进化策略-python实现

ESIndividual.py

 import numpy as np
 import ObjFunction

 class ESIndividual:

     '''
     individual of evolutionary strategy
     '''

     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 evolutionary strategy
         '''
         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)

ES.py

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

 class EvolutionaryStrategy:

     '''
     the class for evolutionary strategy
     '''

     def __init__(self, sizepop, 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[delta_max, delta_min]
         '''
         self.sizepop = sizepop
         self.vardim = vardim
         self.bound = bound
         self.MAXGEN = MAXGEN
         self.params = params
         self.population = []
         self.fitness = np.zeros(self.sizepop)
         self.trace = np.zeros((self.MAXGEN, 2))

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

     def evaluation(self):
         '''
         evaluation the fitness of the population
         '''
         for i in xrange(0, self.sizepop):
             self.population[i].calculateFitness()
             self.fitness[i] = self.population[i].fitness

     def solve(self):
         '''
         the evolution process of the evolutionary strategy
         '''
         self.t = 0
         self.initialize()
         self.evaluation()
         bestIndex = np.argmax(self.fitness)
         self.best = copy.deepcopy(self.population[bestIndex])
         while self.t < self.MAXGEN:
             self.t += 1
             tmpPop = self.mutation()
             self.selection(tmpPop)
             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 - 1, 0] = \
                 (1 - self.best.fitness) / self.best.fitness
             self.trace[self.t - 1, 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 - 1, 0], self.trace[self.t - 1, 1]))
         print("Optimal function value is: %f; " % self.trace[self.t - 1, 0])
         print "Optimal solution is:"
         print self.best.chrom
         self.printResult()

     def mutation(self):
         '''
         mutate the population by a random normal distribution
         '''
         tmpPop = []
         for i in xrange(0, self.sizepop):
             ind = copy.deepcopy(self.population[i])
             delta = self.params[0] + self.t * \
                 (self.params[1] - self.params[0]) / self.MAXGEN
             ind.chrom += np.random.normal(0.0, delta, self.vardim)
             for k in xrange(0, self.vardim):
                 if ind.chrom[k] < self.bound[0, k]:
                     ind.chrom[k] = self.bound[0, k]
                 if ind.chrom[k] > self.bound[1, k]:
                     ind.chrom[k] = self.bound[1, k]
             ind.calculateFitness()
             tmpPop.append(ind)
         return tmpPop

     def selection(self, tmpPop):
         '''
         update the population
         '''
         for i in xrange(0, self.sizepop):
             if self.fitness[i] < tmpPop[i].fitness:
                 self.population[i] = tmpPop[i]
                 self.fitness[i] = tmpPop[i].fitness

     def printResult(self):
         '''
         plot the result of evolutionary strategy
         '''
         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("Evolutionary strategy for function optimization")
         plt.legend()
         plt.show()

运行程序：

 if __name__ == "__main__":

     bound = np.tile([[-600], [600]], 25)
     es = ES(60, 25, bound, 1000, [10, 1])
     es.solve()

ObjFunction见简单遗传算法-python实现。