimization** + * **OriginalPSO**: Eberhart, R., & Kennedy, J. (1995, October). A new optimizer using particle swarm theory. In MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science (pp. 39-43). Ieee. + * **PPSO**: Ghasemi, M., Akbari, E., Rahimnejad, A., Razavi, S. E., Ghavidel, S., & Li, L. (2019). Phasor particle swarm optimization: a simple and efficient variant of PSO. Soft Computing, 23(19), 9701-9718. + * **HPSO_TVAC**: Ghasemi, M., Aghaei, J., & Hadipour, M. (2017). New self-organising hierarchical PSO with jumping time-varying acceleration coefficients. Electronics Letters, 53(20), 1360-1362. + * **C_PSO**: Liu, B., Wang, L., Jin, Y. H., Tang, F., & Huang, D. X. (2005). Improved particle swarm optimization combined with chaos. Chaos, Solitons & Fractals, 25(5), 1261-1271. + * **CL_PSO**: Liang, J. J., Qin, A. K., Suganthan, P. N., & Baskar, S. (2006). Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE transactions on evolutionary computation, 10(3), 281-295. +* **PFA - Pathfinder Algorithm** + * **OriginalPFA**: Yapici, H., & Cetinkaya, N. (2019). A new meta-heuristic optimizer: Pathfinder algorithm. Applied Soft Computing, 78, 545-568. +* **PSS - Pareto-like Sequential Sampling** + * **OriginalPSS**: Shaqfa, M., & Beyer, K. (2021). Pareto-like sequential sampling heuristic for global optimisation. Soft Computing, 25(14), 9077-9096. +### Q +* **QSA - Queuing Search Algorithm** + * **OriginalQSA**: Zhang, J., Xiao, M., Gao, L., & Pan, Q. (2018). Queuing search algorithm: A novel metaheuristic algorithm for solving engineering optimization problems. Applied Mathematical Modelling, 63, 464-490. + * **BaseQSA**: The developed version + * **OppoQSA**: Zheng, X. and Nguyen, H., 2022. A novel artificial intelligent model for predicting water treatment efficiency of various biochar systems based on artificial neural network and queuing search algorithm. Chemosphere, 287, p.132251. + * **LevyQSA**: Abderazek, H., Hamza, F., Yildiz, A.R., Gao, L. and Sait, S.M., 2021. A comparative analysis of the queuing search algorithm, the sine-cosine algorithm, the ant lion algorithm to determine the optimal weight design problem of a spur gear drive system. Materials Testing, 63(5), pp.442-447. + * **ImprovedQSA**: Nguyen, B.M., Hoang, B., Nguyen, T. and Nguyen, G., 2021. nQSV-Net: a novel queuing search variant for global space search and workload modeling. Journal of Ambient Intelligence and Humanized Computing, 12(1), pp.27-46. +### R +* **RUN - RUNge Kutta optimizer**: + * **OriginalRUN**: Ahmadianfar, I., Heidari, A. A., Gandomi, A. H., Chu, X., & Chen, H. (2021). RUN beyond the metaphor: An efficient optimization algorithm based on Runge Kutta method. Expert Systems with Applications, 181, 115079. +### S +* **SA - Simulated Annealling** + * **OriginalSA**: . Van Laarhoven, P. J., & Aarts, E. H. (1987). Simulated annealing. In Simulated annealing: Theory and applications (pp. 7-15). Springer, Dordrecht. +* **SSpiderO - Social Spider Optimization** + * **OriginalSSpiderO**: Cuevas, E., Cienfuegos, M., ZaldíVar, D., & Pérez-Cisneros, M. (2013). A swarm optimization algorithm inspired in the behavior of the social-spider. Expert Systems with Applications, 40(16), 6374-6384. +* **SOS - Symbiotic Organisms Search**: + * **OriginalSOS**: Cheng, M. Y., & Prayogo, D. (2014). Symbiotic organisms search: a new metaheuristic optimization algorithm. Computers & Structures, 139, 98-112. +* **SSpiderA - Social Spider Algorithm** + * **OriginalSSpiderA**: James, J. Q., & Li, V. O. (2015). A social spider algorithm for global optimization. Applied Soft Computing, 30, 614-627. +* **SCA - Sine Cosine Algorithm** + * **OriginalSCA**: Mirjalili, S. (2016). SCA: a sine cosine algorithm for solving optimization problems. Knowledge-Based Systems, 96, 120-133. + * **BaseSCA**: Attia, A.F., El Sehiemy, R.A. and Hasanien, H.M., 2018. Optimal power flow solution in power systems using a novel Sine-Cosine algorithm. International Journal of Electrical Power & Energy Systems, 99, pp.331-343. +* **SRSR - Swarm Robotics Search And Rescue** + * **OriginalSRSR**: Bakhshipour, M., Ghadi, M. J., & Namdari, F. (2017). Swarm robotics search & rescue: A novel artificial intelligence-inspired optimization approach. Applied Soft Computing, 57, 708-726. +* **SBO - Satin Bowerbird Optimizer** + * **OriginalSBO**: Moosavi, S. H. S., & Bardsiri, V. K. (2017). Satin bowerbird optimizer: a new optimization algorithm to optimize ANFIS for software development effort estimation. Engineering Applications of Artificial Intelligence, 60, 1-15. + * **BaseSBO**: The developed version +* **SHO - Spotted Hyena Optimizer** + * **OriginalSHO**: Dhiman, G., & Kumar, V. (2017). Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications. Advances in Engineering Software, 114, 48-70. +* **SSO - Salp Swarm Optimization** + * **OriginalSSO**: Mirjalili, S., Gandomi, A. H., Mirjalili, S. Z., Saremi, S., Faris, H., & Mirjalili, S. M. (2017). Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Software, 114, 163-191. +* **SFO - Sailfish Optimizer** + * **OriginalSFO**: Shadravan, S., Naji, H. R., & Bardsiri, V. K. (2019). The Sailfish Optimizer: A novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems. Engineering Applications of Artificial Intelligence, 80, 20-34. + * **ImprovedSFO**: Li, L.L., Shen, Q., Tseng, M.L. and Luo, S., 2021. Power system hybrid dynamic economic emission dispatch with wind energy based on improved sailfish algorithm. Journal of Cleaner Production, 316, p.128318. +* **SARO - Search And Rescue Optimization** + * **OriginalSARO**: Shabani, A., Asgarian, B., Gharebaghi, S. A., Salido, M. A., & Giret, A. (2019). A New Optimization Algorithm Based on Search and Rescue Operations. Mathematical Problems in Engineering, 2019. + * **BaseSARO**: The developed version using Levy-flight +* **SSDO - Social Ski-Driver Optimization** + * **OriginalSSDO**: Tharwat, A., & Gabel, T. (2019). Parameters optimization of support vector machines for imbalanced data using social ski driver algorithm. Neural Computing and Applications, 1-14. +* **SLO - Sea Lion Optimization** + * **OriginalSLO**: Masadeh, R., Mahafzah, B. A., & Sharieh, A. (2019). Sea Lion Optimization Algorithm. Sea, 10(5). + * **ImprovedSLO**: The developed version + * **ModifiedSLO**: Masadeh, R., Alsharman, N., Sharieh, A., Mahafzah, B.A. and Abdulrahman, A., 2021. Task scheduling on cloud computing based on sea lion optimization algorithm. International Journal of Web Information Systems. +* **Seagull Optimization Algorithm** + * **OriginalSOA**: Dhiman, G., & Kumar, V. (2019). Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems. Knowledge-based systems, 165, 169-196. + * **DevSOA**: The developed version +* **SMA - Slime Mould Algorithm** + * **OriginalSMA**: Li, S., Chen, H., Wang, M., Heidari, A. A., & Mirjalili, S. (2020). Slime mould algorithm: A new method for stochastic optimization. Future Generation Computer Systems. + * **BaseSMA**: The developed version +* **SSA - Sparrow Search Algorithm** + * **OriginalSSA**: Jiankai Xue & Bo Shen (2020) A novel swarm intelligence optimization approach: sparrow search algorithm, Systems Science & Control Engineering, 8:1, 22-34, DOI: 10.1080/21642583.2019.1708830 + * **BaseSSA**: The developed version +* **SPBO - Student Psychology Based Optimization** + * **OriginalSPBO**: Das, B., Mukherjee, V., & Das, D. (2020). Student psychology based optimization algorithm: A new population based optimization algorithm for solving optimization problems. Advances in Engineering software, 146, 102804. + * **DevSPBO**: The developed version +* **SCSO - Sand Cat Swarm Optimization** + * **OriginalSCSO**: Seyyedabbasi, A., & Kiani, F. (2022). Sand Cat swarm optimization: a nature-inspired algorithm to solve global optimization problems. Engineering with Computers, 1-25. +### T +* **TLO - Teaching Learning Optimization** + * **OriginalTLO**: Rao, R. V., Savsani, V. J., & Vakharia, D. P. (2011). Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-Aided Design, 43(3), 303-315. + * **BaseTLO**: Rao, R., & Patel, V. (2012). An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems. International Journal of Industrial Engineering Computations, 3(4), 535-560. + * **ImprovedTLO**: Rao, R. V., & Patel, V. (2013). An improved teaching-learning-based optimization algorithm for solving unconstrained optimization problems. Scientia Iranica, 20(3), 710-720. +* **TWO - Tug of War Optimization** + * **OriginalTWO**: Kaveh, A., & Zolghadr, A. (2016). A novel meta-heuristic algorithm: tug of war optimization. Iran University of Science & Technology, 6(4), 469-492. + * **OppoTWO**: Kaveh, A., Almasi, P. and Khodagholi, A., 2022. Optimum Design of Castellated Beams Using Four Recently Developed Meta-heuristic Algorithms. Iranian Journal of Science and Technology, Transactions of Civil Engineering, pp.1-13. + * **LevyTWO**: The developed version using Levy-flight + * **ImprovedTWO**: Nguyen, T., Hoang, B., Nguyen, G., & Nguyen, B. M. (2020). A new workload prediction model using extreme learning machine and enhanced tug of war optimization. Procedia Computer Science, 170, 362-369. +* **TSA - Tunicate Swarm Algorithm** + * **OriginalTSA**: Kaur, S., Awasthi, L. K., Sangal, A. L., & Dhiman, G. (2020). Tunicate Swarm Algorithm: A new bio-inspired based metaheuristic paradigm for global optimization. Engineering Applications of Artificial Intelligence, 90, 103541. +* **TSO - Tuna Swarm Optimization** + * **OriginalTSO**: Xie, L., Han, T., Zhou, H., Zhang, Z. R., Han, B., & Tang, A. (2021). Tuna swarm optimization: a novel swarm-based metaheuristic algorithm for global optimization. Computational intelligence and Neuroscience, 2021. +### U +### V +* **VCS - Virus Colony Search** + * **OriginalVCS**: Li, M. D., Zhao, H., Weng, X. W., & Han, T. (2016). A novel nature-inspired algorithm for optimization: Virus colony search. Advances in Engineering Software, 92, 65-88. + * **BaseVCS**: The developed version +### W +* **WCA - Water Cycle Algorithm** + * **OriginalWCA**: Eskandar, H., Sadollah, A., Bahreininejad, A., & Hamdi, M. (2012). Water cycle algorithm–A novel metaheuristic optimization method for solving constrained engineering optimization problems. Computers & Structures, 110, 151-166. +* **WOA - Whale Optimization Algorithm** + * **OriginalWOA**: Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in engineering software, 95, 51-67. + * **HI_WOA**: Tang, C., Sun, W., Wu, W., & Xue, M. (2019, July). A hybrid improved whale optimization algorithm. In 2019 IEEE 15th International Conference on Control and Automation (ICCA) (pp. 362-367). IEEE. +* **WHO - Wildebeest Herd Optimization** + * **OriginalWHO**: Amali, D., & Dinakaran, M. (2019). Wildebeest herd optimization: A new global optimization algorithm inspired by wildebeest herding behaviour. Journal of Intelligent & Fuzzy Systems, (Preprint), 1-14. +* **WDO - Wind Driven Optimization** + * **OriginalWDO**: Bayraktar, Z., Komurcu, M., Bossard, J.A. and Werner, D.H., 2013. The wind driven optimization technique and its application in electromagnetics. IEEE transactions on antennas and propagation, 61(5), pp.2745-2757. +### X +### Y +### Z + +%prep +%autosetup -n mealpy-2.5.3 + +%build +%py3_build + +%install +%py3_install +install -d -m755 %{buildroot}/%{_pkgdocdir} +if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi +if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi +if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi +if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi +pushd %{buildroot} +if [ -d usr/lib ]; then + find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/lib64 ]; then + find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/bin ]; then + find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/sbin ]; then + find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst +fi +touch doclist.lst +if [ -d usr/share/man ]; then + find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst +fi +popd +mv %{buildroot}/filelist.lst . +mv %{buildroot}/doclist.lst . + +%files -n python3-mealpy -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Wed May 31 2023 Python_Bot <Python_Bot@openeuler.org> - 2.5.3-1 +- Package Spec generated @@ -0,0 +1 @@ +c81d5e84a3ddae59defa99f26083ad06 mealpy-2.5.3.tar.gz |