粒子群优化算法的python实现(带例子、详细注释版、迭代结果可视化)

#!/usr/bin/env python # -*- coding:utf-8 -*- # author: 'JF' # name: PSO # datetime:2022/1/11 17:26 # software:PyCharm import random import numpy as np import matplotlib.pyplot as plt class PSO: """粒子群算法 Parameters: ------------ dimension:变量维数 times：迭代次数上限 size：种群大小 x_low：位置约束最小值 x_up：位置约束最大值 v_low：速度约束最小值 v_high：速度约束最大值 Returns: ------------- final_best:最佳变量 """ def __init__(self,dimension,times,size,x_low,x_up,v_low,v_high): #初始化 self.dimension = dimension #变量个数 self.time = times # 迭代次数 self.size = size # 种群大小 self.bound = [x_low, x_up] #变量约束 self.v_low = v_low #速度最小约束 self.v_high = v_high#速度最大约束 self.x=np.zeros((self.size,self.dimension))#所有粒子的位置 self.v =np.zeros((self.size,self.dimension))#所有粒子的速度 self.p_best = np.zeros((self.size,self.dimension))# 每个粒子的历史最优位置 self.g_best = np.zeros((1,self.dimension))[0]# 全局历史最优 #初始化第0次迭代的全局最优解 temp = -100000 for i in range(self.size): for j in range(self.dimension): self.x[i][j] = random.uniform(self.bound[0][j],self.bound[1][j]) self.v[i][j] = random.uniform(self.v_low,self.v_high) self.p_best[i] = self.x[i] fit = self.fitness(self.p_best[i]) # 修改 if fit > temp: self.g_best = self.p_best[i] temp = fit def fitness(self,_x): "计算个体适应值" # _y = math.sqrt(reduce(lambda __x, __y: __x ** 2 + __y ** 2, _x)) # y = math.floor()#目标函数 _y = -np.abs(np.sin(_x[0]) * np.cos(_x[1]) * np.exp(np.abs(1 - np.sqrt(_x[0] ** 2 + _x[1] ** 2) / np.pi))) return _y def update(self, size): c1 = 2.0 #个体学习因子 c2 = 2.0 #群体学习因子 w = 0.8 #自身权重因子 for i in range(size): #更新速度（核心） self.v[i] = w*self.v[i]+c1 * random.uniform(0,1)*(self.p_best[i]-self.x[i])+c2 * random.uniform(0,1)*(self.g_best-self.x[i]) #速度限制 for j in range(self.dimension): if self.v[i][j] < self.v_low:self.v[i][j]=self.v_low if self.v[i][j]>self.v_high:self.v[i][j] = self.v_high #更新位置 self.x[i] = self.x[i] + self.v[i] # 位置限制 for j in range(self.dimension): if self.x[i][j]<self.bound[0][j]:self.x[i][j] = self.bound[0][j] if self.x[i][j]>self.bound[1][j]:self.x[i][j] = self.bound[1][j] # 更新p_best和g_best if self.fitness(self.x[i])>self.fitness(self.p_best[i]):self.p_best[i]=self.x[i] if self.fitness(self.x[i])>self.fitness(self.g_best):self.g_best=self.x[i] def pso(self,draw_figure): best=[] # self.final_best = np.array([1,2,3,4,5]) self.final_best = np.random.randn(self.dimension) for gen in range(self.time): self.update(self.size) if self.fitness(self.g_best)>self.fitness(self.final_best): self.final_best = self.g_best.copy() print(f'当前最佳位置：{self.final_best}') # print(f'当前最佳适应度：{temp}') best.append(self.fitness(self.final_best)) if draw_figure: plt.figure(num=1) plt.plot(best, color='red',marker='.',ms=15) plt.show() return self.final_best if __name__ == '__main__': times= 1000 size= 20 dimension = 2 v_low = -0.5 v_high = 0.5 x_low = [-10,-10] x_up=[10,10] pso = PSO(dimension,times,size,x_low,x_up,v_low,v_high) pso.pso(True)