和声搜索算法-python实现

HSIndividual.py

 import numpy as np
 import ObjFunction

 class HSIndividual:

     '''
     individual of harmony search algorithm
     '''

     def __init__(self,  vardim, bound):
         '''
         vardim: dimension of variables
         bound: boundaries of variables
         '''
         self.vardim = vardim
         self.bound = bound
         self.fitness = 0.

     def generate(self):
         '''
         generate a random chromsome for harmony search 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)

HS.py

 import numpy as np
 from HSIndividual import HSIndividual
 import random
 import copy
 import math
 import matplotlib.pyplot as plt

 class HarmonySearch:

     '''
     the class for harmony search algorithm
     '''

     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[HMCR, PAR]
         '''
         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))

     def initialize(self):
         '''
         initialize the population of hs
         '''
         for i in xrange(0, self.sizepop):
             ind = HSIndividual(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 improvise(self):
         '''
         improvise a new harmony
         '''
         ind = HSIndividual(self.vardim, self.bound)
         ind.chrom = np.zeros(self.vardim)
         for i in xrange(0, self.vardim):
             if random.random() < self.params[0]:
                 if random.random() < self.params[1]:
                     ind.chrom[i] += self.best.chrom[i]
                 else:
                     worstIdx = np.argmin(self.fitness)
                     xr = 2 * self.best.chrom[i] - \
                         self.population[worstIdx].chrom[i]
                     if xr < self.bound[0, i]:
                         xr = self.bound[0, i]
                     if xr > self.bound[1, i]:
                         xr = self.bound[1, i]
                     ind.chrom[i] = self.population[worstIdx].chrom[
                         i] + (xr - self.population[worstIdx].chrom[i]) * random.random()
             else:
                 ind.chrom[i] = self.bound[
                     0, i] + (self.bound[1, i] - self.bound[0, i]) * random.random()
         ind.calculateFitness()
         return ind

     def update(self, ind):
         '''
         update harmony memory
         '''
         minIdx = np.argmin(self.fitness)
         if ind.fitness > self.population[minIdx].fitness:
             self.population[minIdx] = ind
             self.fitness[minIdx] = ind.fitness

     def solve(self):
         '''
         the evolution process of the hs algorithm
         '''
         self.t = 0
         self.initialize()
         self.evaluation()
         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
             ind = self.improvise()
             self.update(ind)
             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 printResult(self):
         '''
         plot the result of abs 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("Harmony search algorithm for function optimization")
         plt.legend()
         plt.show()

运行程序：

 if __name__ == "__main__":

     bound = np.tile([[-600], [600]], 25)
     hs = HS(60, 25, bound, 5000, [0.9950, 0.4])
     hs.solve()

ObjFunction见简单遗传算法-python实现。