BFOIndividual.py
import numpy as np
import ObjFunction class BFOIndividual: '''
individual of baterial clony foraging 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 baterial clony foraging algorithm
'''
len = self.vardim
rnd = np.random.random(size=len)
self.chrom = np.zeros(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)
s1 = 0.
s2 = 1.
for i in range(1, self.vardim + 1):
s1 = s1 + self.chrom[i - 1] ** 2
s2 = s2 * np.cos(self.chrom[i - 1] / np.sqrt(i))
y = (1. / 4000.) * s1 - s2 + 1
self.fitness = y
BFO.py
import numpy as np
from BFOIndividual import BFOIndividual
import random
import copy
import matplotlib.pyplot as plt
import math class BacterialForagingOptimization: '''
The class for baterial foraging optimization algorithm
''' def __init__(self, sizepop, vardim, bound, params):
'''
sizepop: population sizepop
vardim: dimension of variables
bound: boundaries of variables
param: algorithm required parameters, it is a list which is consisting of [Ned, Nre, Nc, Ns, C, ped, d_attract, w_attract, d_repellant, w_repellant]
'''
self.sizepop = sizepop
self.vardim = vardim
self.bound = bound
self.population = []
self.bestPopulation = []
self.accuFitness = np.zeros(self.sizepop)
self.fitness = np.zeros(self.sizepop)
self.params = params
self.trace = np.zeros(
(self.params[0] * self.params[1] * self.params[2], 2)) def initialize(self):
'''
initialize the population
'''
for i in xrange(0, self.sizepop):
ind = BFOIndividual(self.vardim, self.bound)
ind.generate()
self.population.append(ind) def evaluate(self):
'''
evaluation of the population fitnesses
'''
for i in xrange(0, self.sizepop):
self.population[i].calculateFitness()
self.fitness[i] = self.population[i].fitness def sortPopulation(self):
'''
sort population according descending order
'''
sortedIdx = np.argsort(self.accuFitness)
newpop = []
newFitness = np.zeros(self.sizepop)
for i in xrange(0, self.sizepop):
ind = self.population[sortedIdx[i]]
newpop.append(ind)
self.fitness[i] = ind.fitness
self.population = newpop def solve(self):
'''
evolution process of baterial clony foraging algorithm
'''
self.t = 0
self.initialize()
self.evaluate()
bestIndex = np.argmin(self.fitness)
self.best = copy.deepcopy(self.population[bestIndex]) for i in xrange(0, self.params[0]):
for j in xrange(0, self.params[1]):
for k in xrange(0, self.params[2]):
self.t += 1
self.chemotaxls()
self.evaluate()
best = np.min(self.fitness)
bestIndex = np.argmin(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] = self.best.fitness
self.trace[self.t - 1, 1] = 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]))
self.reproduction()
self.eliminationAndDispersal() print("Optimal function value is: %f; " %
self.trace[self.t - 1, 0])
print "Optimal solution is:"
print self.best.chrom
self.printResult() def chemotaxls(self):
'''
chemotaxls behavior of baterials
'''
for i in xrange(0, self.sizepop):
tmpInd = copy.deepcopy(self.population[i])
self.fitness[i] += self.communication(tmpInd)
Jlast = self.fitness[i]
rnd = np.random.uniform(low=-1, high=1.0, size=self.vardim)
phi = rnd / np.linalg.norm(rnd)
tmpInd.chrom += self.params[4] * phi
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()
m = 0
while m < self.params[3]:
if tmpInd.fitness < Jlast:
Jlast = tmpInd.fitness
self.population[i] = copy.deepcopy(tmpInd)
# print m, Jlast
tmpInd.fitness += self.communication(tmpInd)
tmpInd.chrom += self.params[4] * phi
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()
m += 1
else:
m = self.params[3]
self.fitness[i] = Jlast
self.accuFitness[i] += Jlast def communication(self, ind):
'''
cell to cell communication
'''
Jcc = 0.0
term1 = 0.0
term2 = 0.0
for j in xrange(0, self.sizepop):
term = 0.0
for k in xrange(0, self.vardim):
term += (ind.chrom[k] -
self.population[j].chrom[k]) ** 2
term1 -= self.params[6] * np.exp(-1 * self.params[7] * term)
term2 += self.params[8] * np.exp(-1 * self.params[9] * term)
Jcc = term1 + term2 return Jcc def reproduction(self):
'''
reproduction of baterials
'''
self.sortPopulation()
newpop = []
for i in xrange(0, self.sizepop / 2):
newpop.append(self.population[i])
for i in xrange(self.sizepop / 2, self.sizepop):
self.population[i] = newpop[i - self.sizepop / 2] def eliminationAndDispersal(self):
'''
elimination and dispersal of baterials
'''
for i in xrange(0, self.sizepop):
rnd = random.random()
if rnd < self.params[5]:
self.population[i].generate() def printResult(self):
'''
plot the result of the baterial clony foraging algorithm
'''
x = np.arange(0, self.t)
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(
"Baterial clony foraging algorithm for function optimization")
plt.legend()
plt.show()
运行程序：
if __name__ == "__main__":
bound = np.tile([[-600], [600]], 25)
bfo = BFO(60, 25, bound, [2, 2, 50, 4, 50, 0.25, 0.1, 0.2, 0.1, 10])
bfo.solve()ObjFunction见简单遗传算法-python实现。