人工鱼群算法-python实现

AFSIndividual.py

 import numpy as np
 import ObjFunction
 import copy

 class AFSIndividual:

     """class for AFSIndividual"""

     def __init__(self, vardim, bound):
         '''
         vardim: dimension of variables
         bound: boundaries of variables
         '''
         self.vardim = vardim
         self.bound = bound

     def generate(self):
         '''
         generate a rondom chromsome
         '''
         len = self.vardim
         rnd = np.random.random(size=len)
         self.chrom = np.zeros(len)
         self.velocity = np.random.random(size=len)
         for i in xrange(0, len):
             self.chrom[i] = self.bound[0, i] + \
                 (self.bound[1, i] - self.bound[0, i]) * rnd[i]
         self.bestPosition = np.zeros(len)
         self.bestFitness = 0.

     def calculateFitness(self):
         '''
         calculate the fitness of the chromsome
         '''
         self.fitness = ObjFunction.GrieFunc(
             self.vardim, self.chrom, self.bound)

AFS.py

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

 class ArtificialFishSwarm:

     """class for  ArtificialFishSwarm"""

     def __init__(self, sizepop, vardim, bound, MAXGEN, params):
         '''
         sizepop: population sizepop
         vardim: dimension of variables
         bound: boundaries of variables, 2*vardim
         MAXGEN: termination condition
         params: algorithm required parameters, it is a list which is consisting of[visual, step, delta, trynum]
         '''
         self.sizepop = sizepop
         self.vardim = vardim
         self.bound = bound
         self.MAXGEN = MAXGEN
         self.params = params
         self.population = []
         self.fitness = np.zeros((self.sizepop, 1))
         self.trace = np.zeros((self.MAXGEN, 2))
         self.lennorm = 6000

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

     def evaluation(self, x):
         '''
         evaluation the fitness of the individual
         '''
         x.calculateFitness()

     def forage(self, x):
         '''
         artificial fish foraging behavior
         '''
         newInd = copy.deepcopy(x)
         found = False
         for i in xrange(0, self.params[3]):
             indi = self.randSearch(x, self.params[0])
             if indi.fitness > x.fitness:
                 newInd.chrom = x.chrom + np.random.random(self.vardim) * self.params[1] * self.lennorm * (
                     indi.chrom - x.chrom) / np.linalg.norm(indi.chrom - x.chrom)
                 newInd = indi
                 found = True
                 break
         if not (found):
             newInd = self.randSearch(x, self.params[1])
         return newInd

     def randSearch(self, x, searLen):
         '''
         artificial fish random search behavior
         '''
         ind = copy.deepcopy(x)
         ind.chrom += np.random.uniform(-1, 1,
                                        self.vardim) * searLen * self.lennorm
         for j in xrange(0, self.vardim):
             if ind.chrom[j] < self.bound[0, j]:
                 ind.chrom[j] = self.bound[0, j]
             if ind.chrom[j] > self.bound[1, j]:
                 ind.chrom[j] = self.bound[1, j]
         self.evaluation(ind)
         return ind

     def huddle(self, x):
         '''
         artificial fish huddling behavior
         '''
         newInd = copy.deepcopy(x)
         dist = self.distance(x)
         index = []
         for i in xrange(1, self.sizepop):
             if dist[i] > 0 and dist[i] < self.params[0] * self.lennorm:
                 index.append(i)
         nf = len(index)
         if nf > 0:
             xc = np.zeros(self.vardim)
             for i in xrange(0, nf):
                 xc += self.population[index[i]].chrom
             xc = xc / nf
             cind = AFSIndividual(self.vardim, self.bound)
             cind.chrom = xc
             cind.calculateFitness()
             if (cind.fitness / nf) > (self.params[2] * x.fitness):
                 xnext = x.chrom + np.random.random(
                     self.vardim) * self.params[1] * self.lennorm * (xc - x.chrom) / np.linalg.norm(xc - x.chrom)
                 for j in xrange(0, self.vardim):
                     if xnext[j] < self.bound[0, j]:
                         xnext[j] = self.bound[0, j]
                     if xnext[j] > self.bound[1, j]:
                         xnext[j] = self.bound[1, j]
                 newInd.chrom = xnext
                 self.evaluation(newInd)
                 # print "hudding"
                 return newInd
             else:
                 return self.forage(x)
         else:
             return self.forage(x)

     def follow(self, x):
         '''
         artificial fish following behivior
         '''
         newInd = copy.deepcopy(x)
         dist = self.distance(x)
         index = []
         for i in xrange(1, self.sizepop):
             if dist[i] > 0 and dist[i] < self.params[0] * self.lennorm:
                 index.append(i)
         nf = len(index)
         if nf > 0:
             best = -999999999.
             bestIndex = 0
             for i in xrange(0, nf):
                 if self.population[index[i]].fitness > best:
                     best = self.population[index[i]].fitness
                     bestIndex = index[i]
             if (self.population[bestIndex].fitness / nf) > (self.params[2] * x.fitness):
                 xnext = x.chrom + np.random.random(
                     self.vardim) * self.params[1] * self.lennorm * (self.population[bestIndex].chrom - x.chrom) / np.linalg.norm(self.population[bestIndex].chrom - x.chrom)
                 for j in xrange(0, self.vardim):
                     if xnext[j] < self.bound[0, j]:
                         xnext[j] = self.bound[0, j]
                     if xnext[j] > self.bound[1, j]:
                         xnext[j] = self.bound[1, j]
                 newInd.chrom = xnext
                 self.evaluation(newInd)
                 # print "follow"
                 return newInd
             else:
                 return self.forage(x)
         else:
             return self.forage(x)

     def solve(self):
         '''
         evolution process for afs algorithm
         '''
         self.t = 0
         self.initialize()
         # evaluation the population
         for i in xrange(0, self.sizepop):
             self.evaluation(self.population[i])
             self.fitness[i] = self.population[i].fitness
         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
             # newpop = []
             for i in xrange(0, self.sizepop):
                 xi1 = self.huddle(self.population[i])
                 xi2 = self.follow(self.population[i])
                 if xi1.fitness > xi2.fitness:
                     self.population[i] = xi1
                     self.fitness[i] = xi1.fitness
                 else:
                     self.population[i] = xi2
                     self.fitness[i] = xi2.fitness
             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 distance(self, x):
         '''
         return the distance array to a individual
         '''
         dist = np.zeros(self.sizepop)
         for i in xrange(0, self.sizepop):
             dist[i] = np.linalg.norm(x.chrom - self.population[i].chrom) / 6000
         return dist

     def printResult(self):
         '''
         plot the result of afs 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 Fish Swarm algorithm for function optimization")
         plt.legend()
         plt.show()

运行程序：

 if __name__ == "__main__":

     bound = np.tile([[-600], [600]], 25)
     afs = AFS(60, 25, bound, 500, [0.001, 0.0001, 0.618, 40])
     afs.solve()

ObjFunction见简单遗传算法-python实现。