萤火虫算法-python实现

FAIndividual.py

 import numpy as np
 import ObjFunction

 class FAIndividual:

     '''
     individual of firefly 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 firefly 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)

FA.py

 import numpy as np
 from FAIndividual import FAIndividual
 import random
 import copy
 import matplotlib.pyplot as plt

 class FireflyAlgorithm:

     '''
     The class for firefly algorithm
     '''

     def __init__(self, sizepop, vardim, bound, MAXGEN, params):
         '''
         sizepop: population sizepop
         vardim: dimension of variables
         bound: boundaries of variables
         MAXGEN: termination condition
         param: algorithm required parameters, it is a list which is consisting of [beta0, gamma, alpha]
         '''
         self.sizepop = sizepop
         self.MAXGEN = MAXGEN
         self.vardim = vardim
         self.bound = bound
         self.population = []
         self.fitness = np.zeros((self.sizepop, 1))
         self.trace = np.zeros((self.MAXGEN, 2))
         self.params = params

     def initialize(self):
         '''
         initialize the population
         '''
         for i in xrange(0, self.sizepop):
             ind = FAIndividual(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 solve(self):
         '''
         evolution process of firefly algorithm
         '''
         self.t = 0
         self.initialize()
         self.evaluate()
         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.move()
             self.evaluate()
             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 move(self):
         '''
         move the a firefly to another brighter firefly
         '''
         for i in xrange(0, self.sizepop):
             for j in xrange(0, self.sizepop):
                 if self.fitness[j] > self.fitness[i]:
                     r = np.linalg.norm(
                         self.population[i].chrom - self.population[j].chrom)
                     beta = self.params[0] * \
                         np.exp(-1 * self.params[1] * (r ** 2))
                     # beta = 1 / (1 + self.params[1] * r)
                     # print beta
                     self.population[i].chrom += beta * (self.population[j].chrom - self.population[
                         i].chrom) + self.params[2] * np.random.uniform(low=-1, high=1, size=self.vardim)
                     for k in xrange(0, self.vardim):
                         if self.population[i].chrom[k] < self.bound[0, k]:
                             self.population[i].chrom[k] = self.bound[0, k]
                         if self.population[i].chrom[k] > self.bound[1, k]:
                             self.population[i].chrom[k] = self.bound[1, k]
                     self.population[i].calculateFitness()
                     self.fitness[i] = self.population[i].fitness

     def printResult(self):
         '''
         plot the result of the firefly 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("Firefly Algorithm for function optimization")
         plt.legend()
         plt.show()

运行程序：

 if __name__ == "__main__":

     bound = np.tile([[-600], [600]], 25)
     fa = FA(60, 25, bound, 200, [1.0, 0.000001, 0.6])
     fa.solve()

ObjFunction见简单遗传算法-python实现。