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@@ -0,0 +1 @@ +/scikit-opt-0.6.6.tar.gz diff --git a/python-scikit-opt.spec b/python-scikit-opt.spec new file mode 100644 index 0000000..01db46a --- /dev/null +++ b/python-scikit-opt.spec @@ -0,0 +1,1556 @@ +%global _empty_manifest_terminate_build 0 +Name: python-scikit-opt +Version: 0.6.6 +Release: 1 +Summary: Swarm Intelligence in Python +License: MIT +URL: https://github.com/guofei9987/scikit-opt +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/59/63/eb0c1c56d7de607cdf93f3ba96e8c631f2da2e734ec32fd7a667fb8594a9/scikit-opt-0.6.6.tar.gz +BuildArch: noarch + +Requires: python3-numpy +Requires: python3-scipy + +%description + + +# [scikit-opt](https://github.com/guofei9987/scikit-opt) + +[](https://pypi.org/project/scikit-opt/) +[](https://travis-ci.com/guofei9987/scikit-opt) +[](https://codecov.io/gh/guofei9987/scikit-opt) +[](https://github.com/guofei9987/scikit-opt/blob/master/LICENSE) + + +[](https://github.com/guofei9987/scikit-opt/fork) +[](https://pepy.tech/project/scikit-opt) +[](https://github.com/guofei9987/scikit-opt/discussions) + + + + +Swarm Intelligence in Python +(Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Algorithm, Immune Algorithm,Artificial Fish Swarm Algorithm in Python) + + +- **Documentation:** [https://scikit-opt.github.io/scikit-opt/#/en/](https://scikit-opt.github.io/scikit-opt/#/en/) +- **文档：** [https://scikit-opt.github.io/scikit-opt/#/zh/](https://scikit-opt.github.io/scikit-opt/#/zh/) +- **Source code:** [https://github.com/guofei9987/scikit-opt](https://github.com/guofei9987/scikit-opt) +- **Help us improve scikit-opt** [https://www.wjx.cn/jq/50964691.aspx](https://www.wjx.cn/jq/50964691.aspx) + +# install +```bash +pip install scikit-opt +``` + +For the current developer version: +```bach +git clone git@github.com:guofei9987/scikit-opt.git +cd scikit-opt +pip install . +``` + +# Features +## Feature1: UDF + +**UDF** (user defined function) is available now! + +For example, you just worked out a new type of `selection` function. +Now, your `selection` function is like this: +-> Demo code: [examples/demo_ga_udf.py#s1](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_udf.py#L1) +```python +# step1: define your own operator: +def selection_tournament(algorithm, tourn_size): + FitV = algorithm.FitV + sel_index = [] + for i in range(algorithm.size_pop): + aspirants_index = np.random.choice(range(algorithm.size_pop), size=tourn_size) + sel_index.append(max(aspirants_index, key=lambda i: FitV[i])) + algorithm.Chrom = algorithm.Chrom[sel_index, :] # next generation + return algorithm.Chrom + + +``` + +Import and build ga +-> Demo code: [examples/demo_ga_udf.py#s2](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_udf.py#L12) +```python +import numpy as np +from sko.GA import GA, GA_TSP + +demo_func = lambda x: x[0] ** 2 + (x[1] - 0.05) ** 2 + (x[2] - 0.5) ** 2 +ga = GA(func=demo_func, n_dim=3, size_pop=100, max_iter=500, prob_mut=0.001, + lb=[-1, -10, -5], ub=[2, 10, 2], precision=[1e-7, 1e-7, 1]) + +``` +Regist your udf to GA +-> Demo code: [examples/demo_ga_udf.py#s3](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_udf.py#L20) +```python +ga.register(operator_name='selection', operator=selection_tournament, tourn_size=3) +``` + +scikit-opt also provide some operators +-> Demo code: [examples/demo_ga_udf.py#s4](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_udf.py#L22) +```python +from sko.operators import ranking, selection, crossover, mutation + +ga.register(operator_name='ranking', operator=ranking.ranking). \ + register(operator_name='crossover', operator=crossover.crossover_2point). \ + register(operator_name='mutation', operator=mutation.mutation) +``` +Now do GA as usual +-> Demo code: [examples/demo_ga_udf.py#s5](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_udf.py#L28) +```python +best_x, best_y = ga.run() +print('best_x:', best_x, '\n', 'best_y:', best_y) +``` + +> Until Now, the **udf** surport `crossover`, `mutation`, `selection`, `ranking` of GA +> scikit-opt provide a dozen of operators, see [here](https://github.com/guofei9987/scikit-opt/tree/master/sko/operators) + +For advanced users: + +-> Demo code: [examples/demo_ga_udf.py#s6](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_udf.py#L31) +```python +class MyGA(GA): + def selection(self, tourn_size=3): + FitV = self.FitV + sel_index = [] + for i in range(self.size_pop): + aspirants_index = np.random.choice(range(self.size_pop), size=tourn_size) + sel_index.append(max(aspirants_index, key=lambda i: FitV[i])) + self.Chrom = self.Chrom[sel_index, :] # next generation + return self.Chrom + + ranking = ranking.ranking + + +demo_func = lambda x: x[0] ** 2 + (x[1] - 0.05) ** 2 + (x[2] - 0.5) ** 2 +my_ga = MyGA(func=demo_func, n_dim=3, size_pop=100, max_iter=500, lb=[-1, -10, -5], ub=[2, 10, 2], + precision=[1e-7, 1e-7, 1]) +best_x, best_y = my_ga.run() +print('best_x:', best_x, '\n', 'best_y:', best_y) +``` + +## feature2: continue to run +(New in version 0.3.6) +Run an algorithm for 10 iterations, and then run another 20 iterations base on the 10 iterations before: +```python +from sko.GA import GA + +func = lambda x: x[0] ** 2 +ga = GA(func=func, n_dim=1) +ga.run(10) +ga.run(20) +``` + +## feature3: 4-ways to accelerate +- vectorization +- multithreading +- multiprocessing +- cached + +see [https://github.com/guofei9987/scikit-opt/blob/master/examples/example_function_modes.py](https://github.com/guofei9987/scikit-opt/blob/master/examples/example_function_modes.py) + + + +## feature4: GPU computation + We are developing GPU computation, which will be stable on version 1.0.0 +An example is already available: [https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_gpu.py](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_gpu.py) + + +# Quick start + +## 1. Differential Evolution +**Step1**：define your problem +-> Demo code: [examples/demo_de.py#s1](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_de.py#L1) +```python +''' +min f(x1, x2, x3) = x1^2 + x2^2 + x3^2 +s.t. + x1*x2 >= 1 + x1*x2 <= 5 + x2 + x3 = 1 + 0 <= x1, x2, x3 <= 5 +''' + + +def obj_func(p): + x1, x2, x3 = p + return x1 ** 2 + x2 ** 2 + x3 ** 2 + + +constraint_eq = [ + lambda x: 1 - x[1] - x[2] +] + +constraint_ueq = [ + lambda x: 1 - x[0] * x[1], + lambda x: x[0] * x[1] - 5 +] + +``` + +**Step2**: do Differential Evolution +-> Demo code: [examples/demo_de.py#s2](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_de.py#L25) +```python +from sko.DE import DE + +de = DE(func=obj_func, n_dim=3, size_pop=50, max_iter=800, lb=[0, 0, 0], ub=[5, 5, 5], + constraint_eq=constraint_eq, constraint_ueq=constraint_ueq) + +best_x, best_y = de.run() +print('best_x:', best_x, '\n', 'best_y:', best_y) + +``` + +## 2. Genetic Algorithm + +**Step1**：define your problem +-> Demo code: [examples/demo_ga.py#s1](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga.py#L1) +```python +import numpy as np + + +def schaffer(p): + ''' + This function has plenty of local minimum, with strong shocks + global minimum at (0,0) with value 0 + https://en.wikipedia.org/wiki/Test_functions_for_optimization + ''' + x1, x2 = p + part1 = np.square(x1) - np.square(x2) + part2 = np.square(x1) + np.square(x2) + return 0.5 + (np.square(np.sin(part1)) - 0.5) / np.square(1 + 0.001 * part2) + + +``` + +**Step2**: do Genetic Algorithm +-> Demo code: [examples/demo_ga.py#s2](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga.py#L16) +```python +from sko.GA import GA + +ga = GA(func=schaffer, n_dim=2, size_pop=50, max_iter=800, prob_mut=0.001, lb=[-1, -1], ub=[1, 1], precision=1e-7) +best_x, best_y = ga.run() +print('best_x:', best_x, '\n', 'best_y:', best_y) +``` + +-> Demo code: [examples/demo_ga.py#s3](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga.py#L22) +```python +import pandas as pd +import matplotlib.pyplot as plt + +Y_history = pd.DataFrame(ga.all_history_Y) +fig, ax = plt.subplots(2, 1) +ax[0].plot(Y_history.index, Y_history.values, '.', color='red') +Y_history.min(axis=1).cummin().plot(kind='line') +plt.show() +``` + + + +### 2.2 Genetic Algorithm for TSP(Travelling Salesman Problem) +Just import the `GA_TSP`, it overloads the `crossover`, `mutation` to solve the TSP + +**Step1**: define your problem. Prepare your points coordinate and the distance matrix. +Here I generate the data randomly as a demo: +-> Demo code: [examples/demo_ga_tsp.py#s1](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_tsp.py#L1) +```python +import numpy as np +from scipy import spatial +import matplotlib.pyplot as plt + +num_points = 50 + +points_coordinate = np.random.rand(num_points, 2) # generate coordinate of points +distance_matrix = spatial.distance.cdist(points_coordinate, points_coordinate, metric='euclidean') + + +def cal_total_distance(routine): + '''The objective function. input routine, return total distance. + cal_total_distance(np.arange(num_points)) + ''' + num_points, = routine.shape + return sum([distance_matrix[routine[i % num_points], routine[(i + 1) % num_points]] for i in range(num_points)]) + + +``` + +**Step2**: do GA +-> Demo code: [examples/demo_ga_tsp.py#s2](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_tsp.py#L19) +```python + +from sko.GA import GA_TSP + +ga_tsp = GA_TSP(func=cal_total_distance, n_dim=num_points, size_pop=50, max_iter=500, prob_mut=1) +best_points, best_distance = ga_tsp.run() + +``` + +**Step3**: Plot the result: +-> Demo code: [examples/demo_ga_tsp.py#s3](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_tsp.py#L26) +```python +fig, ax = plt.subplots(1, 2) +best_points_ = np.concatenate([best_points, [best_points[0]]]) +best_points_coordinate = points_coordinate[best_points_, :] +ax[0].plot(best_points_coordinate[:, 0], best_points_coordinate[:, 1], 'o-r') +ax[1].plot(ga_tsp.generation_best_Y) +plt.show() +``` + + + + +## 3. PSO(Particle swarm optimization) + +### 3.1 PSO +**Step1**: define your problem: +-> Demo code: [examples/demo_pso.py#s1](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_pso.py#L1) +```python +def demo_func(x): + x1, x2, x3 = x + return x1 ** 2 + (x2 - 0.05) ** 2 + x3 ** 2 + + +``` + +**Step2**: do PSO +-> Demo code: [examples/demo_pso.py#s2](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_pso.py#L6) +```python +from sko.PSO import PSO + +pso = PSO(func=demo_func, n_dim=3, pop=40, max_iter=150, lb=[0, -1, 0.5], ub=[1, 1, 1], w=0.8, c1=0.5, c2=0.5) +pso.run() +print('best_x is ', pso.gbest_x, 'best_y is', pso.gbest_y) + +``` + +**Step3**: Plot the result +-> Demo code: [examples/demo_pso.py#s3](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_pso.py#L13) +```python +import matplotlib.pyplot as plt + +plt.plot(pso.gbest_y_hist) +plt.show() +``` + + + + +### 3.2 PSO with nonlinear constraint + +If you need nolinear constraint like `(x[0] - 1) ** 2 + (x[1] - 0) ** 2 - 0.5 ** 2<=0` +Codes are like this: +```python +constraint_ueq = ( + lambda x: (x[0] - 1) ** 2 + (x[1] - 0) ** 2 - 0.5 ** 2 + , +) +pso = PSO(func=demo_func, n_dim=2, pop=40, max_iter=max_iter, lb=[-2, -2], ub=[2, 2] + , constraint_ueq=constraint_ueq) +``` + +Note that, you can add more then one nonlinear constraint. Just add it to `constraint_ueq` + +More over, we have an animation: + +↑**see [examples/demo_pso_ani.py](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_pso_ani.py)** + + +## 4. SA(Simulated Annealing) +### 4.1 SA for multiple function +**Step1**: define your problem +-> Demo code: [examples/demo_sa.py#s1](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_sa.py#L1) +```python +demo_func = lambda x: x[0] ** 2 + (x[1] - 0.05) ** 2 + x[2] ** 2 + +``` +**Step2**: do SA +-> Demo code: [examples/demo_sa.py#s2](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_sa.py#L3) +```python +from sko.SA import SA + +sa = SA(func=demo_func, x0=[1, 1, 1], T_max=1, T_min=1e-9, L=300, max_stay_counter=150) +best_x, best_y = sa.run() +print('best_x:', best_x, 'best_y', best_y) + +``` + +**Step3**: Plot the result +-> Demo code: [examples/demo_sa.py#s3](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_sa.py#L10) +```python +import matplotlib.pyplot as plt +import pandas as pd + +plt.plot(pd.DataFrame(sa.best_y_history).cummin(axis=0)) +plt.show() + +``` + + + +Moreover, scikit-opt provide 3 types of Simulated Annealing: Fast, Boltzmann, Cauchy. See [more sa](https://scikit-opt.github.io/scikit-opt/#/en/more_sa) +### 4.2 SA for TSP +**Step1**: oh, yes, define your problems. To boring to copy this step. + +**Step2**: DO SA for TSP +-> Demo code: [examples/demo_sa_tsp.py#s2](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_sa_tsp.py#L21) +```python +from sko.SA import SA_TSP + +sa_tsp = SA_TSP(func=cal_total_distance, x0=range(num_points), T_max=100, T_min=1, L=10 * num_points) + +best_points, best_distance = sa_tsp.run() +print(best_points, best_distance, cal_total_distance(best_points)) +``` + +**Step3**: plot the result +-> Demo code: [examples/demo_sa_tsp.py#s3](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_sa_tsp.py#L28) +```python +from matplotlib.ticker import FormatStrFormatter + +fig, ax = plt.subplots(1, 2) + +best_points_ = np.concatenate([best_points, [best_points[0]]]) +best_points_coordinate = points_coordinate[best_points_, :] +ax[0].plot(sa_tsp.best_y_history) +ax[0].set_xlabel("Iteration") +ax[0].set_ylabel("Distance") +ax[1].plot(best_points_coordinate[:, 0], best_points_coordinate[:, 1], + marker='o', markerfacecolor='b', color='c', linestyle='-') +ax[1].xaxis.set_major_formatter(FormatStrFormatter('%.3f')) +ax[1].yaxis.set_major_formatter(FormatStrFormatter('%.3f')) +ax[1].set_xlabel("Longitude") +ax[1].set_ylabel("Latitude") +plt.show() + +``` + + + +More: Plot the animation: + + +↑**see [examples/demo_sa_tsp.py](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_sa_tsp.py)** + + + + +## 5. ACA (Ant Colony Algorithm) for tsp +-> Demo code: [examples/demo_aca_tsp.py#s2](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_aca_tsp.py#L17) +```python +from sko.ACA import ACA_TSP + +aca = ACA_TSP(func=cal_total_distance, n_dim=num_points, + size_pop=50, max_iter=200, + distance_matrix=distance_matrix) + +best_x, best_y = aca.run() + +``` + + + + +## 6. immune algorithm (IA) +-> Demo code: [examples/demo_ia.py#s2](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ia.py#L6) +```python + +from sko.IA import IA_TSP + +ia_tsp = IA_TSP(func=cal_total_distance, n_dim=num_points, size_pop=500, max_iter=800, prob_mut=0.2, + T=0.7, alpha=0.95) +best_points, best_distance = ia_tsp.run() +print('best routine:', best_points, 'best_distance:', best_distance) + +``` + + + +## 7. Artificial Fish Swarm Algorithm (AFSA) +-> Demo code: [examples/demo_afsa.py#s1](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_afsa.py#L1) +```python +def func(x): + x1, x2 = x + return 1 / x1 ** 2 + x1 ** 2 + 1 / x2 ** 2 + x2 ** 2 + + +from sko.AFSA import AFSA + +afsa = AFSA(func, n_dim=2, size_pop=50, max_iter=300, + max_try_num=100, step=0.5, visual=0.3, + q=0.98, delta=0.5) +best_x, best_y = afsa.run() +print(best_x, best_y) +``` + + +# Projects using scikit-opt + +- [Yu, J., He, Y., Yan, Q., & Kang, X. (2021). SpecView: Malware Spectrum Visualization Framework With Singular Spectrum Transformation. IEEE Transactions on Information Forensics and Security, 16, 5093-5107.](https://ieeexplore.ieee.org/abstract/document/9607026/) +- [Zhen, H., Zhai, H., Ma, W., Zhao, L., Weng, Y., Xu, Y., ... & He, X. (2021). Design and tests of reinforcement-learning-based optimal power flow solution generator. Energy Reports.](https://www.sciencedirect.com/science/article/pii/S2352484721012737) +- [Heinrich, K., Zschech, P., Janiesch, C., & Bonin, M. (2021). Process data properties matter: Introducing gated convolutional neural networks (GCNN) and key-value-predict attention networks (KVP) for next event prediction with deep learning. Decision Support Systems, 143, 113494.](https://www.sciencedirect.com/science/article/pii/S016792362100004X) +- [Tang, H. K., & Goh, S. K. (2021). A Novel Non-population-based Meta-heuristic Optimizer Inspired by the Philosophy of Yi Jing. arXiv preprint arXiv:2104.08564.](https://arxiv.org/abs/2104.08564) +- [Wu, G., Li, L., Li, X., Chen, Y., Chen, Z., Qiao, B., ... & Xia, L. (2021). Graph embedding based real-time social event matching for EBSNs recommendation. World Wide Web, 1-22.](https://link.springer.com/article/10.1007/s11280-021-00934-y) +- [Pan, X., Zhang, Z., Zhang, H., Wen, Z., Ye, W., Yang, Y., ... & Zhao, X. (2021). A fast and robust mixture gases identification and concentration detection algorithm based on attention mechanism equipped recurrent neural network with double loss function. Sensors and Actuators B: Chemical, 342, 129982.](https://www.sciencedirect.com/science/article/abs/pii/S0925400521005517) +- [Castella Balcell, M. (2021). Optimization of the station keeping system for the WindCrete floating offshore wind turbine.](https://upcommons.upc.edu/handle/2117/350262) +- [Zhai, B., Wang, Y., Wang, W., & Wu, B. (2021). Optimal Variable Speed Limit Control Strategy on Freeway Segments under Fog Conditions. arXiv preprint arXiv:2107.14406.](https://arxiv.org/abs/2107.14406) +- [Yap, X. H. (2021). Multi-label classification on locally-linear data: Application to chemical toxicity prediction.](https://etd.ohiolink.edu/apexprod/rws_olink/r/1501/10?clear=10&p10_accession_num=wright162901936395651) +- [Gebhard, L. (2021). Expansion Planning of Low-Voltage Grids Using Ant Colony Optimization Ausbauplanung von Niederspannungsnetzen mithilfe eines Ameisenalgorithmus.](https://ad-publications.cs.uni-freiburg.de/theses/Master_Lukas_Gebhard_2021.pdf) +- [Ma, X., Zhou, H., & Li, Z. (2021). Optimal Design for Interdependencies between Hydrogen and Power Systems. IEEE Transactions on Industry Applications.](https://ieeexplore.ieee.org/abstract/document/9585654) +- [de Curso, T. D. C. (2021). Estudo do modelo Johansen-Ledoit-Sornette de bolhas financeiras.](https://d1wqtxts1xzle7.cloudfront.net/67649721/TCC_Thibor_Final-with-cover-page-v2.pdf?Expires=1639140872&Signature=LDZoVsAGO0mLMlVsQjnzpLlRhLyt5wdIDmBjm1yWog5bsx6apyRE9aHuwfnFnc96uvam573wiHMeV08QlK2vhRcQS1d0buenBT5fwoRuq6PTDoMsXmpBb-lGtu9ETiMb4sBYvcQb-X3C7Hh0Ec1FoJZ040gXJPWdAli3e1TdOcGrnOaBZMgNiYX6aKFIZaaXmiQeV3418~870bH4IOQXOapIE6-23lcOL-32T~FSjsOrENoLUkcosv6UHPourKgsRufAY-C2HBUWP36iJ7CoH0jSTo1e45dVgvqNDvsHz7tmeI~0UPGH-A8MWzQ9h2ElCbCN~UNQ8ycxOa4TUKfpCw__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA) +- [Wu, T., Liu, J., Liu, J., Huang, Z., Wu, H., Zhang, C., ... & Zhang, G. (2021). A Novel AI-based Framework for AoI-optimal Trajectory Planning in UAV-assisted Wireless Sensor Networks. IEEE Transactions on Wireless Communications.](https://ieeexplore.ieee.org/abstract/document/9543607) +- [Liu, H., Wen, Z., & Cai, W. (2021, August). FastPSO: Towards Efficient Swarm Intelligence Algorithm on GPUs. In 50th International Conference on Parallel Processing (pp. 1-10).](https://dl.acm.org/doi/abs/10.1145/3472456.3472474) +- [Mahbub, R. (2020). Algorithms and Optimization Techniques for Solving TSP.](https://raiyanmahbub.com/images/Research_Paper.pdf) +- [Li, J., Chen, T., Lim, K., Chen, L., Khan, S. A., Xie, J., & Wang, X. (2019). Deep learning accelerated gold nanocluster synthesis. Advanced Intelligent Systems, 1(3), 1900029.](https://onlinelibrary.wiley.com/doi/full/10.1002/aisy.201900029) + + + + +%package -n python3-scikit-opt +Summary: Swarm Intelligence in Python +Provides: python-scikit-opt +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-scikit-opt + + +# [scikit-opt](https://github.com/guofei9987/scikit-opt) + +[](https://pypi.org/project/scikit-opt/) +[](https://travis-ci.com/guofei9987/scikit-opt) +[](https://codecov.io/gh/guofei9987/scikit-opt) +[](https://github.com/guofei9987/scikit-opt/blob/master/LICENSE) + + +[](https://github.com/guofei9987/scikit-opt/fork) +[](https://pepy.tech/project/scikit-opt) +[](https://github.com/guofei9987/scikit-opt/discussions) + + + + +Swarm Intelligence in Python +(Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Algorithm, Immune Algorithm,Artificial Fish Swarm Algorithm in Python) + + +- **Documentation:** [https://scikit-opt.github.io/scikit-opt/#/en/](https://scikit-opt.github.io/scikit-opt/#/en/) +- **文档：** [https://scikit-opt.github.io/scikit-opt/#/zh/](https://scikit-opt.github.io/scikit-opt/#/zh/) +- **Source code:** [https://github.com/guofei9987/scikit-opt](https://github.com/guofei9987/scikit-opt) +- **Help us improve scikit-opt** [https://www.wjx.cn/jq/50964691.aspx](https://www.wjx.cn/jq/50964691.aspx) + +# install +```bash +pip install scikit-opt +``` + +For the current developer version: +```bach +git clone git@github.com:guofei9987/scikit-opt.git +cd scikit-opt +pip install . +``` + +# Features +## Feature1: UDF + +**UDF** (user defined function) is available now! + +For example, you just worked out a new type of `selection` function. +Now, your `selection` function is like this: +-> Demo code: [examples/demo_ga_udf.py#s1](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_udf.py#L1) +```python +# step1: define your own operator: +def selection_tournament(algorithm, tourn_size): + FitV = algorithm.FitV + sel_index = [] + for i in range(algorithm.size_pop): + aspirants_index = np.random.choice(range(algorithm.size_pop), size=tourn_size) + sel_index.append(max(aspirants_index, key=lambda i: FitV[i])) + algorithm.Chrom = algorithm.Chrom[sel_index, :] # next generation + return algorithm.Chrom + + +``` + +Import and build ga +-> Demo code: [examples/demo_ga_udf.py#s2](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_udf.py#L12) +```python +import numpy as np +from sko.GA import GA, GA_TSP + +demo_func = lambda x: x[0] ** 2 + (x[1] - 0.05) ** 2 + (x[2] - 0.5) ** 2 +ga = GA(func=demo_func, n_dim=3, size_pop=100, max_iter=500, prob_mut=0.001, + lb=[-1, -10, -5], ub=[2, 10, 2], precision=[1e-7, 1e-7, 1]) + +``` +Regist your udf to GA +-> Demo code: [examples/demo_ga_udf.py#s3](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_udf.py#L20) +```python +ga.register(operator_name='selection', operator=selection_tournament, tourn_size=3) +``` + +scikit-opt also provide some operators +-> Demo code: [examples/demo_ga_udf.py#s4](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_udf.py#L22) +```python +from sko.operators import ranking, selection, crossover, mutation + +ga.register(operator_name='ranking', operator=ranking.ranking). \ + register(operator_name='crossover', operator=crossover.crossover_2point). \ + register(operator_name='mutation', operator=mutation.mutation) +``` +Now do GA as usual +-> Demo code: [examples/demo_ga_udf.py#s5](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_udf.py#L28) +```python +best_x, best_y = ga.run() +print('best_x:', best_x, '\n', 'best_y:', best_y) +``` + +> Until Now, the **udf** surport `crossover`, `mutation`, `selection`, `ranking` of GA +> scikit-opt provide a dozen of operators, see [here](https://github.com/guofei9987/scikit-opt/tree/master/sko/operators) + +For advanced users: + +-> Demo code: [examples/demo_ga_udf.py#s6](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_udf.py#L31) +```python +class MyGA(GA): + def selection(self, tourn_size=3): + FitV = self.FitV + sel_index = [] + for i in range(self.size_pop): + aspirants_index = np.random.choice(range(self.size_pop), size=tourn_size) + sel_index.append(max(aspirants_index, key=lambda i: FitV[i])) + self.Chrom = self.Chrom[sel_index, :] # next generation + return self.Chrom + + ranking = ranking.ranking + + +demo_func = lambda x: x[0] ** 2 + (x[1] - 0.05) ** 2 + (x[2] - 0.5) ** 2 +my_ga = MyGA(func=demo_func, n_dim=3, size_pop=100, max_iter=500, lb=[-1, -10, -5], ub=[2, 10, 2], + precision=[1e-7, 1e-7, 1]) +best_x, best_y = my_ga.run() +print('best_x:', best_x, '\n', 'best_y:', best_y) +``` + +## feature2: continue to run +(New in version 0.3.6) +Run an algorithm for 10 iterations, and then run another 20 iterations base on the 10 iterations before: +```python +from sko.GA import GA + +func = lambda x: x[0] ** 2 +ga = GA(func=func, n_dim=1) +ga.run(10) +ga.run(20) +``` + +## feature3: 4-ways to accelerate +- vectorization +- multithreading +- multiprocessing +- cached + +see [https://github.com/guofei9987/scikit-opt/blob/master/examples/example_function_modes.py](https://github.com/guofei9987/scikit-opt/blob/master/examples/example_function_modes.py) + + + +## feature4: GPU computation + We are developing GPU computation, which will be stable on version 1.0.0 +An example is already available: [https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_gpu.py](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_gpu.py) + + +# Quick start + +## 1. Differential Evolution +**Step1**：define your problem +-> Demo code: [examples/demo_de.py#s1](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_de.py#L1) +```python +''' +min f(x1, x2, x3) = x1^2 + x2^2 + x3^2 +s.t. + x1*x2 >= 1 + x1*x2 <= 5 + x2 + x3 = 1 + 0 <= x1, x2, x3 <= 5 +''' + + +def obj_func(p): + x1, x2, x3 = p + return x1 ** 2 + x2 ** 2 + x3 ** 2 + + +constraint_eq = [ + lambda x: 1 - x[1] - x[2] +] + +constraint_ueq = [ + lambda x: 1 - x[0] * x[1], + lambda x: x[0] * x[1] - 5 +] + +``` + +**Step2**: do Differential Evolution +-> Demo code: [examples/demo_de.py#s2](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_de.py#L25) +```python +from sko.DE import DE + +de = DE(func=obj_func, n_dim=3, size_pop=50, max_iter=800, lb=[0, 0, 0], ub=[5, 5, 5], + constraint_eq=constraint_eq, constraint_ueq=constraint_ueq) + +best_x, best_y = de.run() +print('best_x:', best_x, '\n', 'best_y:', best_y) + +``` + +## 2. Genetic Algorithm + +**Step1**：define your problem +-> Demo code: [examples/demo_ga.py#s1](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga.py#L1) +```python +import numpy as np + + +def schaffer(p): + ''' + This function has plenty of local minimum, with strong shocks + global minimum at (0,0) with value 0 + https://en.wikipedia.org/wiki/Test_functions_for_optimization + ''' + x1, x2 = p + part1 = np.square(x1) - np.square(x2) + part2 = np.square(x1) + np.square(x2) + return 0.5 + (np.square(np.sin(part1)) - 0.5) / np.square(1 + 0.001 * part2) + + +``` + +**Step2**: do Genetic Algorithm +-> Demo code: [examples/demo_ga.py#s2](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga.py#L16) +```python +from sko.GA import GA + +ga = GA(func=schaffer, n_dim=2, size_pop=50, max_iter=800, prob_mut=0.001, lb=[-1, -1], ub=[1, 1], precision=1e-7) +best_x, best_y = ga.run() +print('best_x:', best_x, '\n', 'best_y:', best_y) +``` + +-> Demo code: [examples/demo_ga.py#s3](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga.py#L22) +```python +import pandas as pd +import matplotlib.pyplot as plt + +Y_history = pd.DataFrame(ga.all_history_Y) +fig, ax = plt.subplots(2, 1) +ax[0].plot(Y_history.index, Y_history.values, '.', color='red') +Y_history.min(axis=1).cummin().plot(kind='line') +plt.show() +``` + + + +### 2.2 Genetic Algorithm for TSP(Travelling Salesman Problem) +Just import the `GA_TSP`, it overloads the `crossover`, `mutation` to solve the TSP + +**Step1**: define your problem. Prepare your points coordinate and the distance matrix. +Here I generate the data randomly as a demo: +-> Demo code: [examples/demo_ga_tsp.py#s1](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_tsp.py#L1) +```python +import numpy as np +from scipy import spatial +import matplotlib.pyplot as plt + +num_points = 50 + +points_coordinate = np.random.rand(num_points, 2) # generate coordinate of points +distance_matrix = spatial.distance.cdist(points_coordinate, points_coordinate, metric='euclidean') + + +def cal_total_distance(routine): + '''The objective function. input routine, return total distance. + cal_total_distance(np.arange(num_points)) + ''' + num_points, = routine.shape + return sum([distance_matrix[routine[i % num_points], routine[(i + 1) % num_points]] for i in range(num_points)]) + + +``` + +**Step2**: do GA +-> Demo code: [examples/demo_ga_tsp.py#s2](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_tsp.py#L19) +```python + +from sko.GA import GA_TSP + +ga_tsp = GA_TSP(func=cal_total_distance, n_dim=num_points, size_pop=50, max_iter=500, prob_mut=1) +best_points, best_distance = ga_tsp.run() + +``` + +**Step3**: Plot the result: +-> Demo code: [examples/demo_ga_tsp.py#s3](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_tsp.py#L26) +```python +fig, ax = plt.subplots(1, 2) +best_points_ = np.concatenate([best_points, [best_points[0]]]) +best_points_coordinate = points_coordinate[best_points_, :] +ax[0].plot(best_points_coordinate[:, 0], best_points_coordinate[:, 1], 'o-r') +ax[1].plot(ga_tsp.generation_best_Y) +plt.show() +``` + + + + +## 3. PSO(Particle swarm optimization) + +### 3.1 PSO +**Step1**: define your problem: +-> Demo code: [examples/demo_pso.py#s1](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_pso.py#L1) +```python +def demo_func(x): + x1, x2, x3 = x + return x1 ** 2 + (x2 - 0.05) ** 2 + x3 ** 2 + + +``` + +**Step2**: do PSO +-> Demo code: [examples/demo_pso.py#s2](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_pso.py#L6) +```python +from sko.PSO import PSO + +pso = PSO(func=demo_func, n_dim=3, pop=40, max_iter=150, lb=[0, -1, 0.5], ub=[1, 1, 1], w=0.8, c1=0.5, c2=0.5) +pso.run() +print('best_x is ', pso.gbest_x, 'best_y is', pso.gbest_y) + +``` + +**Step3**: Plot the result +-> Demo code: [examples/demo_pso.py#s3](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_pso.py#L13) +```python +import matplotlib.pyplot as plt + +plt.plot(pso.gbest_y_hist) +plt.show() +``` + + + + +### 3.2 PSO with nonlinear constraint + +If you need nolinear constraint like `(x[0] - 1) ** 2 + (x[1] - 0) ** 2 - 0.5 ** 2<=0` +Codes are like this: +```python +constraint_ueq = ( + lambda x: (x[0] - 1) ** 2 + (x[1] - 0) ** 2 - 0.5 ** 2 + , +) +pso = PSO(func=demo_func, n_dim=2, pop=40, max_iter=max_iter, lb=[-2, -2], ub=[2, 2] + , constraint_ueq=constraint_ueq) +``` + +Note that, you can add more then one nonlinear constraint. Just add it to `constraint_ueq` + +More over, we have an animation: + +↑**see [examples/demo_pso_ani.py](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_pso_ani.py)** + + +## 4. SA(Simulated Annealing) +### 4.1 SA for multiple function +**Step1**: define your problem +-> Demo code: [examples/demo_sa.py#s1](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_sa.py#L1) +```python +demo_func = lambda x: x[0] ** 2 + (x[1] - 0.05) ** 2 + x[2] ** 2 + +``` +**Step2**: do SA +-> Demo code: [examples/demo_sa.py#s2](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_sa.py#L3) +```python +from sko.SA import SA + +sa = SA(func=demo_func, x0=[1, 1, 1], T_max=1, T_min=1e-9, L=300, max_stay_counter=150) +best_x, best_y = sa.run() +print('best_x:', best_x, 'best_y', best_y) + +``` + +**Step3**: Plot the result +-> Demo code: [examples/demo_sa.py#s3](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_sa.py#L10) +```python +import matplotlib.pyplot as plt +import pandas as pd + +plt.plot(pd.DataFrame(sa.best_y_history).cummin(axis=0)) +plt.show() + +``` + + + +Moreover, scikit-opt provide 3 types of Simulated Annealing: Fast, Boltzmann, Cauchy. See [more sa](https://scikit-opt.github.io/scikit-opt/#/en/more_sa) +### 4.2 SA for TSP +**Step1**: oh, yes, define your problems. To boring to copy this step. + +**Step2**: DO SA for TSP +-> Demo code: [examples/demo_sa_tsp.py#s2](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_sa_tsp.py#L21) +```python +from sko.SA import SA_TSP + +sa_tsp = SA_TSP(func=cal_total_distance, x0=range(num_points), T_max=100, T_min=1, L=10 * num_points) + +best_points, best_distance = sa_tsp.run() +print(best_points, best_distance, cal_total_distance(best_points)) +``` + +**Step3**: plot the result +-> Demo code: [examples/demo_sa_tsp.py#s3](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_sa_tsp.py#L28) +```python +from matplotlib.ticker import FormatStrFormatter + +fig, ax = plt.subplots(1, 2) + +best_points_ = np.concatenate([best_points, [best_points[0]]]) +best_points_coordinate = points_coordinate[best_points_, :] +ax[0].plot(sa_tsp.best_y_history) +ax[0].set_xlabel("Iteration") +ax[0].set_ylabel("Distance") +ax[1].plot(best_points_coordinate[:, 0], best_points_coordinate[:, 1], + marker='o', markerfacecolor='b', color='c', linestyle='-') +ax[1].xaxis.set_major_formatter(FormatStrFormatter('%.3f')) +ax[1].yaxis.set_major_formatter(FormatStrFormatter('%.3f')) +ax[1].set_xlabel("Longitude") +ax[1].set_ylabel("Latitude") +plt.show() + +``` + + + +More: Plot the animation: + + +↑**see [examples/demo_sa_tsp.py](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_sa_tsp.py)** + + + + +## 5. ACA (Ant Colony Algorithm) for tsp +-> Demo code: [examples/demo_aca_tsp.py#s2](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_aca_tsp.py#L17) +```python +from sko.ACA import ACA_TSP + +aca = ACA_TSP(func=cal_total_distance, n_dim=num_points, + size_pop=50, max_iter=200, + distance_matrix=distance_matrix) + +best_x, best_y = aca.run() + +``` + + + + +## 6. immune algorithm (IA) +-> Demo code: [examples/demo_ia.py#s2](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ia.py#L6) +```python + +from sko.IA import IA_TSP + +ia_tsp = IA_TSP(func=cal_total_distance, n_dim=num_points, size_pop=500, max_iter=800, prob_mut=0.2, + T=0.7, alpha=0.95) +best_points, best_distance = ia_tsp.run() +print('best routine:', best_points, 'best_distance:', best_distance) + +``` + + + +## 7. Artificial Fish Swarm Algorithm (AFSA) +-> Demo code: [examples/demo_afsa.py#s1](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_afsa.py#L1) +```python +def func(x): + x1, x2 = x + return 1 / x1 ** 2 + x1 ** 2 + 1 / x2 ** 2 + x2 ** 2 + + +from sko.AFSA import AFSA + +afsa = AFSA(func, n_dim=2, size_pop=50, max_iter=300, + max_try_num=100, step=0.5, visual=0.3, + q=0.98, delta=0.5) +best_x, best_y = afsa.run() +print(best_x, best_y) +``` + + +# Projects using scikit-opt + +- [Yu, J., He, Y., Yan, Q., & Kang, X. (2021). SpecView: Malware Spectrum Visualization Framework With Singular Spectrum Transformation. IEEE Transactions on Information Forensics and Security, 16, 5093-5107.](https://ieeexplore.ieee.org/abstract/document/9607026/) +- [Zhen, H., Zhai, H., Ma, W., Zhao, L., Weng, Y., Xu, Y., ... & He, X. (2021). Design and tests of reinforcement-learning-based optimal power flow solution generator. Energy Reports.](https://www.sciencedirect.com/science/article/pii/S2352484721012737) +- [Heinrich, K., Zschech, P., Janiesch, C., & Bonin, M. (2021). Process data properties matter: Introducing gated convolutional neural networks (GCNN) and key-value-predict attention networks (KVP) for next event prediction with deep learning. Decision Support Systems, 143, 113494.](https://www.sciencedirect.com/science/article/pii/S016792362100004X) +- [Tang, H. K., & Goh, S. K. (2021). A Novel Non-population-based Meta-heuristic Optimizer Inspired by the Philosophy of Yi Jing. arXiv preprint arXiv:2104.08564.](https://arxiv.org/abs/2104.08564) +- [Wu, G., Li, L., Li, X., Chen, Y., Chen, Z., Qiao, B., ... & Xia, L. (2021). Graph embedding based real-time social event matching for EBSNs recommendation. World Wide Web, 1-22.](https://link.springer.com/article/10.1007/s11280-021-00934-y) +- [Pan, X., Zhang, Z., Zhang, H., Wen, Z., Ye, W., Yang, Y., ... & Zhao, X. (2021). A fast and robust mixture gases identification and concentration detection algorithm based on attention mechanism equipped recurrent neural network with double loss function. Sensors and Actuators B: Chemical, 342, 129982.](https://www.sciencedirect.com/science/article/abs/pii/S0925400521005517) +- [Castella Balcell, M. (2021). Optimization of the station keeping system for the WindCrete floating offshore wind turbine.](https://upcommons.upc.edu/handle/2117/350262) +- [Zhai, B., Wang, Y., Wang, W., & Wu, B. (2021). Optimal Variable Speed Limit Control Strategy on Freeway Segments under Fog Conditions. arXiv preprint arXiv:2107.14406.](https://arxiv.org/abs/2107.14406) +- [Yap, X. H. (2021). Multi-label classification on locally-linear data: Application to chemical toxicity prediction.](https://etd.ohiolink.edu/apexprod/rws_olink/r/1501/10?clear=10&p10_accession_num=wright162901936395651) +- [Gebhard, L. (2021). Expansion Planning of Low-Voltage Grids Using Ant Colony Optimization Ausbauplanung von Niederspannungsnetzen mithilfe eines Ameisenalgorithmus.](https://ad-publications.cs.uni-freiburg.de/theses/Master_Lukas_Gebhard_2021.pdf) +- [Ma, X., Zhou, H., & Li, Z. (2021). Optimal Design for Interdependencies between Hydrogen and Power Systems. IEEE Transactions on Industry Applications.](https://ieeexplore.ieee.org/abstract/document/9585654) +- [de Curso, T. D. C. (2021). Estudo do modelo Johansen-Ledoit-Sornette de bolhas financeiras.](https://d1wqtxts1xzle7.cloudfront.net/67649721/TCC_Thibor_Final-with-cover-page-v2.pdf?Expires=1639140872&Signature=LDZoVsAGO0mLMlVsQjnzpLlRhLyt5wdIDmBjm1yWog5bsx6apyRE9aHuwfnFnc96uvam573wiHMeV08QlK2vhRcQS1d0buenBT5fwoRuq6PTDoMsXmpBb-lGtu9ETiMb4sBYvcQb-X3C7Hh0Ec1FoJZ040gXJPWdAli3e1TdOcGrnOaBZMgNiYX6aKFIZaaXmiQeV3418~870bH4IOQXOapIE6-23lcOL-32T~FSjsOrENoLUkcosv6UHPourKgsRufAY-C2HBUWP36iJ7CoH0jSTo1e45dVgvqNDvsHz7tmeI~0UPGH-A8MWzQ9h2ElCbCN~UNQ8ycxOa4TUKfpCw__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA) +- [Wu, T., Liu, J., Liu, J., Huang, Z., Wu, H., Zhang, C., ... & Zhang, G. (2021). A Novel AI-based Framework for AoI-optimal Trajectory Planning in UAV-assisted Wireless Sensor Networks. IEEE Transactions on Wireless Communications.](https://ieeexplore.ieee.org/abstract/document/9543607) +- [Liu, H., Wen, Z., & Cai, W. (2021, August). FastPSO: Towards Efficient Swarm Intelligence Algorithm on GPUs. In 50th International Conference on Parallel Processing (pp. 1-10).](https://dl.acm.org/doi/abs/10.1145/3472456.3472474) +- [Mahbub, R. (2020). Algorithms and Optimization Techniques for Solving TSP.](https://raiyanmahbub.com/images/Research_Paper.pdf) +- [Li, J., Chen, T., Lim, K., Chen, L., Khan, S. A., Xie, J., & Wang, X. (2019). Deep learning accelerated gold nanocluster synthesis. Advanced Intelligent Systems, 1(3), 1900029.](https://onlinelibrary.wiley.com/doi/full/10.1002/aisy.201900029) + + + + +%package help +Summary: Development documents and examples for scikit-opt +Provides: python3-scikit-opt-doc +%description help + + +# [scikit-opt](https://github.com/guofei9987/scikit-opt) + +[](https://pypi.org/project/scikit-opt/) +[](https://travis-ci.com/guofei9987/scikit-opt) +[](https://codecov.io/gh/guofei9987/scikit-opt) +[](https://github.com/guofei9987/scikit-opt/blob/master/LICENSE) + + +[](https://github.com/guofei9987/scikit-opt/fork) +[](https://pepy.tech/project/scikit-opt) +[](https://github.com/guofei9987/scikit-opt/discussions) + + + + +Swarm Intelligence in Python +(Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Algorithm, Immune Algorithm,Artificial Fish Swarm Algorithm in Python) + + +- **Documentation:** [https://scikit-opt.github.io/scikit-opt/#/en/](https://scikit-opt.github.io/scikit-opt/#/en/) +- **文档：** [https://scikit-opt.github.io/scikit-opt/#/zh/](https://scikit-opt.github.io/scikit-opt/#/zh/) +- **Source code:** [https://github.com/guofei9987/scikit-opt](https://github.com/guofei9987/scikit-opt) +- **Help us improve scikit-opt** [https://www.wjx.cn/jq/50964691.aspx](https://www.wjx.cn/jq/50964691.aspx) + +# install +```bash +pip install scikit-opt +``` + +For the current developer version: +```bach +git clone git@github.com:guofei9987/scikit-opt.git +cd scikit-opt +pip install . +``` + +# Features +## Feature1: UDF + +**UDF** (user defined function) is available now! + +For example, you just worked out a new type of `selection` function. +Now, your `selection` function is like this: +-> Demo code: [examples/demo_ga_udf.py#s1](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_udf.py#L1) +```python +# step1: define your own operator: +def selection_tournament(algorithm, tourn_size): + FitV = algorithm.FitV + sel_index = [] + for i in range(algorithm.size_pop): + aspirants_index = np.random.choice(range(algorithm.size_pop), size=tourn_size) + sel_index.append(max(aspirants_index, key=lambda i: FitV[i])) + algorithm.Chrom = algorithm.Chrom[sel_index, :] # next generation + return algorithm.Chrom + + +``` + +Import and build ga +-> Demo code: [examples/demo_ga_udf.py#s2](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_udf.py#L12) +```python +import numpy as np +from sko.GA import GA, GA_TSP + +demo_func = lambda x: x[0] ** 2 + (x[1] - 0.05) ** 2 + (x[2] - 0.5) ** 2 +ga = GA(func=demo_func, n_dim=3, size_pop=100, max_iter=500, prob_mut=0.001, + lb=[-1, -10, -5], ub=[2, 10, 2], precision=[1e-7, 1e-7, 1]) + +``` +Regist your udf to GA +-> Demo code: [examples/demo_ga_udf.py#s3](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_udf.py#L20) +```python +ga.register(operator_name='selection', operator=selection_tournament, tourn_size=3) +``` + +scikit-opt also provide some operators +-> Demo code: [examples/demo_ga_udf.py#s4](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_udf.py#L22) +```python +from sko.operators import ranking, selection, crossover, mutation + +ga.register(operator_name='ranking', operator=ranking.ranking). \ + register(operator_name='crossover', operator=crossover.crossover_2point). \ + register(operator_name='mutation', operator=mutation.mutation) +``` +Now do GA as usual +-> Demo code: [examples/demo_ga_udf.py#s5](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_udf.py#L28) +```python +best_x, best_y = ga.run() +print('best_x:', best_x, '\n', 'best_y:', best_y) +``` + +> Until Now, the **udf** surport `crossover`, `mutation`, `selection`, `ranking` of GA +> scikit-opt provide a dozen of operators, see [here](https://github.com/guofei9987/scikit-opt/tree/master/sko/operators) + +For advanced users: + +-> Demo code: [examples/demo_ga_udf.py#s6](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_udf.py#L31) +```python +class MyGA(GA): + def selection(self, tourn_size=3): + FitV = self.FitV + sel_index = [] + for i in range(self.size_pop): + aspirants_index = np.random.choice(range(self.size_pop), size=tourn_size) + sel_index.append(max(aspirants_index, key=lambda i: FitV[i])) + self.Chrom = self.Chrom[sel_index, :] # next generation + return self.Chrom + + ranking = ranking.ranking + + +demo_func = lambda x: x[0] ** 2 + (x[1] - 0.05) ** 2 + (x[2] - 0.5) ** 2 +my_ga = MyGA(func=demo_func, n_dim=3, size_pop=100, max_iter=500, lb=[-1, -10, -5], ub=[2, 10, 2], + precision=[1e-7, 1e-7, 1]) +best_x, best_y = my_ga.run() +print('best_x:', best_x, '\n', 'best_y:', best_y) +``` + +## feature2: continue to run +(New in version 0.3.6) +Run an algorithm for 10 iterations, and then run another 20 iterations base on the 10 iterations before: +```python +from sko.GA import GA + +func = lambda x: x[0] ** 2 +ga = GA(func=func, n_dim=1) +ga.run(10) +ga.run(20) +``` + +## feature3: 4-ways to accelerate +- vectorization +- multithreading +- multiprocessing +- cached + +see [https://github.com/guofei9987/scikit-opt/blob/master/examples/example_function_modes.py](https://github.com/guofei9987/scikit-opt/blob/master/examples/example_function_modes.py) + + + +## feature4: GPU computation + We are developing GPU computation, which will be stable on version 1.0.0 +An example is already available: [https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_gpu.py](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_gpu.py) + + +# Quick start + +## 1. Differential Evolution +**Step1**：define your problem +-> Demo code: [examples/demo_de.py#s1](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_de.py#L1) +```python +''' +min f(x1, x2, x3) = x1^2 + x2^2 + x3^2 +s.t. + x1*x2 >= 1 + x1*x2 <= 5 + x2 + x3 = 1 + 0 <= x1, x2, x3 <= 5 +''' + + +def obj_func(p): + x1, x2, x3 = p + return x1 ** 2 + x2 ** 2 + x3 ** 2 + + +constraint_eq = [ + lambda x: 1 - x[1] - x[2] +] + +constraint_ueq = [ + lambda x: 1 - x[0] * x[1], + lambda x: x[0] * x[1] - 5 +] + +``` + +**Step2**: do Differential Evolution +-> Demo code: [examples/demo_de.py#s2](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_de.py#L25) +```python +from sko.DE import DE + +de = DE(func=obj_func, n_dim=3, size_pop=50, max_iter=800, lb=[0, 0, 0], ub=[5, 5, 5], + constraint_eq=constraint_eq, constraint_ueq=constraint_ueq) + +best_x, best_y = de.run() +print('best_x:', best_x, '\n', 'best_y:', best_y) + +``` + +## 2. Genetic Algorithm + +**Step1**：define your problem +-> Demo code: [examples/demo_ga.py#s1](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga.py#L1) +```python +import numpy as np + + +def schaffer(p): + ''' + This function has plenty of local minimum, with strong shocks + global minimum at (0,0) with value 0 + https://en.wikipedia.org/wiki/Test_functions_for_optimization + ''' + x1, x2 = p + part1 = np.square(x1) - np.square(x2) + part2 = np.square(x1) + np.square(x2) + return 0.5 + (np.square(np.sin(part1)) - 0.5) / np.square(1 + 0.001 * part2) + + +``` + +**Step2**: do Genetic Algorithm +-> Demo code: [examples/demo_ga.py#s2](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga.py#L16) +```python +from sko.GA import GA + +ga = GA(func=schaffer, n_dim=2, size_pop=50, max_iter=800, prob_mut=0.001, lb=[-1, -1], ub=[1, 1], precision=1e-7) +best_x, best_y = ga.run() +print('best_x:', best_x, '\n', 'best_y:', best_y) +``` + +-> Demo code: [examples/demo_ga.py#s3](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga.py#L22) +```python +import pandas as pd +import matplotlib.pyplot as plt + +Y_history = pd.DataFrame(ga.all_history_Y) +fig, ax = plt.subplots(2, 1) +ax[0].plot(Y_history.index, Y_history.values, '.', color='red') +Y_history.min(axis=1).cummin().plot(kind='line') +plt.show() +``` + + + +### 2.2 Genetic Algorithm for TSP(Travelling Salesman Problem) +Just import the `GA_TSP`, it overloads the `crossover`, `mutation` to solve the TSP + +**Step1**: define your problem. Prepare your points coordinate and the distance matrix. +Here I generate the data randomly as a demo: +-> Demo code: [examples/demo_ga_tsp.py#s1](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_tsp.py#L1) +```python +import numpy as np +from scipy import spatial +import matplotlib.pyplot as plt + +num_points = 50 + +points_coordinate = np.random.rand(num_points, 2) # generate coordinate of points +distance_matrix = spatial.distance.cdist(points_coordinate, points_coordinate, metric='euclidean') + + +def cal_total_distance(routine): + '''The objective function. input routine, return total distance. + cal_total_distance(np.arange(num_points)) + ''' + num_points, = routine.shape + return sum([distance_matrix[routine[i % num_points], routine[(i + 1) % num_points]] for i in range(num_points)]) + + +``` + +**Step2**: do GA +-> Demo code: [examples/demo_ga_tsp.py#s2](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_tsp.py#L19) +```python + +from sko.GA import GA_TSP + +ga_tsp = GA_TSP(func=cal_total_distance, n_dim=num_points, size_pop=50, max_iter=500, prob_mut=1) +best_points, best_distance = ga_tsp.run() + +``` + +**Step3**: Plot the result: +-> Demo code: [examples/demo_ga_tsp.py#s3](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_tsp.py#L26) +```python +fig, ax = plt.subplots(1, 2) +best_points_ = np.concatenate([best_points, [best_points[0]]]) +best_points_coordinate = points_coordinate[best_points_, :] +ax[0].plot(best_points_coordinate[:, 0], best_points_coordinate[:, 1], 'o-r') +ax[1].plot(ga_tsp.generation_best_Y) +plt.show() +``` + + + + +## 3. PSO(Particle swarm optimization) + +### 3.1 PSO +**Step1**: define your problem: +-> Demo code: [examples/demo_pso.py#s1](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_pso.py#L1) +```python +def demo_func(x): + x1, x2, x3 = x + return x1 ** 2 + (x2 - 0.05) ** 2 + x3 ** 2 + + +``` + +**Step2**: do PSO +-> Demo code: [examples/demo_pso.py#s2](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_pso.py#L6) +```python +from sko.PSO import PSO + +pso = PSO(func=demo_func, n_dim=3, pop=40, max_iter=150, lb=[0, -1, 0.5], ub=[1, 1, 1], w=0.8, c1=0.5, c2=0.5) +pso.run() +print('best_x is ', pso.gbest_x, 'best_y is', pso.gbest_y) + +``` + +**Step3**: Plot the result +-> Demo code: [examples/demo_pso.py#s3](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_pso.py#L13) +```python +import matplotlib.pyplot as plt + +plt.plot(pso.gbest_y_hist) +plt.show() +``` + + + + +### 3.2 PSO with nonlinear constraint + +If you need nolinear constraint like `(x[0] - 1) ** 2 + (x[1] - 0) ** 2 - 0.5 ** 2<=0` +Codes are like this: +```python +constraint_ueq = ( + lambda x: (x[0] - 1) ** 2 + (x[1] - 0) ** 2 - 0.5 ** 2 + , +) +pso = PSO(func=demo_func, n_dim=2, pop=40, max_iter=max_iter, lb=[-2, -2], ub=[2, 2] + , constraint_ueq=constraint_ueq) +``` + +Note that, you can add more then one nonlinear constraint. Just add it to `constraint_ueq` + +More over, we have an animation: + +↑**see [examples/demo_pso_ani.py](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_pso_ani.py)** + + +## 4. SA(Simulated Annealing) +### 4.1 SA for multiple function +**Step1**: define your problem +-> Demo code: [examples/demo_sa.py#s1](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_sa.py#L1) +```python +demo_func = lambda x: x[0] ** 2 + (x[1] - 0.05) ** 2 + x[2] ** 2 + +``` +**Step2**: do SA +-> Demo code: [examples/demo_sa.py#s2](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_sa.py#L3) +```python +from sko.SA import SA + +sa = SA(func=demo_func, x0=[1, 1, 1], T_max=1, T_min=1e-9, L=300, max_stay_counter=150) +best_x, best_y = sa.run() +print('best_x:', best_x, 'best_y', best_y) + +``` + +**Step3**: Plot the result +-> Demo code: [examples/demo_sa.py#s3](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_sa.py#L10) +```python +import matplotlib.pyplot as plt +import pandas as pd + +plt.plot(pd.DataFrame(sa.best_y_history).cummin(axis=0)) +plt.show() + +``` + + + +Moreover, scikit-opt provide 3 types of Simulated Annealing: Fast, Boltzmann, Cauchy. See [more sa](https://scikit-opt.github.io/scikit-opt/#/en/more_sa) +### 4.2 SA for TSP +**Step1**: oh, yes, define your problems. To boring to copy this step. + +**Step2**: DO SA for TSP +-> Demo code: [examples/demo_sa_tsp.py#s2](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_sa_tsp.py#L21) +```python +from sko.SA import SA_TSP + +sa_tsp = SA_TSP(func=cal_total_distance, x0=range(num_points), T_max=100, T_min=1, L=10 * num_points) + +best_points, best_distance = sa_tsp.run() +print(best_points, best_distance, cal_total_distance(best_points)) +``` + +**Step3**: plot the result +-> Demo code: [examples/demo_sa_tsp.py#s3](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_sa_tsp.py#L28) +```python +from matplotlib.ticker import FormatStrFormatter + +fig, ax = plt.subplots(1, 2) + +best_points_ = np.concatenate([best_points, [best_points[0]]]) +best_points_coordinate = points_coordinate[best_points_, :] +ax[0].plot(sa_tsp.best_y_history) +ax[0].set_xlabel("Iteration") +ax[0].set_ylabel("Distance") +ax[1].plot(best_points_coordinate[:, 0], best_points_coordinate[:, 1], + marker='o', markerfacecolor='b', color='c', linestyle='-') +ax[1].xaxis.set_major_formatter(FormatStrFormatter('%.3f')) +ax[1].yaxis.set_major_formatter(FormatStrFormatter('%.3f')) +ax[1].set_xlabel("Longitude") +ax[1].set_ylabel("Latitude") +plt.show() + +``` + + + +More: Plot the animation: + + +↑**see [examples/demo_sa_tsp.py](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_sa_tsp.py)** + + + + +## 5. ACA (Ant Colony Algorithm) for tsp +-> Demo code: [examples/demo_aca_tsp.py#s2](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_aca_tsp.py#L17) +```python +from sko.ACA import ACA_TSP + +aca = ACA_TSP(func=cal_total_distance, n_dim=num_points, + size_pop=50, max_iter=200, + distance_matrix=distance_matrix) + +best_x, best_y = aca.run() + +``` + + + + +## 6. immune algorithm (IA) +-> Demo code: [examples/demo_ia.py#s2](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ia.py#L6) +```python + +from sko.IA import IA_TSP + +ia_tsp = IA_TSP(func=cal_total_distance, n_dim=num_points, size_pop=500, max_iter=800, prob_mut=0.2, + T=0.7, alpha=0.95) +best_points, best_distance = ia_tsp.run() +print('best routine:', best_points, 'best_distance:', best_distance) + +``` + + + +## 7. Artificial Fish Swarm Algorithm (AFSA) +-> Demo code: [examples/demo_afsa.py#s1](https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_afsa.py#L1) +```python +def func(x): + x1, x2 = x + return 1 / x1 ** 2 + x1 ** 2 + 1 / x2 ** 2 + x2 ** 2 + + +from sko.AFSA import AFSA + +afsa = AFSA(func, n_dim=2, size_pop=50, max_iter=300, + max_try_num=100, step=0.5, visual=0.3, + q=0.98, delta=0.5) +best_x, best_y = afsa.run() +print(best_x, best_y) +``` + + +# Projects using scikit-opt + +- [Yu, J., He, Y., Yan, Q., & Kang, X. (2021). SpecView: Malware Spectrum Visualization Framework With Singular Spectrum Transformation. IEEE Transactions on Information Forensics and Security, 16, 5093-5107.](https://ieeexplore.ieee.org/abstract/document/9607026/) +- [Zhen, H., Zhai, H., Ma, W., Zhao, L., Weng, Y., Xu, Y., ... & He, X. (2021). Design and tests of reinforcement-learning-based optimal power flow solution generator. Energy Reports.](https://www.sciencedirect.com/science/article/pii/S2352484721012737) +- [Heinrich, K., Zschech, P., Janiesch, C., & Bonin, M. (2021). Process data properties matter: Introducing gated convolutional neural networks (GCNN) and key-value-predict attention networks (KVP) for next event prediction with deep learning. Decision Support Systems, 143, 113494.](https://www.sciencedirect.com/science/article/pii/S016792362100004X) +- [Tang, H. K., & Goh, S. K. (2021). A Novel Non-population-based Meta-heuristic Optimizer Inspired by the Philosophy of Yi Jing. arXiv preprint arXiv:2104.08564.](https://arxiv.org/abs/2104.08564) +- [Wu, G., Li, L., Li, X., Chen, Y., Chen, Z., Qiao, B., ... & Xia, L. (2021). Graph embedding based real-time social event matching for EBSNs recommendation. World Wide Web, 1-22.](https://link.springer.com/article/10.1007/s11280-021-00934-y) +- [Pan, X., Zhang, Z., Zhang, H., Wen, Z., Ye, W., Yang, Y., ... & Zhao, X. (2021). A fast and robust mixture gases identification and concentration detection algorithm based on attention mechanism equipped recurrent neural network with double loss function. Sensors and Actuators B: Chemical, 342, 129982.](https://www.sciencedirect.com/science/article/abs/pii/S0925400521005517) +- [Castella Balcell, M. (2021). Optimization of the station keeping system for the WindCrete floating offshore wind turbine.](https://upcommons.upc.edu/handle/2117/350262) +- [Zhai, B., Wang, Y., Wang, W., & Wu, B. (2021). Optimal Variable Speed Limit Control Strategy on Freeway Segments under Fog Conditions. arXiv preprint arXiv:2107.14406.](https://arxiv.org/abs/2107.14406) +- [Yap, X. H. (2021). Multi-label classification on locally-linear data: Application to chemical toxicity prediction.](https://etd.ohiolink.edu/apexprod/rws_olink/r/1501/10?clear=10&p10_accession_num=wright162901936395651) +- [Gebhard, L. (2021). Expansion Planning of Low-Voltage Grids Using Ant Colony Optimization Ausbauplanung von Niederspannungsnetzen mithilfe eines Ameisenalgorithmus.](https://ad-publications.cs.uni-freiburg.de/theses/Master_Lukas_Gebhard_2021.pdf) +- [Ma, X., Zhou, H., & Li, Z. (2021). Optimal Design for Interdependencies between Hydrogen and Power Systems. IEEE Transactions on Industry Applications.](https://ieeexplore.ieee.org/abstract/document/9585654) +- [de Curso, T. D. C. (2021). Estudo do modelo Johansen-Ledoit-Sornette de bolhas financeiras.](https://d1wqtxts1xzle7.cloudfront.net/67649721/TCC_Thibor_Final-with-cover-page-v2.pdf?Expires=1639140872&Signature=LDZoVsAGO0mLMlVsQjnzpLlRhLyt5wdIDmBjm1yWog5bsx6apyRE9aHuwfnFnc96uvam573wiHMeV08QlK2vhRcQS1d0buenBT5fwoRuq6PTDoMsXmpBb-lGtu9ETiMb4sBYvcQb-X3C7Hh0Ec1FoJZ040gXJPWdAli3e1TdOcGrnOaBZMgNiYX6aKFIZaaXmiQeV3418~870bH4IOQXOapIE6-23lcOL-32T~FSjsOrENoLUkcosv6UHPourKgsRufAY-C2HBUWP36iJ7CoH0jSTo1e45dVgvqNDvsHz7tmeI~0UPGH-A8MWzQ9h2ElCbCN~UNQ8ycxOa4TUKfpCw__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA) +- [Wu, T., Liu, J., Liu, J., Huang, Z., Wu, H., Zhang, C., ... & Zhang, G. (2021). A Novel AI-based Framework for AoI-optimal Trajectory Planning in UAV-assisted Wireless Sensor Networks. IEEE Transactions on Wireless Communications.](https://ieeexplore.ieee.org/abstract/document/9543607) +- [Liu, H., Wen, Z., & Cai, W. (2021, August). FastPSO: Towards Efficient Swarm Intelligence Algorithm on GPUs. In 50th International Conference on Parallel Processing (pp. 1-10).](https://dl.acm.org/doi/abs/10.1145/3472456.3472474) +- [Mahbub, R. (2020). Algorithms and Optimization Techniques for Solving TSP.](https://raiyanmahbub.com/images/Research_Paper.pdf) +- [Li, J., Chen, T., Lim, K., Chen, L., Khan, S. A., Xie, J., & Wang, X. (2019). Deep learning accelerated gold nanocluster synthesis. Advanced Intelligent Systems, 1(3), 1900029.](https://onlinelibrary.wiley.com/doi/full/10.1002/aisy.201900029) + + + + +%prep +%autosetup -n scikit-opt-0.6.6 + +%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-scikit-opt -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Wed May 31 2023 Python_Bot <Python_Bot@openeuler.org> - 0.6.6-1 +- Package Spec generated @@ -0,0 +1 @@ +8575064094d238ee341cf1da39f2aaef scikit-opt-0.6.6.tar.gz |