蚁群算法python库_使用流行的遗传算法python库是哪个？

guofei9987/scikit-opt 这套算法库，很符合要求了。这个库对遗传算法、粒子群算法、模拟退火、蚁群算法较好的封装。

先定义好你的目标函数后，仅需要一两行代码就可以实现相应算法。

1. 遗传算法(Genetic Algorithm)

定义目标函数

def demo_func(x):

x1, x2, x3 = x

return x1 ** 2 + (x2 - 0.05) ** 2 + x3 ** 2

调入遗传算法求解器

from ga import GA

ga = GA(func=demo_func, lb=[-1, -10, -5], ub=[2, 10, 2], max_iter=500)

best_x, best_y = ga.fit()

用 matplotlib 画出结果

import pandas as pd

import matplotlib.pyplot as plt

FitV_history = pd.DataFrame(ga.FitV_history)

fig, ax = plt.subplots(2, 1)

ax[0].plot(FitV_history.index, FitV_history.values, '.', color='red')

plt_max = FitV_history.max(axis=1)

ax[1].plot(plt_max.index, plt_max, label='max')

ax[1].plot(plt_max.index, plt_max.cummax())

plt.show()

1.1 用遗传算法解决TSP问题(旅行商问题)

GA_TSP 针对TSP问题重载了 交叉(crossover)、变异(mutation) 两个算子

这里作为demo，随机生成距离矩阵. 实战中，从数据源中读取。

import numpy as np

num_points = 8

points = range(num_points)

points_coordinate = np.random.rand(num_points, 2)

distance_matrix = np.zeros(shape=(num_points, num_points))

for i in range(num_points):

for j in range(num_points):

distance_matrix[i][j] = np.linalg.norm(points_coordinate[i] - points_coordinate[j], ord=2)

print('distance_matrix is: \n', distance_matrix)

def cal_total_distance(points):

num_points, = points.shape

total_distance = 0

for i in range(num_points - 1):

total_distance += distance_matrix[points[i], points[i + 1]]

total_distance += distance_matrix[points[i + 1], points[0]]

return total_distance

然后调用遗传算法进行求解

from GA import GA_TSP

ga_tsp = GA_TSP(func=cal_total_distance, points=points, pop=50, max_iter=200, Pm=0.001)

best_points, best_distance = ga_tsp.fit()

画出结果

fig, ax = plt.subplots(1, 1)

best_points_ = np.concatenate([best_points, [best_points[0]]])

best_points_coordinate = points_coordinate[best_points_, :]

ax.plot(best_points_coordinate[:, 0], best_points_coordinate[:, 1],'o-r')

plt.show()