安装
pip install scikit-opt
对于当前的开发者版本：
git clone git@:guofei9987/scikit-opt.git
cd scikit-opt
pipinstall .
Genetic Algorithm
第一步：定义你的问题
importnumpy as npdefschaffer(p):'''This function has plenty of local minimum, with strong shocks
global minimum at (0,0) with value 0'''x1, x2=p
x= np.square(x1) +np.square(x2)return 0.5 + (np.sin(x) - 0.5) / np.square(1 + 0.001 * x)
第二步：运行遗传算法
from sko.GA importGA
#2个变量，每代取50个，800次迭代，上下界及精度
ga= GA(func=schaffer, n_dim=2, size_pop=50, max_iter=800, 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：画出结果
importpandas as pdimportmatplotlib.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()
精度改成1就能视为整数规划。
Genetic Algorithm for TSP(Travelling Salesman Problem)
只需要导入GA_TSP，它重载了crossover, mutation来解决TSP.
第一步：定义你的问题。准备你的点的坐标和距离矩阵。
这里使用随机数据作为Demo.
importnumpy as npfrom scipy importspatialimportmatplotlib.pyplot as plt
num_points= 50points_coordinate= np.random.rand(num_points, 2) #generate coordinate of points
distance_matrix = spatial.distance.cdist(points_coordinate, points_coordinate, metric='euclidean')defcal_total_distance(routine):'''The objective function. input routine, return total distance.
cal_total_distance(np.arange(num_points))'''num_points,=routine.shapereturn sum([distance_matrix[routine[i % num_points], routine[(i + 1) % num_points]] for i in range(num_points)])
第二步：运行GA算法
from sko.GA importGA_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()
第三步：画出结果
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()
