BAIndividual.py
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。