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| import numpy as np import random import math import copy import matplotlib.pyplot as plt
class PSOIndividual: ''' 模拟粒子个体 ''' def __init__(self, vardim, bound): ''' 个体初始化 vardim: 变量维数 bound: 变量取值范围 ''' self.vardim = vardim self.bound = bound self.fitness = 0.
def generate(self): ''' 生成随机性 ''' len = self.vardim rnd = np.random.random(size=len) self.chrom = np.zeros(len) self.velocity = np.random.random(size=len) for i in range(0, len): self.chrom[i] = self.bound[0, i] + (self.bound[1, i] - self.bound[0, i]) * rnd[i] self.bestPosition = np.zeros(len) self.bestFitness = 0.
def GrieFunc(self, vardim, x, bound): ''' 差分算法 ''' s1 = 0. s2 = 1. for i in range(1, vardim + 1): s1 = s1 + x[i - 1] ** 2 s2 = s2 * math.cos(x[i - 1] / math.sqrt(i)) y = (1. / 4000.) * s1 - s2 + 1 y = 1. / (1. + y) return y
def calculateFitness(self): ''' 计算适应性 ''' self.fitness = self.GrieFunc(self.vardim, self.chrom, self.bound)
class PSO: ''' 粒子群算法 ''' def __init__(self, sizepop, vardim, bound, MAXGEN, params): ''' sizepop: 种群规模 vardim: 变量维数 bound: 变量取值范围 MAXGEN: 最大迭代次数(终止条件) param: 其他参数(列表) ''' self.sizepop = sizepop self.vardim = vardim self.bound = bound self.MAXGEN = MAXGEN self.params = params self.population = [] self.fitness = np.zeros((self.sizepop, 1)) self.history = np.zeros((self.MAXGEN, 2))
def initialize(self): ''' 初始化种群 ''' for i in range(0, self.sizepop): ind = PSOIndividual(self.vardim, self.bound) ind.generate() self.population.append(ind)
def evaluation(self): ''' 评估环境适应性得分 ''' for i in range(0, self.sizepop): self.population[i].calculateFitness() self.fitness[i] = self.population[i].fitness if self.population[i].fitness > self.population[i].bestFitness: self.population[i].bestFitness = self.population[i].fitness self.population[i].bestIndex = copy.deepcopy( self.population[i].chrom)
def update(self): ''' 更新粒子群 ''' for i in range(0, self.sizepop): self.population[i].velocity = self.params[0] * self.population[i].velocity + self.params[1] * np.random.random(self.vardim) * (self.population[i].bestPosition - self.population[i].chrom) + self.params[2] * np.random.random(self.vardim) * (self.best.chrom - self.population[i].chrom) self.population[i].chrom = self.population[i].chrom + self.population[i].velocity
def printResult(self): ''' 绘制粒子群算法优化结果 ''' x = np.arange(0, self.MAXGEN) y1 = self.history[:, 0] y2 = self.history[:, 1] plt.plot(x, y1, 'r', label='optimal value') plt.plot(x, y2, 'g', label='average value') plt.xlabel("Iteration") plt.ylabel("function value") plt.title("Particle Swarm Optimization algorithm for function optimization") plt.legend() plt.show()
def main(self): ''' 主流程:粒子群优化过程 ''' self.t = 0 self.initialize() self.evaluation() best = np.max(self.fitness) bestIndex = np.argmax(self.fitness) self.best = copy.deepcopy(self.population[bestIndex]) self.avefitness = np.mean(self.fitness) self.history[self.t, 0] = (1 - self.best.fitness) / self.best.fitness self.history[self.t, 1] = (1 - self.avefitness) / self.avefitness print("Generation %d: optimal function value is: %f; average function value is %f" % (self.t, self.history[self.t, 0], self.history[self.t, 1])) while self.t < self.MAXGEN - 1: self.t += 1 self.update() self.evaluation() best = np.max(self.fitness) bestIndex = np.argmax(self.fitness) if best > self.best.fitness: self.best = copy.deepcopy(self.population[bestIndex]) self.avefitness = np.mean(self.fitness) self.history[self.t, 0] = (1 - self.best.fitness) / self.best.fitness self.history[self.t, 1] = (1 - self.avefitness) / self.avefitness print("Generation %d: optimal function value is: %f; average function value is %f" % (self.t, self.history[self.t, 0], self.history[self.t, 1]))
print("Optimal function value is: %f; " % self.history[self.t, 0]) print("Optimal solution is:") print(self.best.chrom) self.printResult()
if __name__ == "__main__": bound = np.tile([[-600], [600]], 25) pso = PSO(60, 25, bound, 1000, [0.7298, 1.4962, 1.4962]) pso.main()
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