萤火虫算法-python实现
FAIndividual.py1 import numpy as np
2 import ObjFunction
3
4
5 class FAIndividual:
6
7 '''
8 individual of firefly 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 firefly algorithm
24 '''
25 len = self.vardim
26 rnd = np.random.random(size=len)
27 self.chrom = np.zeros(len)
28 for i in xrange(0, len):
29 self.chrom = self.bound + \
30 (self.bound - self.bound) * rnd
31
32 def calculateFitness(self):
33 '''
34 calculate the fitness of the chromsome
35 '''
36 self.fitness = ObjFunction.GrieFunc(
37 self.vardim, self.chrom, self.bound)
FA.py
1 import numpy as np
2 from FAIndividual import FAIndividual
3 import random
4 import copy
5 import matplotlib.pyplot as plt
6
7
8 class FireflyAlgorithm:
9
10 '''
11 The class for firefly 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 param: algorithm required parameters, it is a list which is consisting of
21 '''
22 self.sizepop = sizepop
23 self.MAXGEN = MAXGEN
24 self.vardim = vardim
25 self.bound = bound
26 self.population = []
27 self.fitness = np.zeros((self.sizepop, 1))
28 self.trace = np.zeros((self.MAXGEN, 2))
29 self.params = params
30
31 def initialize(self):
32 '''
33 initialize the population
34 '''
35 for i in xrange(0, self.sizepop):
36 ind = FAIndividual(self.vardim, self.bound)
37 ind.generate()
38 self.population.append(ind)
39
40 def evaluate(self):
41 '''
42 evaluation of the population fitnesses
43 '''
44 for i in xrange(0, self.sizepop):
45 self.population.calculateFitness()
46 self.fitness = self.population.fitness
47
48 def solve(self):
49 '''
50 evolution process of firefly algorithm
51 '''
52 self.t = 0
53 self.initialize()
54 self.evaluate()
55 best = np.max(self.fitness)
56 bestIndex = np.argmax(self.fitness)
57 self.best = copy.deepcopy(self.population)
58 self.avefitness = np.mean(self.fitness)
59 self.trace = (1 - self.best.fitness) / self.best.fitness
60 self.trace = (1 - self.avefitness) / self.avefitness
61 print("Generation %d: optimal function value is: %f; average function value is %f" % (
62 self.t, self.trace, self.trace))
63 while (self.t < self.MAXGEN - 1):
64 self.t += 1
65 self.move()
66 self.evaluate()
67 best = np.max(self.fitness)
68 bestIndex = np.argmax(self.fitness)
69 if best > self.best.fitness:
70 self.best = copy.deepcopy(self.population)
71 self.avefitness = np.mean(self.fitness)
72 self.trace = (1 - self.best.fitness) / self.best.fitness
73 self.trace = (1 - self.avefitness) / self.avefitness
74 print("Generation %d: optimal function value is: %f; average function value is %f" % (
75 self.t, self.trace, self.trace))
76
77 print("Optimal function value is: %f; " %
78 self.trace)
79 print "Optimal solution is:"
80 print self.best.chrom
81 self.printResult()
82
83 def move(self):
84 '''
85 move the a firefly to another brighter firefly
86 '''
87 for i in xrange(0, self.sizepop):
88 for j in xrange(0, self.sizepop):
89 if self.fitness > self.fitness:
90 r = np.linalg.norm(
91 self.population.chrom - self.population.chrom)
92 beta = self.params * \
93 np.exp(-1 * self.params * (r ** 2))
94 # beta = 1 / (1 + self.params * r)
95 # print beta
96 self.population.chrom += beta * (self.population.chrom - self.population[
97 i].chrom) + self.params * np.random.uniform(low=-1, high=1, size=self.vardim)
98 for k in xrange(0, self.vardim):
99 if self.population.chrom < self.bound:
100 self.population.chrom = self.bound
101 if self.population.chrom > self.bound:
102 self.population.chrom = self.bound
103 self.population.calculateFitness()
104 self.fitness = self.population.fitness
105
106 def printResult(self):
107 '''
108 plot the result of the firefly algorithm
109 '''
110 x = np.arange(0, self.MAXGEN)
111 y1 = self.trace[:, 0]
112 y2 = self.trace[:, 1]
113 plt.plot(x, y1, 'r', label='optimal value')
114 plt.plot(x, y2, 'g', label='average value')
115 plt.xlabel("Iteration")
116 plt.ylabel("function value")
117 plt.title("Firefly Algorithm for function optimization")
118 plt.legend()
119 plt.show()
运行程序：
1 if __name__ == "__main__":
2
3 bound = np.tile([[-600], ], 25)
4 fa = FA(60, 25, bound, 200, )
5 fa.solve()
ObjFunction见简单遗传算法-python实现。
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