CSAIndividual.py
1 import numpy as np 2 import ObjFunction 3 4 5 class CSAIndividual: 6 7 ''' 8 individual of clone selection 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 clone selection 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[i] = self.bound[0, i] + 30 (self.bound[1, i] - self.bound[0, i]) * rnd[i] 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)
CSA.py
1 import numpy as np 2 from CSAIndividual import CSAIndividual 3 import random 4 import copy 5 import matplotlib.pyplot as plt 6 7 8 class CloneSelectionAlgorithm: 9 10 ''' 11 the class for clone selection 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[beta, pm, alpha_max, alpha_min] 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.trace = np.zeros((self.MAXGEN, 2)) 30 31 def initialize(self): 32 ''' 33 initialize the population of ba 34 ''' 35 for i in xrange(0, self.sizepop): 36 ind = CSAIndividual(self.vardim, self.bound) 37 ind.generate() 38 self.population.append(ind) 39 40 def evaluation(self): 41 ''' 42 evaluation the fitness of the population 43 ''' 44 for i in xrange(0, self.sizepop): 45 self.population[i].calculateFitness() 46 self.fitness[i] = self.population[i].fitness 47 48 def solve(self): 49 ''' 50 the evolution process of the clone selection algorithm 51 ''' 52 self.t = 0 53 self.initialize() 54 self.evaluation() 55 bestIndex = np.argmax(self.fitness) 56 self.best = copy.deepcopy(self.population[bestIndex]) 57 while self.t < self.MAXGEN: 58 self.t += 1 59 tmpPop = self.reproduction() 60 tmpPop = self.mutation(tmpPop) 61 self.selection(tmpPop) 62 best = np.max(self.fitness) 63 bestIndex = np.argmax(self.fitness) 64 if best > self.best.fitness: 65 self.best = copy.deepcopy(self.population[bestIndex]) 66 67 self.avefitness = np.mean(self.fitness) 68 self.trace[self.t - 1, 0] = 69 (1 - self.best.fitness) / self.best.fitness 70 self.trace[self.t - 1, 1] = (1 - self.avefitness) / self.avefitness 71 print("Generation %d: optimal function value is: %f; average function value is %f" % ( 72 self.t, self.trace[self.t - 1, 0], self.trace[self.t - 1, 1])) 73 print("Optimal function value is: %f; " % self.trace[self.t - 1, 0]) 74 print "Optimal solution is:" 75 print self.best.chrom 76 self.printResult() 77 78 def reproduction(self): 79 ''' 80 reproduction 81 ''' 82 tmpPop = [] 83 for i in xrange(0, self.sizepop): 84 nc = int(self.params[1] * self.sizepop) 85 for j in xrange(0, nc): 86 ind = copy.deepcopy(self.population[i]) 87 tmpPop.append(ind) 88 return tmpPop 89 90 def mutation(self, tmpPop): 91 ''' 92 hypermutation 93 ''' 94 for i in xrange(0, self.sizepop): 95 nc = int(self.params[1] * self.sizepop) 96 for j in xrange(1, nc): 97 rnd = np.random.random(1) 98 if rnd < self.params[0]: 99 # alpha = self.params[ 100 # 2] + self.t * (self.params[3] - self.params[2]) / self.MAXGEN 101 delta = self.params[2] + self.t * 102 (self.params[3] - self.params[3]) / self.MAXGEN 103 tmpPop[i * nc + j].chrom += np.random.normal(0.0, delta, self.vardim) 104 # tmpPop[i * nc + j].chrom += alpha * np.random.random( 105 # self.vardim) * (self.best.chrom - tmpPop[i * nc + 106 # j].chrom) 107 for k in xrange(0, self.vardim): 108 if tmpPop[i * nc + j].chrom[k] < self.bound[0, k]: 109 tmpPop[i * nc + j].chrom[k] = self.bound[0, k] 110 if tmpPop[i * nc + j].chrom[k] > self.bound[1, k]: 111 tmpPop[i * nc + j].chrom[k] = self.bound[1, k] 112 tmpPop[i * nc + j].calculateFitness() 113 return tmpPop 114 115 def selection(self, tmpPop): 116 ''' 117 re-selection 118 ''' 119 for i in xrange(0, self.sizepop): 120 nc = int(self.params[1] * self.sizepop) 121 best = 0.0 122 bestIndex = -1 123 for j in xrange(0, nc): 124 if tmpPop[i * nc + j].fitness > best: 125 best = tmpPop[i * nc + j].fitness 126 bestIndex = i * nc + j 127 if self.fitness[i] < best: 128 self.population[i] = copy.deepcopy(tmpPop[bestIndex]) 129 self.fitness[i] = best 130 131 def printResult(self): 132 ''' 133 plot the result of clone selection algorithm 134 ''' 135 x = np.arange(0, self.MAXGEN) 136 y1 = self.trace[:, 0] 137 y2 = self.trace[:, 1] 138 plt.plot(x, y1, 'r', label='optimal value') 139 plt.plot(x, y2, 'g', label='average value') 140 plt.xlabel("Iteration") 141 plt.ylabel("function value") 142 plt.title("Clone selection algorithm for function optimization") 143 plt.legend() 144 plt.show()
运行程序：
1 if __name__ == "__main__": 2 3 bound = np.tile([[-600], [600]], 25) 4 csa = CSA(50, 25, bound, 500, [0.3, 0.4, 5, 0.1]) 5 csa.solve()
ObjFunction见简单遗传算法-python实现。