基于遗传算法、粒子群算法、模拟退火、蚁群算法、免疫优化算法、鱼群算法，旅行商问题仿真（完整源码+数据）.zip

# [scikit-opt](https://github.com/guofei9987/scikit-opt) [](https://pypi.org/project/scikit-opt/) [](https://github.com/guofei9987/scikit-opt) [](https://travis-ci.com/guofei9987/scikit-opt) [](https://codecov.io/gh/guofei9987/scikit-opt) [](https://pepy.tech/project/scikit-opt) [](https://github.com/guofei9987/scikit-opt/stargazers) [](https://github.com/guofei9987/scikit-opt/network/members) [](https://gitter.im/guofei9987/scikit-opt?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) 一个封装了7种启发式算法的 Python 代码库 （差分进化算法、遗传算法、粒子群算法、模拟退火算法、蚁群算法、鱼群算法、免疫优化算法） # 安装 ```bash pip install scikit-opt ``` 或者直接把源代码中的 `sko` 文件夹下载下来放本地也调用可以 # 特性 ## 特性1：UDF（用户自定义算子） 举例来说，你想出一种新的“选择算子”，如下 -> 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 ``` 导入包，并且创建遗传算法实例 -> 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]) ``` 把你的算子注册到你创建好的遗传算法实例上 -> 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 也提供了十几个算子供你调用 -> 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) ``` 做遗传算法运算 -> 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) ``` > 现在 **udf** 支持遗传算法的这几个算子： `crossover`, `mutation`, `selection`, `ranking` > Scikit-opt 也提供了十来个算子，参考[这里](https://github.com/guofei9987/scikit-opt/tree/master/sko/operators) > 提供一个面向对象风格的自定义算子的方法，供进阶用户使用: -> 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) ``` ## 特性2：断点继续运行 例如，先跑10代，然后在此基础上再跑20代，可以这么写： ```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) ``` ## 特性3：4种加速方法 - [x] 矢量化计算：vectorization - [x] 多线程计算：multithreading，适用于 IO 密集型目标函数 - [x] 多进程计算：multiprocessing，适用于 CPU 密集型目标函数 - [x] 缓存化计算：cached，适用于目标函数的每次输入有大量重复 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) ## 特性4: GPU 加速 GPU加速功能还比较简单，将会在 1.0.0 版本大大完善。 有个 demo 已经可以在现版本运行了: [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) # 快速开始 ## 1. 差分进化算法 **Step1**：定义你的问题，这个demo定义了有约束优化问题 -> 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**: 做差分进化算法 -> 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. 遗传算法 **第一步**：定义你的问题 -> 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) ``` **第二步**：运行遗传算法 -> 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) ``` **第三步**：用 matplotlib 画出结果 -> 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.