ABSIndividual.py

import numpy as npimport ObjFunctionclass ABSIndividual: ’’’ individual of artificial bee swarm algorithm ’’’ def __init__(self, vardim, bound): ’’’ vardim: dimension of variables bound: boundaries of variables ’’’ self.vardim = vardim self.bound = bound self.fitness = 0. self.trials = 0 def generate(self): ’’’ generate a random chromsome for artificial bee swarm algorithm ’’’ len = self.vardim rnd = np.random.random(size=len) self.chrom = np.zeros(len) for i in xrange(0, len): self.chrom[i] = self.bound[0, i] + (self.bound[1, i] - self.bound[0, i]) * rnd[i] def calculateFitness(self): ’’’ calculate the fitness of the chromsome ’’’ self.fitness = ObjFunction.GrieFunc( self.vardim, self.chrom, self.bound)

ABS.py

import numpy as npfrom ABSIndividual import ABSIndividualimport randomimport copyimport matplotlib.pyplot as pltclass ArtificialBeeSwarm: ’’’ the class for artificial bee swarm algorithm ’’’ def __init__(self, sizepop, vardim, bound, MAXGEN, params): ’’’ sizepop: population sizepop vardim: dimension of variables bound: boundaries of variables MAXGEN: termination condition params: algorithm required parameters, it is a list which is consisting of[trailLimit, C] ’’’ self.sizepop = sizepop self.vardim = vardim self.bound = bound self.foodSource = self.sizepop / 2 self.MAXGEN = MAXGEN self.params = params self.population = [] self.fitness = np.zeros((self.sizepop, 1)) self.trace = np.zeros((self.MAXGEN, 2)) def initialize(self): ’’’ initialize the population of abs ’’’ for i in xrange(0, self.foodSource): ind = ABSIndividual(self.vardim, self.bound) ind.generate() self.population.append(ind) def evaluation(self): ’’’ evaluation the fitness of the population ’’’ for i in xrange(0, self.foodSource): self.population[i].calculateFitness() self.fitness[i] = self.population[i].fitness def employedBeePhase(self): ’’’ employed bee phase ’’’ for i in xrange(0, self.foodSource): k = np.random.random_integers(0, self.vardim - 1) j = np.random.random_integers(0, self.foodSource - 1) while j == i:j = np.random.random_integers(0, self.foodSource - 1) vi = copy.deepcopy(self.population[i]) # vi.chrom = vi.chrom + np.random.uniform(-1, 1, self.vardim) * ( # vi.chrom - self.population[j].chrom) + np.random.uniform(0.0, self.params[1], self.vardim) * (self.best.chrom - vi.chrom) # for k in xrange(0, self.vardim): # if vi.chrom[k] < self.bound[0, k]: # vi.chrom[k] = self.bound[0, k] # if vi.chrom[k] > self.bound[1, k]: # vi.chrom[k] = self.bound[1, k] vi.chrom[k] += np.random.uniform(low=-1, high=1.0, size=1) * (vi.chrom[k] - self.population[j].chrom[k]) if vi.chrom[k] < self.bound[0, k]:vi.chrom[k] = self.bound[0, k] if vi.chrom[k] > self.bound[1, k]:vi.chrom[k] = self.bound[1, k] vi.calculateFitness() if vi.fitness > self.fitness[fi]:self.population[fi] = viself.fitness[fi] = vi.fitnessif vi.fitness > self.best.fitness: self.best = vi vi.calculateFitness() if vi.fitness > self.fitness[i]:self.population[i] = viself.fitness[i] = vi.fitnessif vi.fitness > self.best.fitness: self.best = vi else:self.population[i].trials += 1 def onlookerBeePhase(self): ’’’ onlooker bee phase ’’’ accuFitness = np.zeros((self.foodSource, 1)) maxFitness = np.max(self.fitness) for i in xrange(0, self.foodSource): accuFitness[i] = 0.9 * self.fitness[i] / maxFitness + 0.1 for i in xrange(0, self.foodSource): for fi in xrange(0, self.foodSource):r = random.random()if r < accuFitness[i]: k = np.random.random_integers(0, self.vardim - 1) j = np.random.random_integers(0, self.foodSource - 1) while j == fi: j = np.random.random_integers(0, self.foodSource - 1) vi = copy.deepcopy(self.population[fi]) # vi.chrom = vi.chrom + np.random.uniform(-1, 1, self.vardim) * ( # vi.chrom - self.population[j].chrom) + np.random.uniform(0.0, self.params[1], self.vardim) * (self.best.chrom - vi.chrom) # for k in xrange(0, self.vardim): # if vi.chrom[k] < self.bound[0, k]: # vi.chrom[k] = self.bound[0, k] # if vi.chrom[k] > self.bound[1, k]: # vi.chrom[k] = self.bound[1, k] vi.chrom[ k] += np.random.uniform(low=-1, high=1.0, size=1) * (vi.chrom[k] - self.population[j].chrom[k]) if vi.chrom[k] < self.bound[0, k]: vi.chrom[k] = self.bound[0, k] if vi.chrom[k] > self.bound[1, k]: vi.chrom[k] = self.bound[1, k] vi.calculateFitness() if vi.fitness > self.fitness[fi]: self.population[fi] = vi self.fitness[fi] = vi.fitness if vi.fitness > self.best.fitness: self.best = vi else: self.population[fi].trials += 1 break def scoutBeePhase(self): ’’’ scout bee phase ’’’ for i in xrange(0, self.foodSource): if self.population[i].trials > self.params[0]:self.population[i].generate()self.population[i].trials = 0self.population[i].calculateFitness()self.fitness[i] = self.population[i].fitness def solve(self): ’’’ the evolution process of the abs algorithm ’’’ 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.trace[self.t, 0] = (1 - self.best.fitness) / self.best.fitness self.trace[self.t, 1] = (1 - self.avefitness) / self.avefitness print('Generation %d: optimal function value is: %f; average function value is %f' % ( self.t, self.trace[self.t, 0], self.trace[self.t, 1])) while self.t < self.MAXGEN - 1: self.t += 1 self.employedBeePhase() self.onlookerBeePhase() self.scoutBeePhase() 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.trace[self.t, 0] = (1 - self.best.fitness) / self.best.fitness self.trace[self.t, 1] = (1 - self.avefitness) / self.avefitness print('Generation %d: optimal function value is: %f; average function value is %f' % (self.t, self.trace[self.t, 0], self.trace[self.t, 1])) print('Optimal function value is: %f; ' % self.trace[self.t, 0]) print 'Optimal solution is:' print self.best.chrom self.printResult() def printResult(self): ’’’ plot the result of abs algorithm ’’’ x = np.arange(0, self.MAXGEN) y1 = self.trace[:, 0] y2 = self.trace[:, 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('Artificial Bee Swarm algorithm for function optimization') plt.legend() plt.show()

運(yùn)行程序：

if __name__ == '__main__': bound = np.tile([[-600], [600]], 25) abs = ABS(60, 25, bound, 1000, [100, 0.5]) abs.solve()

ObjFunction見(jiàn)簡(jiǎn)單遺傳算法-python實(shí)現(xiàn)。

以上就是python實(shí)現(xiàn)人工蜂群算法的詳細(xì)內(nèi)容，更多關(guān)于python 人工蜂群算法的資料請(qǐng)關(guān)注好吧啦網(wǎng)其它相關(guān)文章！