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+%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)
+
+[![PyPI](https://img.shields.io/pypi/v/scikit-opt)](https://pypi.org/project/scikit-opt/)
+[![Build Status](https://travis-ci.com/guofei9987/scikit-opt.svg?branch=master)](https://travis-ci.com/guofei9987/scikit-opt)
+[![codecov](https://codecov.io/gh/guofei9987/scikit-opt/branch/master/graph/badge.svg)](https://codecov.io/gh/guofei9987/scikit-opt)
+[![License](https://img.shields.io/pypi/l/scikit-opt.svg)](https://github.com/guofei9987/scikit-opt/blob/master/LICENSE)
+![Python](https://img.shields.io/badge/python->=3.5-green.svg)
+![Platform](https://img.shields.io/badge/platform-windows%20|%20linux%20|%20macos-green.svg)
+[![fork](https://img.shields.io/github/forks/guofei9987/scikit-opt?style=social)](https://github.com/guofei9987/scikit-opt/fork)
+[![Downloads](https://pepy.tech/badge/scikit-opt)](https://pepy.tech/project/scikit-opt)
+[![Discussions](https://img.shields.io/badge/discussions-green.svg)](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()
+```
+
+![Figure_1-1](https://img1.github.io/heuristic_algorithm/ga_1.png)
+
+### 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()
+```
+
+![GA_TPS](https://img1.github.io/heuristic_algorithm/ga_tsp.png)
+
+
+## 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()
+```
+
+
+![PSO_TPS](https://img1.github.io/heuristic_algorithm/pso.png)
+
+### 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:  
+![pso_ani](https://img1.github.io/heuristic_algorithm/pso.gif)  
+↑**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()
+
+```
+![sa](https://img1.github.io/heuristic_algorithm/sa.png)
+
+
+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()
+
+```
+![sa](https://img1.github.io/heuristic_algorithm/sa_tsp.png)
+
+
+More: Plot the animation:  
+
+![sa](https://img1.github.io/heuristic_algorithm/sa_tsp1.gif)  
+↑**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()
+
+```
+
+![ACA](https://img1.github.io/heuristic_algorithm/aca_tsp.png)
+
+
+## 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)
+
+```
+
+![IA](https://img1.github.io/heuristic_algorithm/ia2.png)
+
+## 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)
+
+[![PyPI](https://img.shields.io/pypi/v/scikit-opt)](https://pypi.org/project/scikit-opt/)
+[![Build Status](https://travis-ci.com/guofei9987/scikit-opt.svg?branch=master)](https://travis-ci.com/guofei9987/scikit-opt)
+[![codecov](https://codecov.io/gh/guofei9987/scikit-opt/branch/master/graph/badge.svg)](https://codecov.io/gh/guofei9987/scikit-opt)
+[![License](https://img.shields.io/pypi/l/scikit-opt.svg)](https://github.com/guofei9987/scikit-opt/blob/master/LICENSE)
+![Python](https://img.shields.io/badge/python->=3.5-green.svg)
+![Platform](https://img.shields.io/badge/platform-windows%20|%20linux%20|%20macos-green.svg)
+[![fork](https://img.shields.io/github/forks/guofei9987/scikit-opt?style=social)](https://github.com/guofei9987/scikit-opt/fork)
+[![Downloads](https://pepy.tech/badge/scikit-opt)](https://pepy.tech/project/scikit-opt)
+[![Discussions](https://img.shields.io/badge/discussions-green.svg)](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()
+```
+
+![Figure_1-1](https://img1.github.io/heuristic_algorithm/ga_1.png)
+
+### 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()
+```
+
+![GA_TPS](https://img1.github.io/heuristic_algorithm/ga_tsp.png)
+
+
+## 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()
+```
+
+
+![PSO_TPS](https://img1.github.io/heuristic_algorithm/pso.png)
+
+### 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:  
+![pso_ani](https://img1.github.io/heuristic_algorithm/pso.gif)  
+↑**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()
+
+```
+![sa](https://img1.github.io/heuristic_algorithm/sa.png)
+
+
+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()
+
+```
+![sa](https://img1.github.io/heuristic_algorithm/sa_tsp.png)
+
+
+More: Plot the animation:  
+
+![sa](https://img1.github.io/heuristic_algorithm/sa_tsp1.gif)  
+↑**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()
+
+```
+
+![ACA](https://img1.github.io/heuristic_algorithm/aca_tsp.png)
+
+
+## 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)
+
+```
+
+![IA](https://img1.github.io/heuristic_algorithm/ia2.png)
+
+## 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)
+
+[![PyPI](https://img.shields.io/pypi/v/scikit-opt)](https://pypi.org/project/scikit-opt/)
+[![Build Status](https://travis-ci.com/guofei9987/scikit-opt.svg?branch=master)](https://travis-ci.com/guofei9987/scikit-opt)
+[![codecov](https://codecov.io/gh/guofei9987/scikit-opt/branch/master/graph/badge.svg)](https://codecov.io/gh/guofei9987/scikit-opt)
+[![License](https://img.shields.io/pypi/l/scikit-opt.svg)](https://github.com/guofei9987/scikit-opt/blob/master/LICENSE)
+![Python](https://img.shields.io/badge/python->=3.5-green.svg)
+![Platform](https://img.shields.io/badge/platform-windows%20|%20linux%20|%20macos-green.svg)
+[![fork](https://img.shields.io/github/forks/guofei9987/scikit-opt?style=social)](https://github.com/guofei9987/scikit-opt/fork)
+[![Downloads](https://pepy.tech/badge/scikit-opt)](https://pepy.tech/project/scikit-opt)
+[![Discussions](https://img.shields.io/badge/discussions-green.svg)](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()
+```
+
+![Figure_1-1](https://img1.github.io/heuristic_algorithm/ga_1.png)
+
+### 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()
+```
+
+![GA_TPS](https://img1.github.io/heuristic_algorithm/ga_tsp.png)
+
+
+## 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()
+```
+
+
+![PSO_TPS](https://img1.github.io/heuristic_algorithm/pso.png)
+
+### 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:  
+![pso_ani](https://img1.github.io/heuristic_algorithm/pso.gif)  
+↑**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()
+
+```
+![sa](https://img1.github.io/heuristic_algorithm/sa.png)
+
+
+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()
+
+```
+![sa](https://img1.github.io/heuristic_algorithm/sa_tsp.png)
+
+
+More: Plot the animation:  
+
+![sa](https://img1.github.io/heuristic_algorithm/sa_tsp1.gif)  
+↑**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()
+
+```
+
+![ACA](https://img1.github.io/heuristic_algorithm/aca_tsp.png)
+
+
+## 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)
+
+```
+
+![IA](https://img1.github.io/heuristic_algorithm/ia2.png)
+
+## 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