零基础学习Python--粒子群算法PSO函数寻优-2

Halcom

零基础学习Python--粒子群算法PSO函数寻优-2：（菜鸟编程solemnysw）
版权声明：本文为博主原创文章，未经博主允许不得转载。严格禁止其它商用行为！
百度网盘链接：
链接：http://pan.baidu.com/s/1hrYe6cW
链接：https://pan.baidu.com/s/1yECy3SICh_8a1QZzmtu5ww 提取码：n4ip

具体链接在halcom.cn论坛，联系人QQ：3283892722
该论坛是一个学习交流平台，我会逐一的和大家分享学习。
欢迎大家录制视频，你可在论坛进行打赏分享。
视频专用播放器：http://halcom.cn/forum.php?mod=viewthread&tid=258&extra=page%3D1

1. from PIL import Image
   
2. from pylab import *
   
3. import string
   
4. import numpy
   
5. from numpy import random
   
6. import fun
   
7. # web('halcom.cn')
   
8. 
   
9. # PSO algorithm parameters
   
10. c1 = 1.49445;
    
11. c2 = 1.49445;
    
12. maxg=50;  
    
13. sizepop=20;
    
14. Vmax=1;
    
15. Vmin=-1;
    
16. popmax= numpy.array( [ [5], [5] ] );  
    
17. popmin = numpy.array( [ [-5], [-4] ] );  
    
18. nVar = 2;  
    
19. pop = [[0 for x in range(nVar)] for y in range(sizepop)]
    
20. gbest = [[0 for x in range(nVar)] for y in range(sizepop)]
    
21. fitness = [0 for x in range(sizepop)]
    
22. fitnessgbest = [0 for x in range(sizepop)]
    
23. zbest = [0 for x in range(nVar)]
    
24. V = [[0 for x in range(nVar)] for y in range(sizepop)]
    
25. 
    
26. # init pop
    
27. for i in range(sizepop):
    
28.     pop[i] = popmin + (popmax-popmin)*random.random( [ nVar, 1] );
    
29.     V[i] = numpy.array( [ [0], [0] ] );
    
30.     fitness[i] = fun.funfitness2(pop[i]);
    
31.     gbest[i] = pop[i]
    
32.     fitnessgbest[i] = fitness[i]
    
33. fitnesszbest = min(fitness);
    
34. index = fun.minIndex(fitness, fitnesszbest);
    
35. zbest = pop[index]
    
36. 
    
37. fitness_iter = [0 for x in range(maxg)]
    
38. # main loop
    
39. for i in range(maxg):
    
40.     for j in range(sizepop):
    
41.         # update vel
    
42.         r1 = random.random();
    
43.         r2 = random.random();
    
44.         V[j] = V[j] + c1*r1*(gbest[j]-pop[j])+c2*r2*(zbest-pop[j]);
    
45.         if (V[j][0]>Vmax):
    
46.             V[j][0] = Vmax;
    
47.         elif (V[j][0]<Vmin):
    
48.             V[j][0] = Vmin;
    
49.         if (V[j][1]>Vmax):
    
50.             V[j][1] = Vmax;
    
51.         elif (V[j][1]<Vmin):
    
52.             V[j][1] = Vmin;
    
53. 
    
54.         # update position
    
55.         pop[j] = pop[j] + 0.5*V[j];
    
56.         if (pop[j][0]>popmax[0]):
    
57.             pop[j][0] = popmax[0];
    
58.         elif (pop[j][0]<popmin[0]):
    
59.             pop[j][0] = popmin[0];
    
60.         if (pop[j][1]>popmax[1]):
    
61.             pop[j][1] = popmax[1];
    
62.         elif (pop[j][1]<popmin[1]):
    
63.             pop[j][1] = popmin[1];
    
64.         
    
65.         # update fitness
    
66.         fitness[j] = fun.funfitness2(pop[j]);
    
67. 
    
68.         # compare
    
69.         if (fitness[j]<fitnessgbest[j]):
    
70.             fitnessgbest[j] = fitness[j]
    
71.             gbest[j][0] = pop[j][0];
    
72.             gbest[j][1] = pop[j][1];
    
73.         if (fitness[j]<fitnesszbest):
    
74.             fitnesszbest= fitness[j];
    
75.             zbest = pop[j];
    
76.     fitness_iter[i] = fitnesszbest;
    
77. 
    
78. print "best solution is:"
    
79. print zbest
    
80. 
    
81. print "min objective value is:"
    
82. print fitnesszbest
    
83. 
    
84. plot( fitness_iter,'b-' )
    
85. show()

复制代码

子函数如下：

1. def funfitness(x1,x2):
   
2.     y = (x1-0.5)**2+(x2-0.6)**2;
   
3.     return y;
   
4. 
   
5. def funfitness2(x):
   
6.     y = (x[0]-0.5)**2+(x[1]-0.6)**2;
   
7.     return y;
   
8. 
   
9. 
   
10. def minIndex(fitness, fitnesszbest):
    
11.     for i in range(len(fitness)):
    
12.         if(fitness[i] == fitnesszbest):
    
13.             break;
    
14.     return i;
    

复制代码