蝙蝠算法-python实现
BAIndividual.py1 import numpy as np
2 import ObjFunction
3
4
5 class BAIndividual:
6
7 '''
8 individual of bat 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 self.trials = 0
20
21 def generate(self):
22 '''
23 generate a random chromsome for bat algorithm
24 '''
25 len = self.vardim
26 rnd = np.random.random(size=len)
27 self.chrom = np.zeros(len)
28 self.velocity = np.random.random(size=len)
29 for i in xrange(0, len):
30 self.chrom = self.bound + \
31 (self.bound - self.bound) * rnd
32
33 def calculateFitness(self):
34 '''
35 calculate the fitness of the chromsome
36 '''
37 self.fitness = ObjFunction.GrieFunc(
38 self.vardim, self.chrom, self.bound)
BA.py
1 import numpy as np
2 from BAIndividual import BAIndividual
3 import random
4 import copy
5 import matplotlib.pyplot as plt
6
7
8 class BatAlgorithm:
9
10 '''
11 the class for bat algorithm
12 '''
13
14 def __init__(self, sizepop, vardim, bound, MAXGEN, params):
15 '''
16 sizepop: population sizepop
17 vardim: dimension of variables
18 bound: boundaries of variables
19 MAXGEN: termination condition
20 params: algorithm required parameters, it is a list which is consisting of
21 '''
22 self.sizepop = sizepop
23 self.vardim = vardim
24 self.bound = bound
25 self.MAXGEN = MAXGEN
26 self.params = params
27 self.population = []
28 self.fitness = np.zeros(self.sizepop)
29 self.freq = np.zeros(self.sizepop)
30 self.loudness = np.zeros(self.sizepop)
31 self.emissionrate = np.zeros(self.sizepop)
32 self.initEmissionrate = np.zeros(self.sizepop)
33 self.trace = np.zeros((self.MAXGEN, 2))
34
35 def initialize(self):
36 '''
37 initialize the population of ba
38 '''
39 for i in xrange(0, self.sizepop):
40 ind = BAIndividual(self.vardim, self.bound)
41 ind.generate()
42 self.population.append(ind)
43 self.freq = self.params + \
44 (self.params - self.params) * np.random.random(1)
45 self.loudness = self.params + \
46 (self.params - self.params) * np.random.random(1)
47 self.initEmissionrate = np.random.random(1)
48 self.emissionrate = self.initEmissionrate
49
50 def evaluation(self):
51 '''
52 evaluation the fitness of the population
53 '''
54 for i in xrange(0, self.sizepop):
55 self.population.calculateFitness()
56 self.fitness = self.population.fitness
57
58 def solve(self):
59 '''
60 the evolution process of the bat algorithm
61 '''
62 self.t = 0
63 self.initialize()
64 self.evaluation()
65 bestIndex = np.argmax(self.fitness)
66 self.best = copy.deepcopy(self.population)
67 while self.t < self.MAXGEN:
68 self.t += 1
69 self.update()
70 # idx = self.select()
71 self.evaluation()
72 best = np.max(self.fitness)
73 bestIndex = np.argmax(self.fitness)
74 if best > self.best.fitness:
75 self.best = copy.deepcopy(self.population)
76
77 self.avefitness = np.mean(self.fitness)
78 self.trace = \
79 (1 - self.best.fitness) / self.best.fitness
80 self.trace = (1 - self.avefitness) / self.avefitness
81 print("Generation %d: optimal function value is: %f; average function value is %f" % (
82 self.t, self.trace, self.trace))
83 print("Optimal function value is: %f; " % self.trace)
84 print "Optimal solution is:"
85 print self.best.chrom
86 self.printResult()
87
88 def update(self):
89 '''
90 update the population
91 '''
92 for i in xrange(0, self.sizepop):
93 self.freq = self.params + \
94 (self.params - self.params) * np.random.random(1)
95 self.population[
96 i].velocity += (self.best.chrom - self.population.chrom) * self.freq
97
98 self.population.chrom += self.population.velocity
99 for k in xrange(0, self.vardim):
100 if self.population.chrom < self.bound:
101 self.population.chrom = self.bound
102 if self.population.chrom > self.bound:
103 self.population.chrom = self.bound
104 rnd = np.random.random(1)
105 A = np.mean(self.emissionrate)
106 tmpInd = copy.deepcopy(self.best)
107 if rnd > self.emissionrate:
108 tmpInd.chrom += np.random.uniform(low=-1,
109 high=1.0, size=self.vardim) * A
110 for k in xrange(0, self.vardim):
111 if tmpInd.chrom < self.bound:
112 tmpInd.chrom = self.bound
113 if tmpInd.chrom > self.bound:
114 tmpInd.chrom = self.bound
115 tmpInd.calculateFitness()
116 if tmpInd.fitness > self.best.fitness and random.random() < self.loudness:
117 self.population = tmpInd
118 self.loudness *= self.params
119 self.emissionrate = self.initEmissionrate[
120 i] * (1 - np.exp(self.params * self.t))
121 if tmpInd.fitness > self.best.fitness:
122 self.best = copy.deepcopy(tmpInd)
123
124 def selectOne(self):
125 '''
126 select one individual from the population
127 '''
128 totalFitness = np.sum(self.fitness)
129 accuFitness = np.zeros(self.sizepop)
130
131 sum1 = 0.
132 for i in xrange(0, self.sizepop):
133 accuFitness = sum1 + self.fitness / totalFitness
134 sum1 = accuFitness
135
136 r = random.random()
137 idx = 0
138 for j in xrange(0, self.sizepop - 1):
139 if j == 0 and r < accuFitness:
140 idx = 0
141 break
142 elif r >= accuFitness and r < accuFitness:
143 idx = j + 1
144 break
145 return idx
146
147 def printResult(self):
148 '''
149 plot the result of bat algorithm
150 '''
151 x = np.arange(0, self.MAXGEN)
152 y1 = self.trace[:, 0]
153 y2 = self.trace[:, 1]
154 plt.plot(x, y1, 'r', label='optimal value')
155 plt.plot(x, y2, 'g', label='average value')
156 plt.xlabel("Iteration")
157 plt.ylabel("function value")
158 plt.title("Bat algorithm for function optimization")
159 plt.legend()
160 plt.show()
运行程序：
1 if __name__ == "__main__":
2
3 bound = np.tile([[-600], ], 25)
4 ba = BA(60, 25, bound, 1000, )
5 ba.solve()
ObjFunction见简单遗传算法-python实现。
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