AFSIndividual.py
import numpy as np
import ObjFunction
import copy class AFSIndividual: """class for AFSIndividual""" def __init__(self, vardim, bound):
'''
vardim: dimension of variables
bound: boundaries of variables
'''
self.vardim = vardim
self.bound = bound def generate(self):
'''
generate a rondom chromsome
'''
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]
self.bestPosition = np.zeros(len)
self.bestFitness = 0. def calculateFitness(self):
'''
calculate the fitness of the chromsome
'''
self.fitness = ObjFunction.GrieFunc(
self.vardim, self.chrom, self.bound)
AFS.py
import numpy as np
from AFSIndividual import AFSIndividual
import random
import copy
import matplotlib.pyplot as plt class ArtificialFishSwarm: """class for ArtificialFishSwarm""" def __init__(self, sizepop, vardim, bound, MAXGEN, params):
'''
sizepop: population sizepop
vardim: dimension of variables
bound: boundaries of variables, 2*vardim
MAXGEN: termination condition
params: algorithm required parameters, it is a list which is consisting of[visual, step, delta, trynum]
'''
self.sizepop = sizepop
self.vardim = vardim
self.bound = bound
self.MAXGEN = MAXGEN
self.params = params
self.population = []
self.fitness = np.zeros((self.sizepop, 1))
self.trace = np.zeros((self.MAXGEN, 2))
self.lennorm = 6000 def initialize(self):
'''
initialize the population of afs
'''
for i in xrange(0, self.sizepop):
ind = AFSIndividual(self.vardim, self.bound)
ind.generate()
self.population.append(ind) def evaluation(self, x):
'''
evaluation the fitness of the individual
'''
x.calculateFitness() def forage(self, x):
'''
artificial fish foraging behavior
'''
newInd = copy.deepcopy(x)
found = False
for i in xrange(0, self.params[3]):
indi = self.randSearch(x, self.params[0])
if indi.fitness > x.fitness:
newInd.chrom = x.chrom + np.random.random(self.vardim) * self.params[1] * self.lennorm * (
indi.chrom - x.chrom) / np.linalg.norm(indi.chrom - x.chrom)
newInd = indi
found = True
break
if not (found):
newInd = self.randSearch(x, self.params[1])
return newInd def randSearch(self, x, searLen):
'''
artificial fish random search behavior
'''
ind = copy.deepcopy(x)
ind.chrom += np.random.uniform(-1, 1,
self.vardim) * searLen * self.lennorm
for j in xrange(0, self.vardim):
if ind.chrom[j] < self.bound[0, j]:
ind.chrom[j] = self.bound[0, j]
if ind.chrom[j] > self.bound[1, j]:
ind.chrom[j] = self.bound[1, j]
self.evaluation(ind)
return ind def huddle(self, x):
'''
artificial fish huddling behavior
'''
newInd = copy.deepcopy(x)
dist = self.distance(x)
index = []
for i in xrange(1, self.sizepop):
if dist[i] > 0 and dist[i] < self.params[0] * self.lennorm:
index.append(i)
nf = len(index)
if nf > 0:
xc = np.zeros(self.vardim)
for i in xrange(0, nf):
xc += self.population[index[i]].chrom
xc = xc / nf
cind = AFSIndividual(self.vardim, self.bound)
cind.chrom = xc
cind.calculateFitness()
if (cind.fitness / nf) > (self.params[2] * x.fitness):
xnext = x.chrom + np.random.random(
self.vardim) * self.params[1] * self.lennorm * (xc - x.chrom) / np.linalg.norm(xc - x.chrom)
for j in xrange(0, self.vardim):
if xnext[j] < self.bound[0, j]:
xnext[j] = self.bound[0, j]
if xnext[j] > self.bound[1, j]:
xnext[j] = self.bound[1, j]
newInd.chrom = xnext
self.evaluation(newInd)
# print "hudding"
return newInd
else:
return self.forage(x)
else:
return self.forage(x) def follow(self, x):
'''
artificial fish following behivior
'''
newInd = copy.deepcopy(x)
dist = self.distance(x)
index = []
for i in xrange(1, self.sizepop):
if dist[i] > 0 and dist[i] < self.params[0] * self.lennorm:
index.append(i)
nf = len(index)
if nf > 0:
best = -999999999.
bestIndex = 0
for i in xrange(0, nf):
if self.population[index[i]].fitness > best:
best = self.population[index[i]].fitness
bestIndex = index[i]
if (self.population[bestIndex].fitness / nf) > (self.params[2] * x.fitness):
xnext = x.chrom + np.random.random(
self.vardim) * self.params[1] * self.lennorm * (self.population[bestIndex].chrom - x.chrom) / np.linalg.norm(self.population[bestIndex].chrom - x.chrom)
for j in xrange(0, self.vardim):
if xnext[j] < self.bound[0, j]:
xnext[j] = self.bound[0, j]
if xnext[j] > self.bound[1, j]:
xnext[j] = self.bound[1, j]
newInd.chrom = xnext
self.evaluation(newInd)
# print "follow"
return newInd
else:
return self.forage(x)
else:
return self.forage(x) def solve(self):
'''
evolution process for afs algorithm
'''
self.t = 0
self.initialize()
# evaluation the population
for i in xrange(0, self.sizepop):
self.evaluation(self.population[i])
self.fitness[i] = self.population[i].fitness
best = np.max(self.fitness)
bestIndex = np.argmax(self.fitness)
self.best = copy.deepcopy(self.population[bestIndex])
self.avefitness = np.mean(self.fitness)
self.trace[self.t, 0] = (1 - self.best.fitness) / self.best.fitness
self.trace[self.t, 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, 0], self.trace[self.t, 1]))
while self.t < self.MAXGEN - 1:
self.t += 1
# newpop = []
for i in xrange(0, self.sizepop):
xi1 = self.huddle(self.population[i])
xi2 = self.follow(self.population[i])
if xi1.fitness > xi2.fitness:
self.population[i] = xi1
self.fitness[i] = xi1.fitness
else:
self.population[i] = xi2
self.fitness[i] = xi2.fitness
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, 0] = (1 - self.best.fitness) / self.best.fitness
self.trace[self.t, 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, 0], self.trace[self.t, 1])) print("Optimal function value is: %f; " % self.trace[self.t, 0])
print "Optimal solution is:"
print self.best.chrom
self.printResult() def distance(self, x):
'''
return the distance array to a individual
'''
dist = np.zeros(self.sizepop)
for i in xrange(0, self.sizepop):
dist[i] = np.linalg.norm(x.chrom - self.population[i].chrom) / 6000
return dist def printResult(self):
'''
plot the result of afs 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("Artificial Fish Swarm algorithm for function optimization")
plt.legend()
plt.show()
运行程序：
if __name__ == "__main__":
bound = np.tile([[-600], [600]], 25)
afs = AFS(60, 25, bound, 500, [0.001, 0.0001, 0.618, 40])
afs.solve()
ObjFunction见简单遗传算法-python实现。
欢迎分享，转载请注明来源：内存溢出
微信扫一扫
支付宝扫一扫
评论列表（0条）