零基础学习Python--粒子群算法PSO函数寻优-2
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！
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视频专用播放器：http://halcom.cn/forum.php?mod=viewthread&tid=258&extra=page%3D1from PIL import Image
from pylab import *
import string
import numpy
from numpy import random
import fun
# web('halcom.cn')
# PSO algorithm parameters
c1 = 1.49445;
c2 = 1.49445;
maxg=50;
sizepop=20;
Vmax=1;
Vmin=-1;
popmax= numpy.array( [ , ] );
popmin = numpy.array( [ [-5], [-4] ] );
nVar = 2;
pop = [ for y in range(sizepop)]
gbest = [ for y in range(sizepop)]
fitness =
fitnessgbest =
zbest =
V = [ for y in range(sizepop)]
# init pop
for i in range(sizepop):
pop = popmin + (popmax-popmin)*random.random( [ nVar, 1] );
V = numpy.array( [ , ] );
fitness = fun.funfitness2(pop);
gbest = pop
fitnessgbest = fitness
fitnesszbest = min(fitness);
index = fun.minIndex(fitness, fitnesszbest);
zbest = pop
fitness_iter =
# main loop
for i in range(maxg):
for j in range(sizepop):
# update vel
r1 = random.random();
r2 = random.random();
V = V + c1*r1*(gbest-pop)+c2*r2*(zbest-pop);
if (V>Vmax):
V = Vmax;
elif (V<Vmin):
V = Vmin;
if (V>Vmax):
V = Vmax;
elif (V<Vmin):
V = Vmin;
# update position
pop = pop + 0.5*V;
if (pop>popmax):
pop = popmax;
elif (pop<popmin):
pop = popmin;
if (pop>popmax):
pop = popmax;
elif (pop<popmin):
pop = popmin;
# update fitness
fitness = fun.funfitness2(pop);
# compare
if (fitness<fitnessgbest):
fitnessgbest = fitness
gbest = pop;
gbest = pop;
if (fitness<fitnesszbest):
fitnesszbest= fitness;
zbest = pop;
fitness_iter = fitnesszbest;
print "best solution is:"
print zbest
print "min objective value is:"
print fitnesszbest
plot( fitness_iter,'b-' )
show()子函数如下：
def funfitness(x1,x2):
y = (x1-0.5)**2+(x2-0.6)**2;
return y;
def funfitness2(x):
y = (x-0.5)**2+(x-0.6)**2;
return y;
def minIndex(fitness, fitnesszbest):
for i in range(len(fitness)):
if(fitness == fitnesszbest):
break;
return i;
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