细菌觅食算法-python实现

BFOIndividual.py

 import numpy as np
 import ObjFunction

 class BFOIndividual:

     '''
     individual of baterial clony foraging 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 baterial clony foraging 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)
         s1 = 0.
         s2 = 1.
         for i in range(1, self.vardim + 1):
             s1 = s1 + self.chrom[i - 1] ** 2
             s2 = s2 * np.cos(self.chrom[i - 1] / np.sqrt(i))
         y = (1. / 4000.) * s1 - s2 + 1
         self.fitness = y

BFO.py

 import numpy as np
 from BFOIndividual import BFOIndividual
 import random
 import copy
 import matplotlib.pyplot as plt
 import math

 class BacterialForagingOptimization:

     '''
     The class for baterial foraging optimization algorithm
     '''

     def __init__(self, sizepop, vardim, bound, params):
         '''
         sizepop: population sizepop
         vardim: dimension of variables
         bound: boundaries of variables
         param: algorithm required parameters, it is a list which is consisting of [Ned, Nre, Nc, Ns, C, ped, d_attract, w_attract, d_repellant, w_repellant]
         '''
         self.sizepop = sizepop
         self.vardim = vardim
         self.bound = bound
         self.population = []
         self.bestPopulation = []
         self.accuFitness = np.zeros(self.sizepop)
         self.fitness = np.zeros(self.sizepop)
         self.params = params
         self.trace = np.zeros(
             (self.params[0] * self.params[1] * self.params[2], 2))

     def initialize(self):
         '''
         initialize the population
         '''
         for i in xrange(0, self.sizepop):
             ind = BFOIndividual(self.vardim, self.bound)
             ind.generate()
             self.population.append(ind)

     def evaluate(self):
         '''
         evaluation of the population fitnesses
         '''
         for i in xrange(0, self.sizepop):
             self.population[i].calculateFitness()
             self.fitness[i] = self.population[i].fitness

     def sortPopulation(self):
         '''
         sort population according descending order
         '''
         sortedIdx = np.argsort(self.accuFitness)
         newpop = []
         newFitness = np.zeros(self.sizepop)
         for i in xrange(0, self.sizepop):
             ind = self.population[sortedIdx[i]]
             newpop.append(ind)
             self.fitness[i] = ind.fitness
         self.population = newpop

     def solve(self):
         '''
         evolution process of baterial clony foraging algorithm
         '''
         self.t = 0
         self.initialize()
         self.evaluate()
         bestIndex = np.argmin(self.fitness)
         self.best = copy.deepcopy(self.population[bestIndex])

         for i in xrange(0, self.params[0]):
             for j in xrange(0, self.params[1]):
                 for k in xrange(0, self.params[2]):
                     self.t += 1
                     self.chemotaxls()
                     self.evaluate()
                     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.trace[self.t - 1, 0] = self.best.fitness
                     self.trace[self.t - 1, 1] = self.avefitness
                     print("Generation %d: optimal function value is: %f; average function value is %f" % (
                         self.t, self.trace[self.t - 1, 0], self.trace[self.t - 1, 1]))
                 self.reproduction()
             self.eliminationAndDispersal()

         print("Optimal function value is: %f; " %
               self.trace[self.t - 1, 0])
         print "Optimal solution is:"
         print self.best.chrom
         self.printResult()

     def chemotaxls(self):
         '''
         chemotaxls behavior of baterials
         '''
         for i in xrange(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 xrange(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)
                     # print m, Jlast
                     tmpInd.fitness += self.communication(tmpInd)
                     tmpInd.chrom += self.params[4] * phi
                     for k in xrange(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):
         '''
         cell to cell communication
         '''
         Jcc = 0.0
         term1 = 0.0
         term2 = 0.0
         for j in xrange(0, self.sizepop):
             term = 0.0
             for k in xrange(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):
         '''
         reproduction of baterials
         '''
         self.sortPopulation()
         newpop = []
         for i in xrange(0, self.sizepop / 2):
             newpop.append(self.population[i])
         for i in xrange(self.sizepop / 2, self.sizepop):
             self.population[i] = newpop[i - self.sizepop / 2]

     def eliminationAndDispersal(self):
         '''
         elimination and dispersal of baterials
         '''
         for i in xrange(0, self.sizepop):
             rnd = random.random()
             if rnd < self.params[5]:
                 self.population[i].generate()

     def printResult(self):
         '''
         plot the result of the baterial clony foraging algorithm
         '''
         x = np.arange(0, self.t)
         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(
             "Baterial clony foraging algorithm for function optimization")
         plt.legend()
         plt.show()

运行程序：

 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.solve()

ObjFunction见简单遗传算法-python实现。