差分进化算法用c语言实现,python 实现差分进化算法
对于种群优化算法来说，最著名的为粒子群算法、遗传算法及差分进化算法，在前面的博客中实现了其中基本型。应在用C语言编写的运行速度快，但是Python近年来越来越流行，所以蟒蛇实现差分进化算法，通过函数测试运行很快速。主要是用到了numpy库来实现，绘图显示使用的是matplotlib库。
差分进化算法总的有四步骤：
(1)种群初始化
(2)变异操作
(3)交叉操作
(4)贪婪选择操作
# -*- coding:utf-8 -*-
# copyright(c) Greg Gong reserved
#Copyright Clarify
#Copyright ownership belongs to Greg Gong, shall not be reproduced , copied, or used in #other ways without permission. Otherwise Greg Gong will have the right to pursue legal #responsibilities.
import numpy as np
import matplotlib.pyplot as plt
class de:
"""
pop_pos:
pop_fit:
pop_best_pos:
pop_best_fit:
CR:
F:
max_iter:
n_var:
"""
def __init__(self,max_iter,n_var,CR,F,n_pop,bound,fit_func):
self.max_iter=max_iter
self.n_var=n_var
self.CR=CR
self.F=F
self.fit_func=fit_func
self.n_pop=n_pop
self.iter_fit=np.arange(max_iter)
self.bound=bound
# 产生种群
self.pop_pos=bound[1,:]+np.random.rand(n_pop,n_var)*(bound[0,:]-bound[1,:])
self.pop_fit=self.fit_func(self.pop_pos)
self.pop_best_fit=np.min(self.pop_fit)
self.pop_best_pos=self.pop_pos[np.argmin(self.pop_fit),:]
self.pop_cross=self.pop_pos.copy()
self.pop_cross_fit=self.pop_fit.copy()
self.pop_mutute=self.pop_pos.copy()
self.pop_mutute_fit=self.pop_fit.copy()
def mutute_pop(self):
rand1=np.random.choice(self.n_pop)
rand2=np.random.choice(self.n_pop)
rand3=np.random.choice(self.n_pop)
if (rand1==rand2)|(rand2==rand3)|(rand1==rand3):
rand1=np.random.choice(self.n_pop)
rand2=np.random.choice(self.n_pop)
rand3=np.random.choice(self.n_pop)
for i in range(self.n_pop):
self.pop_mutute[i,:]=self.pop_pos[rand1,:]+self.F*(self.pop_pos[rand2,:]-self.pop_pos[rand3,:])
index_up=self.pop_mutute[i,:]>self.bound[0,:]
self.pop_mutute[i,index_up]=self.bound[0,index_up].copy()
index_down=self.pop_mutute[i,:]self.bound[0,:]
self.pop_cross[i,index_up]=self.bound[0,index_up].copy()
index_down=self.pop_cross[i,:]