和声搜索算法-python实现

 1 import numpy as np
 2 import ObjFunction
 3 
 4 
 5 class HSIndividual:
 6 
 7     ‘‘‘
 8     individual of harmony search algorithm
 9     ‘‘‘
10 
11     def __init__(self,  vardim, bound):
12         ‘‘‘
13         vardim: dimension of variables
14         bound: boundaries of variables
15         ‘‘‘
16         self.vardim = vardim
17         self.bound = bound
18         self.fitness = 0.
19 
20     def generate(self):
21         ‘‘‘
22         generate a random chromsome for harmony search algorithm
23         ‘‘‘
24         len = self.vardim
25         rnd = np.random.random(size=len)
26         self.chrom = np.zeros(len)
27         for i in xrange(0, len):
28             self.chrom[i] = self.bound[0, i] + 29                 (self.bound[1, i] - self.bound[0, i]) * rnd[i]
30 
31     def calculateFitness(self):
32         ‘‘‘
33         calculate the fitness of the chromsome
34         ‘‘‘
35         self.fitness = ObjFunction.GrieFunc(
36             self.vardim, self.chrom, self.bound)

  1 import numpy as np
  2 from HSIndividual import HSIndividual
  3 import random
  4 import copy
  5 import math
  6 import matplotlib.pyplot as plt
  7 
  8 
  9 class HarmonySearch:
 10 
 11     ‘‘‘
 12     the class for harmony search algorithm
 13     ‘‘‘
 14 
 15     def __init__(self, sizepop, vardim, bound, MAXGEN, params):
 16         ‘‘‘
 17         sizepop: population sizepop
 18         vardim: dimension of variables
 19         bound: boundaries of variables
 20         MAXGEN: termination condition
 21         params: algorithm required parameters, it is a list which is consisting of[HMCR, PAR]
 22         ‘‘‘
 23         self.sizepop = sizepop
 24         self.vardim = vardim
 25         self.bound = bound
 26         self.MAXGEN = MAXGEN
 27         self.params = params
 28         self.population = []
 29         self.fitness = np.zeros((self.sizepop, 1))
 30         self.trace = np.zeros((self.MAXGEN, 2))
 31 
 32     def initialize(self):
 33         ‘‘‘
 34         initialize the population of hs
 35         ‘‘‘
 36         for i in xrange(0, self.sizepop):
 37             ind = HSIndividual(self.vardim, self.bound)
 38             ind.generate()
 39             self.population.append(ind)
 40 
 41     def evaluation(self):
 42         ‘‘‘
 43         evaluation the fitness of the population
 44         ‘‘‘
 45         for i in xrange(0, self.sizepop):
 46             self.population[i].calculateFitness()
 47             self.fitness[i] = self.population[i].fitness
 48 
 49     def improvise(self):
 50         ‘‘‘
 51         improvise a new harmony
 52         ‘‘‘
 53         ind = HSIndividual(self.vardim, self.bound)
 54         ind.chrom = np.zeros(self.vardim)
 55         for i in xrange(0, self.vardim):
 56             if random.random() < self.params[0]:
 57                 if random.random() < self.params[1]:
 58                     ind.chrom[i] += self.best.chrom[i]
 59                 else:
 60                     worstIdx = np.argmin(self.fitness)
 61                     xr = 2 * self.best.chrom[i] -  62                         self.population[worstIdx].chrom[i]
 63                     if xr < self.bound[0, i]:
 64                         xr = self.bound[0, i]
 65                     if xr > self.bound[1, i]:
 66                         xr = self.bound[1, i]
 67                     ind.chrom[i] = self.population[worstIdx].chrom[
 68                         i] + (xr - self.population[worstIdx].chrom[i]) * random.random()
 69             else:
 70                 ind.chrom[i] = self.bound[
 71                     0, i] + (self.bound[1, i] - self.bound[0, i]) * random.random()
 72         ind.calculateFitness()
 73         return ind
 74 
 75     def update(self, ind):
 76         ‘‘‘
 77         update harmony memory
 78         ‘‘‘
 79         minIdx = np.argmin(self.fitness)
 80         if ind.fitness > self.population[minIdx].fitness:
 81             self.population[minIdx] = ind
 82             self.fitness[minIdx] = ind.fitness
 83 
 84     def solve(self):
 85         ‘‘‘
 86         the evolution process of the hs algorithm
 87         ‘‘‘
 88         self.t = 0
 89         self.initialize()
 90         self.evaluation()
 91         best = np.max(self.fitness)
 92         bestIndex = np.argmax(self.fitness)
 93         self.best = copy.deepcopy(self.population[bestIndex])
 94         self.avefitness = np.mean(self.fitness)
 95         self.trace[self.t, 0] = (1 - self.best.fitness) / self.best.fitness
 96         self.trace[self.t, 1] = (1 - self.avefitness) / self.avefitness
 97         print("Generation %d: optimal function value is: %f; average function value is %f" % (
 98             self.t, self.trace[self.t, 0], self.trace[self.t, 1]))
 99         while self.t < self.MAXGEN - 1:
100             self.t += 1
101             ind = self.improvise()
102             self.update(ind)
103             best = np.max(self.fitness)
104             bestIndex = np.argmax(self.fitness)
105             if best > self.best.fitness:
106                 self.best = copy.deepcopy(self.population[bestIndex])
107             self.avefitness = np.mean(self.fitness)
108             self.trace[self.t, 0] = (1 - self.best.fitness) / self.best.fitness
109             self.trace[self.t, 1] = (1 - self.avefitness) / self.avefitness
110             print("Generation %d: optimal function value is: %f; average function value is %f" % (
111                 self.t, self.trace[self.t, 0], self.trace[self.t, 1]))
112         print("Optimal function value is: %f; " % self.trace[self.t, 0])
113         print "Optimal solution is:"
114         print self.best.chrom
115         self.printResult()
116 
117     def printResult(self):
118         ‘‘‘
119         plot the result of abs algorithm
120         ‘‘‘
121         x = np.arange(0, self.MAXGEN)
122         y1 = self.trace[:, 0]
123         y2 = self.trace[:, 1]
124         plt.plot(x, y1, ‘r‘, label=‘optimal value‘)
125         plt.plot(x, y2, ‘g‘, label=‘average value‘)
126         plt.xlabel("Iteration")
127         plt.ylabel("function value")
128         plt.title("Harmony search algorithm for function optimization")
129         plt.legend()
130         plt.show()

1 if __name__ == "__main__":
2 
3     bound = np.tile([[-600], [600]], 25)
4     hs = HS(60, 25, bound, 5000, [0.9950, 0.4])
5     hs.solve()