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。