和声搜索算法-python实现
HSIndividual.py1 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 = self.bound + \
29 (self.bound - self.bound) * rnd
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)
HS.py
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
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.calculateFitness()
47 self.fitness = self.population.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:
57 if random.random() < self.params:
58 ind.chrom += self.best.chrom
59 else:
60 worstIdx = np.argmin(self.fitness)
61 xr = 2 * self.best.chrom - \
62 self.population.chrom
63 if xr < self.bound:
64 xr = self.bound
65 if xr > self.bound:
66 xr = self.bound
67 ind.chrom = self.population.chrom[
68 i] + (xr - self.population.chrom) * random.random()
69 else:
70 ind.chrom = self.bound[
71 0, i] + (self.bound - self.bound) * 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.fitness:
81 self.population = ind
82 self.fitness = 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)
94 self.avefitness = np.mean(self.fitness)
95 self.trace = (1 - self.best.fitness) / self.best.fitness
96 self.trace = (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.trace))
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)
107 self.avefitness = np.mean(self.fitness)
108 self.trace = (1 - self.best.fitness) / self.best.fitness
109 self.trace = (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.trace))
112 print("Optimal function value is: %f; " % self.trace)
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()
运行程序：
1 if __name__ == "__main__":
2
3 bound = np.tile([[-600], ], 25)
4 hs = HS(60, 25, bound, 5000, )
5 hs.solve()
ObjFunction见简单遗传算法-python实现。
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