NSGA2快速非支配排序实现-python

 1 import numpy as np
 2 
 3 
 4 def compare(p1, p2):
 5     # return 0同层 1 p1支配p2
 6     # 每个维度越小越优秀
 7     # 计D次
 8     D = len(p1)
 9     p1_dominate_p2 = True  # p1 更小
10     p2_dominate_p1 = True
11     for i in range(D):
12         if p1[i] > p2[i]:
13             p1_dominate_p2 = False
14         if p1[i] < p2[i]:
15             p2_dominate_p1 = False
16 
17     if p1_dominate_p2 == p2_dominate_p1:
18         return 0
19     return 1 if p1_dominate_p2 else -1
20 
21 
22 def fast_non_dominated_sort(P):
23     # 成员编号为 0 ~ P_size-1
24     P_size = len(P)
25     # 被支配数
26     n = np.full(shape=P_size, fill_value=0)
27     # 支配的成员
28     S = []
29     # 每层包含的成员编号们
30     f = []  # 0 开始
31     # 所处等级
32     rank = np.full(shape=P_size, fill_value=-1)
33 
34     f_0 = []
35     for p in range(P_size):
36         n_p = 0
37         S_p = []
38         for q in range(P_size):
39             if p == q:
40                 continue
41             cmp = compare(P[p], P[q])
42             if cmp == 1:
43                 S_p.append(q)
44             elif cmp == -1:  # 被支配
45                 n_p += 1
46         S.append(S_p)
47         n[p] = n_p
48         if n_p == 0:
49             rank[p] = 0
50             f_0.append(p)
51 
52     f.append(f_0)  # 这时候f[0]必存在
53 
54     i = 0
55     while len(f[i]) != 0:  # 可能还有i+1层
56         Q = []
57         for p in f[i]:  # i层中每个个体
58             for q in S[p]:  # 被p支配的个体
59                 n[q] -= 1
60                 if n[q] == 0:
61                     rank[q] = i + 1
62                     Q.append(q)
63         i += 1
64         f.append(Q)
65     return rank, f
66 
67 
68 import matplotlib.pyplot as plt
69 
70 if __name__ == '__main__':
71     P = np.random.random(size=(200, 2))
72     rank, f = fast_non_dominated_sort(P)
73     f.pop()
74     # print(rank)
75     # print(f)
76 
77     # 绘图
78     for t in f:
79         # 每level
80         x = P[t][:, 0]
81         y = P[t][:, 1]
82         plt.scatter(x, y, s=15)  # s 点的大小  c 点的颜色 alpha 透明度
83 
84     plt.show()

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