蝙蝠算法-python实现
2015-10-06 22:37
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BAIndividual.py
BA.py
运行程序:
ObjFunction见简单遗传算法-python实现。
import numpy as np import ObjFunction class BAIndividual: ''' individual of bat algorithm ''' def __init__(self, vardim, bound): ''' vardim: dimension of variables bound: boundaries of variables ''' self.vardim = vardim self.bound = bound self.fitness = 0. self.trials = 0 def generate(self): ''' generate a random chromsome for bat algorithm ''' len = self.vardim rnd = np.random.random(size=len) self.chrom = np.zeros(len) self.velocity = np.random.random(size=len) for i in xrange(0, len): self.chrom[i] = self.bound[0, i] + \ (self.bound[1, i] - self.bound[0, i]) * rnd[i] def calculateFitness(self): ''' calculate the fitness of the chromsome ''' self.fitness = ObjFunction.GrieFunc( self.vardim, self.chrom, self.bound)
BA.py
import numpy as np from BAIndividual import BAIndividual import random import copy import matplotlib.pyplot as plt class BatAlgorithm: ''' the class for bat algorithm ''' def __init__(self, sizepop, vardim, bound, MAXGEN, params): ''' sizepop: population sizepop vardim: dimension of variables bound: boundaries of variables MAXGEN: termination condition params: algorithm required parameters, it is a list which is consisting of[fmax, fmin, Amax, Amin, alpha, gamma] ''' self.sizepop = sizepop self.vardim = vardim self.bound = bound self.MAXGEN = MAXGEN self.params = params self.population = [] self.fitness = np.zeros(self.sizepop) self.freq = np.zeros(self.sizepop) self.loudness = np.zeros(self.sizepop) self.emissionrate = np.zeros(self.sizepop) self.initEmissionrate = np.zeros(self.sizepop) self.trace = np.zeros((self.MAXGEN, 2)) def initialize(self): ''' initialize the population of ba ''' for i in xrange(0, self.sizepop): ind = BAIndividual(self.vardim, self.bound) ind.generate() self.population.append(ind) self.freq[i] = self.params[1] + \ (self.params[0] - self.params[1]) * np.random.random(1) self.loudness[i] = self.params[3] + \ (self.params[2] - self.params[3]) * np.random.random(1) self.initEmissionrate[i] = np.random.random(1) self.emissionrate[i] = self.initEmissionrate[i] def evaluation(self): ''' evaluation the fitness of the population ''' for i in xrange(0, self.sizepop): self.population[i].calculateFitness() self.fitness[i] = self.population[i].fitness def solve(self): ''' the evolution process of the bat algorithm ''' self.t = 0 self.initialize() self.evaluation() bestIndex = np.argmax(self.fitness) self.best = copy.deepcopy(self.population[bestIndex]) while self.t < self.MAXGEN: self.t += 1 self.update() # idx = self.select() self.evaluation() best = np.max(self.fitness) bestIndex = np.argmax(self.fitness) if best > self.best.fitness: self.best = copy.deepcopy(self.population[bestIndex]) self.avefitness = np.mean(self.fitness) self.trace[self.t - 1, 0] = \ (1 - self.best.fitness) / self.best.fitness self.trace[self.t - 1, 1] = (1 - self.avefitness) / self.avefitness print("Generation %d: optimal function value is: %f; average function value is %f" % ( self.t, self.trace[self.t - 1, 0], self.trace[self.t - 1, 1])) print("Optimal function value is: %f; " % self.trace[self.t - 1, 0]) print "Optimal solution is:" print self.best.chrom self.printResult() def update(self): ''' update the population ''' for i in xrange(0, self.sizepop): self.freq[i] = self.params[1] + \ (self.params[0] - self.params[1]) * np.random.random(1) self.population[ i].velocity += (self.best.chrom - self.population[i].chrom) * self.freq[i] self.population[i].chrom += self.population[i].velocity for k in xrange(0, self.vardim): if self.population[i].chrom[k] < self.bound[0, k]: self.population[i].chrom[k] = self.bound[0, k] if self.population[i].chrom[k] > self.bound[1, k]: self.population[i].chrom[k] = self.bound[1, k] rnd = np.random.random(1) A = np.mean(self.emissionrate) tmpInd = copy.deepcopy(self.best) if rnd > self.emissionrate[i]: tmpInd.chrom += np.random.uniform(low=-1, high=1.0, size=self.vardim) * A for k in xrange(0, self.vardim): if tmpInd.chrom[k] < self.bound[0, k]: tmpInd.chrom[k] = self.bound[0, k] if tmpInd.chrom[k] > self.bound[1, k]: tmpInd.chrom[k] = self.bound[1, k] tmpInd.calculateFitness() if tmpInd.fitness > self.best.fitness and random.random() < self.loudness[i]: self.population[i] = tmpInd self.loudness[i] *= self.params[4] self.emissionrate[i] = self.initEmissionrate[ i] * (1 - np.exp(self.params[5] * self.t)) if tmpInd.fitness > self.best.fitness: self.best = copy.deepcopy(tmpInd) def selectOne(self): ''' select one individual from the population ''' totalFitness = np.sum(self.fitness) accuFitness = np.zeros(self.sizepop) sum1 = 0. for i in xrange(0, self.sizepop): accuFitness[i] = sum1 + self.fitness[i] / totalFitness sum1 = accuFitness[i] r = random.random() idx = 0 for j in xrange(0, self.sizepop - 1): if j == 0 and r < accuFitness[j]: idx = 0 break elif r >= accuFitness[j] and r < accuFitness[j + 1]: idx = j + 1 break return idx def printResult(self): ''' plot the result of bat algorithm ''' x = np.arange(0, self.MAXGEN) y1 = self.trace[:, 0] y2 = self.trace[:, 1] plt.plot(x, y1, 'r', label='optimal value') plt.plot(x, y2, 'g', label='average value') plt.xlabel("Iteration") plt.ylabel("function value") plt.title("Bat algorithm for function optimization") plt.legend() plt.show()
运行程序:
if __name__ == "__main__": bound = np.tile([[-600], [600]], 25) ba = BA(60, 25, bound, 1000, [1, 0, 1, 0, 0.8, 0.9]) ba.solve()
ObjFunction见简单遗传算法-python实现。
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