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,:]