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| import numpy as np import random import copy import matplotlib.pyplot as plt import math
class BFOIndividual: ''' 模拟细菌个体 ''' 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. self.trials = 0.
def generate(self): ''' 生成随机性 ''' len = self.vardim rnd = np.random.random(size=len) self.chrom = np.zeros(len) for i in range(0, len): self.chrom[i] = self.bound[0, i] + (self.bound[1, i] - self.bound[0, i]) * rnd[i]
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): ''' 计算适应度 ''' self.fitness = self.GrieFunc(self.vardim, self.chrom, self.bound)
class BFO: ''' 菌群觅食算法 ''' def __init__(self, sizepop, vardim, bound, params): ''' sizepop: 种群规模 vardim: 变量维数 bound: 变量取值范围 param: 其他参数(列表) ''' self.sizepop = sizepop self.vardim = vardim self.bound = bound self.bestPopulation = [] self.accuFitness = np.zeros(self.sizepop) self.population = [] self.fitness = np.zeros(self.sizepop) self.params = params self.history = np.zeros((self.params[0] * self.params[1] * self.params[2], 2))
def initialize(self): ''' 初始化菌群 ''' for i in range(0, self.sizepop): ind = BFOIndividual(self.vardim, self.bound) ind.generate() self.population.append(ind)
def evaluation(self): ''' 迭代 ''' for i in range(0, self.sizepop): self.population[i].calculateFitness() self.fitness[i] = self.population[i].fitness
def sortPopulation(self): ''' 种群排序 ''' sortedIdx = np.argsort(self.accuFitness) newpop = [] newFitness = np.zeros(self.sizepop) for i in range(0, self.sizepop): ind = self.population[sortedIdx[i]] newpop.append(ind) self.fitness[i] = ind.fitness self.population = newpop
def chemotaxls(self): ''' 趋化行为 ''' for i in range(0, self.sizepop): tmpInd = copy.deepcopy(self.population[i]) self.fitness[i] += self.communication(tmpInd) Jlast = self.fitness[i] rnd = np.random.uniform(low=-1, high=1.0, size=self.vardim) phi = rnd / np.linalg.norm(rnd) tmpInd.chrom += self.params[4] * phi for k in range(0, self.vardim): if tmpInd.chrom[k] < self.bound[0, k]: tmpInd.chrom[k] = self.bound[0, k] if tmpInd.chrom[k] > self.bound[1, k]: tmpInd.chrom[k] = self.bound[1, k] tmpInd.calculateFitness() m = 0 while m < self.params[3]: if tmpInd.fitness < Jlast: Jlast = tmpInd.fitness self.population[i] = copy.deepcopy(tmpInd) tmpInd.fitness += self.communication(tmpInd) tmpInd.chrom += self.params[4] * phi for k in range(0, self.vardim): if tmpInd.chrom[k] < self.bound[0, k]: tmpInd.chrom[k] = self.bound[0, k] if tmpInd.chrom[k] > self.bound[1, k]: tmpInd.chrom[k] = self.bound[1, k] tmpInd.calculateFitness() m += 1 else: m = self.params[3] self.fitness[i] = Jlast self.accuFitness[i] += Jlast
def communication(self, ind): ''' 细菌间交流行为 ''' Jcc = 0.0 term1 = 0.0 term2 = 0.0 for j in range(0, self.sizepop): term = 0.0 for k in range(0, self.vardim): term += (ind.chrom[k] - self.population[j].chrom[k]) ** 2 term1 -= self.params[6] * np.exp(-1 * self.params[7] * term) term2 += self.params[8] * np.exp(-1 * self.params[9] * term) Jcc = term1 + term2
return Jcc
def reproduction(self): ''' 繁殖行为 ''' self.sortPopulation() newpop = [] for i in range(0, self.sizepop // 2): newpop.append(self.population[i]) for i in range(self.sizepop // 2, self.sizepop): self.population[i] = newpop[i - self.sizepop // 2]
def eliminationAndDispersal(self): ''' 分散与淘汰 ''' for i in range(0, self.sizepop): rnd = random.random() if rnd < self.params[5]: self.population[i].generate()
def printResult(self): ''' 绘制菌群迭代过程 ''' x = np.arange(0, self.t) 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("Baterial clony foraging algorithm for function optimization") plt.legend() plt.show()
def main(self): ''' 主流程:蜂群搜索过程 ''' self.t = 0 self.initialize() self.evaluation() bestIndex = np.argmin(self.fitness) self.best = copy.deepcopy(self.population[bestIndex]) for i in range(0, self.params[0]): for j in range(0, self.params[1]): for k in range(0, self.params[2]): self.t += 1 self.chemotaxls() self.evaluation() best = np.min(self.fitness) bestIndex = np.argmin(self.fitness) if best < self.best.fitness: self.best = copy.deepcopy(self.population[bestIndex]) self.avefitness = np.mean(self.fitness) self.history[self.t - 1, 0] = self.best.fitness self.history[self.t - 1, 1] = self.avefitness print("Generation %d: optimal function value is: %f; average function value is %f" % (self.t, self.history[self.t - 1, 0], self.history[self.t - 1, 1])) self.reproduction() self.eliminationAndDispersal()
print("Optimal function value is: %f; " % self.history[self.t - 1, 0]) print("Optimal solution is:") print(self.best.chrom) self.printResult()
if __name__ == "__main__": bound = np.tile([[-600], [600]], 25) bfo = BFO(60, 25, bound, [2, 2, 50, 4, 50, 0.25, 0.1, 0.2, 0.1, 10]) bfo.main()
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