差分进化算法-python实现
DEIndividual.py1 import numpy as np
2 import ObjFunction
3
4
5 class DEIndividual:
6
7 '''
8 individual of differential evolution 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 differential evolution 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)
DE.py
1 import numpy as np
2 from DEIndividual import DEIndividual
3 import random
4 import copy
5 import matplotlib.pyplot as plt
6
7
8 class DifferentialEvolutionAlgorithm:
9
10 '''
11 The class for differential evolution 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 = DEIndividual(self.vardim, self.bound)
37 ind.generate()
38 self.population.append(ind)
39
40 def evaluate(self, x):
41 '''
42 evaluation of the population fitnesses
43 '''
44 x.calculateFitness()
45
46 def solve(self):
47 '''
48 evolution process of differential evolution algorithm
49 '''
50 self.t = 0
51 self.initialize()
52 for i in xrange(0, self.sizepop):
53 self.evaluate(self.population)
54 self.fitness = self.population.fitness
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 for i in xrange(0, self.sizepop):
66 vi = self.mutationOperation(i)
67 ui = self.crossoverOperation(i, vi)
68 xi_next = self.selectionOperation(i, ui)
69 self.population = xi_next
70 for i in xrange(0, self.sizepop):
71 self.evaluate(self.population)
72 self.fitness = self.population.fitness
73 best = np.max(self.fitness)
74 bestIndex = np.argmax(self.fitness)
75 if best > self.best.fitness:
76 self.best = copy.deepcopy(self.population)
77 self.avefitness = np.mean(self.fitness)
78 self.trace = (1 - self.best.fitness) / self.best.fitness
79 self.trace = (1 - self.avefitness) / self.avefitness
80 print("Generation %d: optimal function value is: %f; average function value is %f" % (
81 self.t, self.trace, self.trace))
82
83 print("Optimal function value is: %f; " %
84 self.trace)
85 print "Optimal solution is:"
86 print self.best.chrom
87 self.printResult()
88
89 def selectionOperation(self, i, ui):
90 '''
91 selection operation for differential evolution algorithm
92 '''
93 xi_next = copy.deepcopy(self.population)
94 xi_next.chrom = ui
95 self.evaluate(xi_next)
96 if xi_next.fitness > self.population.fitness:
97 return xi_next
98 else:
99 return self.population
100
101 def crossoverOperation(self, i, vi):
102 '''
103 crossover operation for differential evolution algorithm
104 '''
105 k = np.random.random_integers(0, self.vardim - 1)
106 ui = np.zeros(self.vardim)
107 for j in xrange(0, self.vardim):
108 pick = random.random()
109 if pick < self.params or j == k:
110 ui = vi
111 else:
112 ui = self.population.chrom
113 return ui
114
115 def mutationOperation(self, i):
116 '''
117 mutation operation for differential evolution algorithm
118 '''
119 a = np.random.random_integers(0, self.sizepop - 1)
120 while a == i:
121 a = np.random.random_integers(0, self.sizepop - 1)
122 b = np.random.random_integers(0, self.sizepop - 1)
123 while b == i or b == a:
124 b = np.random.random_integers(0, self.sizepop - 1)
125 c = np.random.random_integers(0, self.sizepop - 1)
126 while c == i or c == b or c == a:
127 c = np.random.random_integers(0, self.sizepop - 1)
128 vi = self.population.chrom + self.params * \
129 (self.population.chrom - self.population.chrom)
130 for j in xrange(0, self.vardim):
131 if vi < self.bound:
132 vi = self.bound
133 if vi > self.bound:
134 vi = self.bound
135 return vi
136
137 def printResult(self):
138 '''
139 plot the result of the differential evolution algorithm
140 '''
141 x = np.arange(0, self.MAXGEN)
142 y1 = self.trace[:, 0]
143 y2 = self.trace[:, 1]
144 plt.plot(x, y1, 'r', label='optimal value')
145 plt.plot(x, y2, 'g', label='average value')
146 plt.xlabel("Iteration")
147 plt.ylabel("function value")
148 plt.title("Differential Evolution Algorithm for function optimization")
149 plt.legend()
150 plt.show()
运行程序：
1 if __name__ == "__main__":
2
3 bound = np.tile([[-600], ], 25)
4 dea = DEA(60, 25, bound, 1000, )
5 dea.solve()
ObjFunction见简单遗传算法-python实现。
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