AFSIndividual.py
1 import numpy as np 2 import ObjFunction 3 import copy 4 5 6 class AFSIndividual: 7 8 """class for AFSIndividual""" 9 10 def __init__(self, vardim, bound): 11 ''' 12 vardim: dimension of variables 13 bound: boundaries of variables 14 ''' 15 self.vardim = vardim 16 self.bound = bound 17 18 def generate(self): 19 ''' 20 generate a rondom chromsome 21 ''' 22 len = self.vardim 23 rnd = np.random.random(size=len) 24 self.chrom = np.zeros(len) 25 self.velocity = np.random.random(size=len) 26 for i in xrange(0, len): 27 self.chrom[i] = self.bound[0, i] + 28 (self.bound[1, i] - self.bound[0, i]) * rnd[i] 29 self.bestPosition = np.zeros(len) 30 self.bestFitness = 0. 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)
AFS.py
1 import numpy as np 2 from AFSIndividual import AFSIndividual 3 import random 4 import copy 5 import matplotlib.pyplot as plt 6 7 8 class ArtificialFishSwarm: 9 10 """class for ArtificialFishSwarm""" 11 12 def __init__(self, sizepop, vardim, bound, MAXGEN, params): 13 ''' 14 sizepop: population sizepop 15 vardim: dimension of variables 16 bound: boundaries of variables, 2*vardim 17 MAXGEN: termination condition 18 params: algorithm required parameters, it is a list which is consisting of[visual, step, delta, trynum] 19 ''' 20 self.sizepop = sizepop 21 self.vardim = vardim 22 self.bound = bound 23 self.MAXGEN = MAXGEN 24 self.params = params 25 self.population = [] 26 self.fitness = np.zeros((self.sizepop, 1)) 27 self.trace = np.zeros((self.MAXGEN, 2)) 28 self.lennorm = 6000 29 30 def initialize(self): 31 ''' 32 initialize the population of afs 33 ''' 34 for i in xrange(0, self.sizepop): 35 ind = AFSIndividual(self.vardim, self.bound) 36 ind.generate() 37 self.population.append(ind) 38 39 def evaluation(self, x): 40 ''' 41 evaluation the fitness of the individual 42 ''' 43 x.calculateFitness() 44 45 def forage(self, x): 46 ''' 47 artificial fish foraging behavior 48 ''' 49 newInd = copy.deepcopy(x) 50 found = False 51 for i in xrange(0, self.params[3]): 52 indi = self.randSearch(x, self.params[0]) 53 if indi.fitness > x.fitness: 54 newInd.chrom = x.chrom + np.random.random(self.vardim) * self.params[1] * self.lennorm * ( 55 indi.chrom - x.chrom) / np.linalg.norm(indi.chrom - x.chrom) 56 newInd = indi 57 found = True 58 break 59 if not (found): 60 newInd = self.randSearch(x, self.params[1]) 61 return newInd 62 63 def randSearch(self, x, searLen): 64 ''' 65 artificial fish random search behavior 66 ''' 67 ind = copy.deepcopy(x) 68 ind.chrom += np.random.uniform(-1, 1, 69 self.vardim) * searLen * self.lennorm 70 for j in xrange(0, self.vardim): 71 if ind.chrom[j] < self.bound[0, j]: 72 ind.chrom[j] = self.bound[0, j] 73 if ind.chrom[j] > self.bound[1, j]: 74 ind.chrom[j] = self.bound[1, j] 75 self.evaluation(ind) 76 return ind 77 78 def huddle(self, x): 79 ''' 80 artificial fish huddling behavior 81 ''' 82 newInd = copy.deepcopy(x) 83 dist = self.distance(x) 84 index = [] 85 for i in xrange(1, self.sizepop): 86 if dist[i] > 0 and dist[i] < self.params[0] * self.lennorm: 87 index.append(i) 88 nf = len(index) 89 if nf > 0: 90 xc = np.zeros(self.vardim) 91 for i in xrange(0, nf): 92 xc += self.population[index[i]].chrom 93 xc = xc / nf 94 cind = AFSIndividual(self.vardim, self.bound) 95 cind.chrom = xc 96 cind.calculateFitness() 97 if (cind.fitness / nf) > (self.params[2] * x.fitness): 98 xnext = x.chrom + np.random.random( 99 self.vardim) * self.params[1] * self.lennorm * (xc - x.chrom) / np.linalg.norm(xc - x.chrom) 100 for j in xrange(0, self.vardim): 101 if xnext[j] < self.bound[0, j]: 102 xnext[j] = self.bound[0, j] 103 if xnext[j] > self.bound[1, j]: 104 xnext[j] = self.bound[1, j] 105 newInd.chrom = xnext 106 self.evaluation(newInd) 107 # print "hudding" 108 return newInd 109 else: 110 return self.forage(x) 111 else: 112 return self.forage(x) 113 114 def follow(self, x): 115 ''' 116 artificial fish following behivior 117 ''' 118 newInd = copy.deepcopy(x) 119 dist = self.distance(x) 120 index = [] 121 for i in xrange(1, self.sizepop): 122 if dist[i] > 0 and dist[i] < self.params[0] * self.lennorm: 123 index.append(i) 124 nf = len(index) 125 if nf > 0: 126 best = -999999999. 127 bestIndex = 0 128 for i in xrange(0, nf): 129 if self.population[index[i]].fitness > best: 130 best = self.population[index[i]].fitness 131 bestIndex = index[i] 132 if (self.population[bestIndex].fitness / nf) > (self.params[2] * x.fitness): 133 xnext = x.chrom + np.random.random( 134 self.vardim) * self.params[1] * self.lennorm * (self.population[bestIndex].chrom - x.chrom) / np.linalg.norm(self.population[bestIndex].chrom - x.chrom) 135 for j in xrange(0, self.vardim): 136 if xnext[j] < self.bound[0, j]: 137 xnext[j] = self.bound[0, j] 138 if xnext[j] > self.bound[1, j]: 139 xnext[j] = self.bound[1, j] 140 newInd.chrom = xnext 141 self.evaluation(newInd) 142 # print "follow" 143 return newInd 144 else: 145 return self.forage(x) 146 else: 147 return self.forage(x) 148 149 def solve(self): 150 ''' 151 evolution process for afs algorithm 152 ''' 153 self.t = 0 154 self.initialize() 155 # evaluation the population 156 for i in xrange(0, self.sizepop): 157 self.evaluation(self.population[i]) 158 self.fitness[i] = self.population[i].fitness 159 best = np.max(self.fitness) 160 bestIndex = np.argmax(self.fitness) 161 self.best = copy.deepcopy(self.population[bestIndex]) 162 self.avefitness = np.mean(self.fitness) 163 self.trace[self.t, 0] = (1 - self.best.fitness) / self.best.fitness 164 self.trace[self.t, 1] = (1 - self.avefitness) / self.avefitness 165 print("Generation %d: optimal function value is: %f; average function value is %f" % ( 166 self.t, self.trace[self.t, 0], self.trace[self.t, 1])) 167 while self.t < self.MAXGEN - 1: 168 self.t += 1 169 # newpop = [] 170 for i in xrange(0, self.sizepop): 171 xi1 = self.huddle(self.population[i]) 172 xi2 = self.follow(self.population[i]) 173 if xi1.fitness > xi2.fitness: 174 self.population[i] = xi1 175 self.fitness[i] = xi1.fitness 176 else: 177 self.population[i] = xi2 178 self.fitness[i] = xi2.fitness 179 best = np.max(self.fitness) 180 bestIndex = np.argmax(self.fitness) 181 if best > self.best.fitness: 182 self.best = copy.deepcopy(self.population[bestIndex]) 183 self.avefitness = np.mean(self.fitness) 184 self.trace[self.t, 0] = (1 - self.best.fitness) / self.best.fitness 185 self.trace[self.t, 1] = (1 - self.avefitness) / self.avefitness 186 print("Generation %d: optimal function value is: %f; average function value is %f" % ( 187 self.t, self.trace[self.t, 0], self.trace[self.t, 1])) 188 189 print("Optimal function value is: %f; " % self.trace[self.t, 0]) 190 print "Optimal solution is:" 191 print self.best.chrom 192 self.printResult() 193 194 def distance(self, x): 195 ''' 196 return the distance array to a individual 197 ''' 198 dist = np.zeros(self.sizepop) 199 for i in xrange(0, self.sizepop): 200 dist[i] = np.linalg.norm(x.chrom - self.population[i].chrom) / 6000 201 return dist 202 203 def printResult(self): 204 ''' 205 plot the result of afs algorithm 206 ''' 207 x = np.arange(0, self.MAXGEN) 208 y1 = self.trace[:, 0] 209 y2 = self.trace[:, 1] 210 plt.plot(x, y1, 'r', label='optimal value') 211 plt.plot(x, y2, 'g', label='average value') 212 plt.xlabel("Iteration") 213 plt.ylabel("function value") 214 plt.title("Artificial Fish Swarm algorithm for function optimization") 215 plt.legend() 216 plt.show()
运行程序：
1 if __name__ == "__main__": 2 3 bound = np.tile([[-600], [600]], 25) 4 afs = AFS(60, 25, bound, 500, [0.001, 0.0001, 0.618, 40]) 5 afs.solve()
ObjFunction见简单遗传算法-python实现。