标签：流程图 进入 algo dev 组成 col test 生成 nbsp
人工免疫算法(Immune Algorithm)是一种具有生成+检测 (generate and test)的迭代过程的群智能搜索算法。从理论上分析，迭代过程中，在保留上一代最佳个体的前提下，免疫算法是全局收敛的。
用IA解决TSP问题
import numpy as np from scipy import spatial # 全国31个省会(部分)城市的坐标 points_coordinate=[ [1304, 2312], [3639, 1315], [4177, 2244], [3712, 1399], [3488, 1535], [3326, 1556], [3238, 1229], [4196, 1004], [4312, 790], [4386, 570], [3007, 1970], [2562, 1756], [2788, 1491], [2381, 1676], [1332, 695], [3715, 1678], [3918, 2179], [4061, 2370], [3780, 2212], [3676, 2578], [4029, 2838], [4263, 2931], [3429, 1908], [3507, 2367], [3394, 2643], [3439, 3201], [2935, 3240], [3140, 3550], [2545, 2357], [2778, 2826], [2370, 2975], ] points_coordinate = np.array(points_coordinate) num_points = points_coordinate.shape[0] distance_matrix = spatial.distance.cdist(points_coordinate, points_coordinate, metric=‘euclidean‘) def cal_total_distance(routine): num_points, = routine.shape return sum([distance_matrix[routine[i % num_points], routine[(i + 1) % num_points]] for i in range(num_points)]) # run IA 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) # step3: plot import matplotlib.pyplot as plt 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()
结果如下：
best routine: [19 20 21 17 2 16 18 22 15 3 7 8 9 1 4 5 6 12 11 13 14 0 30 26 27 25 29 28 10 23 24]
best_distance: [15844.52047043]
参考链接：
3. 百度百科-免疫算法
标签：流程图 进入 algo dev 组成 col test 生成 nbsp
原文地址：https://www.cnblogs.com/lfri/p/12242443.html
