FAIndividual.py
1 import numpy as np 2 import ObjFunction 3 4 5 class FAIndividual: 6 7 ''' 8 individual of firefly algorithm 9 ''' 10 11 def __init__(self, vardim, bound): 12 ''' 13 vardim: dimension of variables 14 bound: boundaries of variables 15 ''' 16 self.vardim = vardim 17 self.bound = bound 18 self.fitness = 0. 19 self.trials = 0 20 21 def generate(self): 22 ''' 23 generate a random chromsome for firefly algorithm 24 ''' 25 len = self.vardim 26 rnd = np.random.random(size=len) 27 self.chrom = np.zeros(len) 28 for i in xrange(0, len): 29 self.chrom[i] = self.bound[0, i] + 30 (self.bound[1, i] - self.bound[0, i]) * rnd[i] 31 32 def calculateFitness(self): 33 ''' 34 calculate the fitness of the chromsome 35 ''' 36 self.fitness = ObjFunction.GrieFunc( 37 self.vardim, self.chrom, self.bound)
FA.py
1 import numpy as np 2 from FAIndividual import FAIndividual 3 import random 4 import copy 5 import matplotlib.pyplot as plt 6 7 8 class FireflyAlgorithm: 9 10 ''' 11 The class for firefly algorithm 12 ''' 13 14 def __init__(self, sizepop, vardim, bound, MAXGEN, params): 15 ''' 16 sizepop: population sizepop 17 vardim: dimension of variables 18 bound: boundaries of variables 19 MAXGEN: termination condition 20 param: algorithm required parameters, it is a list which is consisting of [beta0, gamma, alpha] 21 ''' 22 self.sizepop = sizepop 23 self.MAXGEN = MAXGEN 24 self.vardim = vardim 25 self.bound = bound 26 self.population = [] 27 self.fitness = np.zeros((self.sizepop, 1)) 28 self.trace = np.zeros((self.MAXGEN, 2)) 29 self.params = params 30 31 def initialize(self): 32 ''' 33 initialize the population 34 ''' 35 for i in xrange(0, self.sizepop): 36 ind = FAIndividual(self.vardim, self.bound) 37 ind.generate() 38 self.population.append(ind) 39 40 def evaluate(self): 41 ''' 42 evaluation of the population fitnesses 43 ''' 44 for i in xrange(0, self.sizepop): 45 self.population[i].calculateFitness() 46 self.fitness[i] = self.population[i].fitness 47 48 def solve(self): 49 ''' 50 evolution process of firefly algorithm 51 ''' 52 self.t = 0 53 self.initialize() 54 self.evaluate() 55 best = np.max(self.fitness) 56 bestIndex = np.argmax(self.fitness) 57 self.best = copy.deepcopy(self.population[bestIndex]) 58 self.avefitness = np.mean(self.fitness) 59 self.trace[self.t, 0] = (1 - self.best.fitness) / self.best.fitness 60 self.trace[self.t, 1] = (1 - self.avefitness) / self.avefitness 61 print("Generation %d: optimal function value is: %f; average function value is %f" % ( 62 self.t, self.trace[self.t, 0], self.trace[self.t, 1])) 63 while (self.t < self.MAXGEN - 1): 64 self.t += 1 65 self.move() 66 self.evaluate() 67 best = np.max(self.fitness) 68 bestIndex = np.argmax(self.fitness) 69 if best > self.best.fitness: 70 self.best = copy.deepcopy(self.population[bestIndex]) 71 self.avefitness = np.mean(self.fitness) 72 self.trace[self.t, 0] = (1 - self.best.fitness) / self.best.fitness 73 self.trace[self.t, 1] = (1 - self.avefitness) / self.avefitness 74 print("Generation %d: optimal function value is: %f; average function value is %f" % ( 75 self.t, self.trace[self.t, 0], self.trace[self.t, 1])) 76 77 print("Optimal function value is: %f; " % 78 self.trace[self.t, 0]) 79 print "Optimal solution is:" 80 print self.best.chrom 81 self.printResult() 82 83 def move(self): 84 ''' 85 move the a firefly to another brighter firefly 86 ''' 87 for i in xrange(0, self.sizepop): 88 for j in xrange(0, self.sizepop): 89 if self.fitness[j] > self.fitness[i]: 90 r = np.linalg.norm( 91 self.population[i].chrom - self.population[j].chrom) 92 beta = self.params[0] * 93 np.exp(-1 * self.params[1] * (r ** 2)) 94 # beta = 1 / (1 + self.params[1] * r) 95 # print beta 96 self.population[i].chrom += beta * (self.population[j].chrom - self.population[ 97 i].chrom) + self.params[2] * np.random.uniform(low=-1, high=1, size=self.vardim) 98 for k in xrange(0, self.vardim): 99 if self.population[i].chrom[k] < self.bound[0, k]: 100 self.population[i].chrom[k] = self.bound[0, k] 101 if self.population[i].chrom[k] > self.bound[1, k]: 102 self.population[i].chrom[k] = self.bound[1, k] 103 self.population[i].calculateFitness() 104 self.fitness[i] = self.population[i].fitness 105 106 def printResult(self): 107 ''' 108 plot the result of the firefly algorithm 109 ''' 110 x = np.arange(0, self.MAXGEN) 111 y1 = self.trace[:, 0] 112 y2 = self.trace[:, 1] 113 plt.plot(x, y1, 'r', label='optimal value') 114 plt.plot(x, y2, 'g', label='average value') 115 plt.xlabel("Iteration") 116 plt.ylabel("function value") 117 plt.title("Firefly Algorithm for function optimization") 118 plt.legend() 119 plt.show()
运行程序：
1 if __name__ == "__main__": 2 3 bound = np.tile([[-600], [600]], 25) 4 fa = FA(60, 25, bound, 200, [1.0, 0.000001, 0.6]) 5 fa.solve()
ObjFunction见简单遗传算法-python实现。