人工免疫算法-python实现

AIAIndividual.py

 import numpy as np
 import ObjFunction

 class AIAIndividual:

     '''
     individual of artificial immune 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
         self.concentration = 0

     def generate(self):
         '''
         generate a random chromsome for artificial immune 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)

AIA.py

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

 class ArtificialImmuneAlgorithm:

     '''
     The class for artificial immune algorithm
     '''

     def __init__(self, sizepop, sizemem, vardim, bound, MAXGEN, params):
         '''
         sizepop: population sizepop
         vardim: dimension of variables
         bound: boundaries of variables
         MAXGEN: termination condition
         params: algorithm required parameters, it is a list which is consisting of [mutation rate, cloneNum]
         '''
         self.sizepop = sizepop
         self.sizemem = sizemem
         self.MAXGEN = MAXGEN
         self.vardim = vardim
         self.bound = bound
         self.population = []
         self.clonePopulation = []
         self.memories = []
         self.cloneMemories = []
         self.popFitness = np.zeros(self.sizepop)
         self.popCloneFitness = np.zeros(
             int(self.sizepop * self.sizepop * params[1]))
         self.memfitness = np.zero(self.sizemem)
         self.memClonefitness = np.zero(
             int(self.sizemem * self.sizemem * params[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 = AIAIndividual(self.vardim, self.bound)
             ind.generate()
             self.population.append(ind)
         for i in xrange(0, self.sizemem):
             ind = AIAIndividual(self.vardim, self.bound)
             ind.generate()
             self.memories.append(ind)

     def evaluatePopulation(self, flag):
         '''
         evaluation of the population fitnesses
         '''
         if flag == 1:
             for i in xrange(0, self.sizepop):
                 self.population[i].calculateFitness()
                 self.popFitness[i] = self.population[i].fitness
         else:
             for i in xrange(0, self.sizemem):
                 self.memories[i].calculateFitness()
                 self.memfitness[i] = self.memories[i].fitness

     def evaluateClone(self, flag):
         '''
         evaluation of the clone fitnesses
         '''
         if flag == 1:
             for i in xrange(0, self.sizepop):
                 self.clonePopulation[i].calculateFitness()
                 self.popCloneFitness[i] = self.clonePopulation[i].fitness
         else:
             for i in xrange(0, self.sizemem):
                 self.cloneMemories[i].calculateFitness()
                 self.memClonefitness[i] = self.cloneMemories[i].fitness

     def solve(self):
         '''
         evolution process of artificial immune algorithm
         '''
         self.t = 0
         self.initialize()
         self.best = AIAIndividual(self.vardim, self.bound)
         while (self.t < self.MAXGEN):
             # evolution of population
             self.cloneOperation(1)
             self.mutationOperation(1)
             self.evaluatePopulation(1)
             self.selectionOperation(1)

             # evolution of memories
             self.cloneOperation(2)
             self.mutationOperation(2)
             self.evaluatePopulation()
             self.selectionOperation(2)

             best = np.max(self.popFitness)
             bestIndex = np.argmax(self.popFitness)
             if best > self.best.fitness:
                 self.best = copy.deepcopy(self.population[bestIndex])
             self.avefitness = np.mean(self.popFitness)
             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]))
             self.t += 1

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

     def cloneOperation(self, individuals):
         '''
         clone operation for alforithm immune algorithm
         '''
         newpop = []
         sizeInds = len(individuals)
         for i in xrange(0, sizeInds):
             for j in xrange(0, int(self.params[1] * sizeInds)):
                 newpop.append(copy.deepcopy(individuals[i]))
         return newpop

     def selectionOperation(self, flag):
         '''
         selection operation for artificial immune algorithm
         '''
         if flag == 1:
             sortedIdx = np.argsort(-self.clonefit)
             for i in xrange(0, int(self.sizepop*self.sizepop*self.params[1]):
             tmpInd = individuals[sortedIdx[i]]
             if tmpInd.fitness > self.population[i].fitness:
                 self.population[i] = tmpInd
                 self.popFitness[i] = tmpInd.fitness
         else:
             pass
         newpop = []
         sizeInds = len(individuals)
         fitness = np.zeros(sizeInds)
         for i in xrange(0, sizeInds):
             fitness[i] = individuals[i].fitness
         sortedIdx = np.argsort(-fitness)
         for i in xrange(0, sizeInds):
             tmpInd = individuals[sortedIdx[i]]
             if tmpInd.fitness > self.population[i].fitness:
                 self.population[i] = tmpInd
                 self.popFitness[i] = tmpInd.fitness

     def mutationOperation(self, individuals):
         '''
         mutation operation for artificial immune algorithm
         '''
         newpop = []
         sizeInds = len(individuals)
         for i in xrange(0, sizeInds):
             newpop.append(copy.deepcopy(individuals[i]))
             r = random.random()
             if r < self.params[0]:
                 mutatePos = random.randint(0, self.vardim - 1)
                 theta = random.random()
                 if theta > 0.5:
                     newpop[i].chrom[mutatePos] = newpop[i].chrom[
                         mutatePos] - (newpop[i].chrom[mutatePos] - self.bound[0, mutatePos]) * (1 - random.random() ** (1 - self.t / self.MAXGEN))
                 else:
                     newpop[i].chrom[mutatePos] = newpop[i].chrom[
                         mutatePos] + (self.bound[1, mutatePos] - newpop[i].chrom[mutatePos]) * (1 - random.random() ** (1 - self.t / self.MAXGEN))
                 for k in xrange(0, self.vardim):
                     if newpop.chrom[mutatePos] < self.bound[0, mutatePos]:
                         newpop.chrom[mutatePos] = self.bound[0, mutatePos]
                     if newpop.chrom[mutatePos] > self.bound[1, mutatePos]:
                         newpop.chrom[mutatePos] = self.bound[1, mutatePos]
                 newpop.calculateFitness()
         return newpop

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

运行程序：

 if __name__ == "__main__":

     bound = np.tile([[-600], [600]], 25)
     aia = AIA(100, 25, bound, 100, [0.9, 0.1])
     aia.solve()

ObjFunction见简单遗传算法-python实现。