人工免疫算法-python实现
AIAIndividual.py
1 import numpy as np2 import ObjFunction
3
4
5 class AIAIndividual:
6
7 '''
8 individual of artificial immune 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 self.concentration = 0
21
22 def generate(self):
23 '''
24 generate a random chromsome for artificial immune algorithm
25 '''
26 len = self.vardim
27 rnd = np.random.random(size=len)
28 self.chrom = np.zeros(len)
29 for i in xrange(0, len):
30 self.chrom[i] = self.bound[0, i] + \
31 (self.bound[1, i] - self.bound[0, i]) * rnd[i]
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)
AIA.py
1 import numpy as np2 from AIAIndividual import AIAIndividual
3 import random
4 import copy
5 import matplotlib.pyplot as plt
6
7
8 class ArtificialImmuneAlgorithm:
9
10 '''
11 The class for artificial immune algorithm
12 '''
13
14 def __init__(self, sizepop, sizemem, 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 [mutation rate, cloneNum]
21 '''
22 self.sizepop = sizepop
23 self.sizemem = sizemem
24 self.MAXGEN = MAXGEN
25 self.vardim = vardim
26 self.bound = bound
27 self.population = []
28 self.clonePopulation = []
29 self.memories = []
30 self.cloneMemories = []
31 self.popFitness = np.zeros(self.sizepop)
32 self.popCloneFitness = np.zeros(
33 int(self.sizepop * self.sizepop * params[1]))
34 self.memfitness = np.zero(self.sizemem)
35 self.memClonefitness = np.zero(
36 int(self.sizemem * self.sizemem * params[1]))
37 self.trace = np.zeros((self.MAXGEN, 2))
38 self.params = params
39
40 def initialize(self):
41 '''
42 initialize the population
43 '''
44 for i in xrange(0, self.sizepop):
45 ind = AIAIndividual(self.vardim, self.bound)
46 ind.generate()
47 self.population.append(ind)
48 for i in xrange(0, self.sizemem):
49 ind = AIAIndividual(self.vardim, self.bound)
50 ind.generate()
51 self.memories.append(ind)
52
53 def evaluatePopulation(self, flag):
54 '''
55 evaluation of the population fitnesses
56 '''
57 if flag == 1:
58 for i in xrange(0, self.sizepop):
59 self.population[i].calculateFitness()
60 self.popFitness[i] = self.population[i].fitness
61 else:
62 for i in xrange(0, self.sizemem):
63 self.memories[i].calculateFitness()
64 self.memfitness[i] = self.memories[i].fitness
65
66 def evaluateClone(self, flag):
67 '''
68 evaluation of the clone fitnesses
69 '''
70 if flag == 1:
71 for i in xrange(0, self.sizepop):
72 self.clonePopulation[i].calculateFitness()
73 self.popCloneFitness[i] = self.clonePopulation[i].fitness
74 else:
75 for i in xrange(0, self.sizemem):
76 self.cloneMemories[i].calculateFitness()
77 self.memClonefitness[i] = self.cloneMemories[i].fitness
78
79 def solve(self):
80 '''
81 evolution process of artificial immune algorithm
82 '''
83 self.t = 0
84 self.initialize()
85 self.best = AIAIndividual(self.vardim, self.bound)
86 while (self.t < self.MAXGEN):
87 # evolution of population
88 self.cloneOperation(1)
89 self.mutationOperation(1)
90 self.evaluatePopulation(1)
91 self.selectionOperation(1)
92
93 # evolution of memories
94 self.cloneOperation(2)
95 self.mutationOperation(2)
96 self.evaluatePopulation()
97 self.selectionOperation(2)
98
99 best = np.max(self.popFitness)
100 bestIndex = np.argmax(self.popFitness)
101 if best > self.best.fitness:
102 self.best = copy.deepcopy(self.population[bestIndex])
103 self.avefitness = np.mean(self.popFitness)
104 self.trace[self.t, 0] = (1 - self.best.fitness) / self.best.fitness
105 self.trace[self.t, 1] = (1 - self.avefitness) / self.avefitness
106 print("Generation %d: optimal function value is: %f; average function value is %f" % (
107 self.t, self.trace[self.t, 0], self.trace[self.t, 1]))
108 self.t += 1
109
110 print("Optimal function value is: %f; " %
111 self.trace[self.t - 1, 0])
112 print "Optimal solution is:"
113 print self.best.chrom
114 self.printResult()
115
116 def cloneOperation(self, individuals):
117 '''
118 clone operation for alforithm immune algorithm
119 '''
120 newpop = []
121 sizeInds = len(individuals)
122 for i in xrange(0, sizeInds):
123 for j in xrange(0, int(self.params[1] * sizeInds)):
124 newpop.append(copy.deepcopy(individuals[i]))
125 return newpop
126
127 def selectionOperation(self, flag):
128 '''
129 selection operation for artificial immune algorithm
130 '''
131 if flag == 1:
132 sortedIdx = np.argsort(-self.clonefit)
133 for i in xrange(0, int(self.sizepop*self.sizepop*self.params[1]):
134 tmpInd = individuals[sortedIdx[i]]
135 if tmpInd.fitness > self.population[i].fitness:
136 self.population[i] = tmpInd
137 self.popFitness[i] = tmpInd.fitness
138 else:
139 pass
140 newpop = []
141 sizeInds = len(individuals)
142 fitness = np.zeros(sizeInds)
143 for i in xrange(0, sizeInds):
144 fitness[i] = individuals[i].fitness
145 sortedIdx = np.argsort(-fitness)
146 for i in xrange(0, sizeInds):
147 tmpInd = individuals[sortedIdx[i]]
148 if tmpInd.fitness > self.population[i].fitness:
149 self.population[i] = tmpInd
150 self.popFitness[i] = tmpInd.fitness
151
152 def mutationOperation(self, individuals):
153 '''
154 mutation operation for artificial immune algorithm
155 '''
156 newpop = []
157 sizeInds = len(individuals)
158 for i in xrange(0, sizeInds):
159 newpop.append(copy.deepcopy(individuals[i]))
160 r = random.random()
161 if r < self.params[0]:
162 mutatePos = random.randint(0, self.vardim - 1)
163 theta = random.random()
164 if theta > 0.5:
165 newpop[i].chrom[mutatePos] = newpop[i].chrom[
166 mutatePos] - (newpop[i].chrom[mutatePos] - self.bound[0, mutatePos]) * (1 - random.random() ** (1 - self.t / self.MAXGEN))
167 else:
168 newpop[i].chrom[mutatePos] = newpop[i].chrom[
169 mutatePos] + (self.bound[1, mutatePos] - newpop[i].chrom[mutatePos]) * (1 - random.random() ** (1 - self.t / self.MAXGEN))
170 for k in xrange(0, self.vardim):
171 if newpop.chrom[mutatePos] < self.bound[0, mutatePos]:
172 newpop.chrom[mutatePos] = self.bound[0, mutatePos]
173 if newpop.chrom[mutatePos] > self.bound[1, mutatePos]:
174 newpop.chrom[mutatePos] = self.bound[1, mutatePos]
175 newpop.calculateFitness()
176 return newpop
177
178 def printResult(self):
179 '''
180 plot the result of the artificial immune algorithm
181 '''
182 x = np.arange(0, self.MAXGEN)
183 y1 = self.trace[:, 0]
184 y2 = self.trace[:, 1]
185 plt.plot(x, y1, 'r', label='optimal value')
186 plt.plot(x, y2, 'g', label='average value')
187 plt.xlabel("Iteration")
188 plt.ylabel("function value")
189 plt.title("Artificial immune algorithm for function optimization")
190 plt.legend()
191 plt.show()
运行程序：
1 if __name__ == "__main__":2
3 bound = np.tile([[-600], [600]], 25)
4 aia = AIA(100, 25, bound, 100, [0.9, 0.1])
5 aia.solve()
ObjFunction见简单遗传算法-python实现。
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