人工蜂群算法-python实现

ABSIndividual.py

import numpy as np
import ObjFunction


class 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 np
from ABSIndividual import ABSIndividual
import random
import copy
import matplotlib.pyplot as plt


class 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] = vi
                self.fitness[fi] = vi.fitness
                if vi.fitness > self.best.fitness:
                    self.best = vi
            vi.calculateFitness()
            if vi.fitness > self.fitness[i]:
                self.population[i] = vi
                self.fitness[i] = vi.fitness
                if 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 = 0
                self.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()

 运行程序：

if __name__ == "__main__":

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

 

ObjFunction见简单遗传算法-python实现。