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ABSIndividual.py
? 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37import numpy as np
import ObjFunction
class ABSIndividual:
'''
individual of artificial bee swarm 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 artificial bee swarm 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)
ABS.py
? 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187import numpy as np
from ABSIndividual import ABSIndividual
import random
import copy
import matplotlib.pyplot as plt
class ArtificialBeeSwarm:
'''
the class for artificial bee swarm 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[trailLimit, C]
'''
self.sizepop = sizepop
self.vardim = vardim
self.bound = bound
self.foodSource = self.sizepop / 2
self.MAXGEN = MAXGEN
self.params = params
self.population = []
self.fitness = np.zeros((self.sizepop, 1))
self.trace = np.zeros((self.MAXGEN, 2))
def initialize(self):
'''
initialize the population of abs
'''
for i in xrange(0, self.foodSource):
ind = ABSIndividual(self.vardim, self.bound)
ind.generate()
self.population.append(ind)
def evaluation(self):
'''
evaluation the fitness of the population
'''
for i in xrange(0, self.foodSource):
self.population[i].calculateFitness()
self.fitness[i] = self.population[i].fitness
def employedBeePhase(self):
'''
employed bee phase
'''
for i in xrange(0, self.foodSource):
k = np.random.random_integers(0, self.vardim - 1)
j = np.random.random_integers(0, self.foodSource - 1)
while j == i:
j = np.random.random_integers(0, self.foodSource - 1)
vi = copy.deepcopy(self.population[i])
# vi.chrom = vi.chrom + np.random.uniform(-1, 1, self.vardim) * (
# vi.chrom - self.population[j].chrom) + np.random.uniform(0.0, self.params[1], self.vardim) * (self.best.chrom - vi.chrom)
# for k in xrange(0, self.vardim):
# if vi.chrom[k] < self.bound[0, k]:
# vi.chrom[k] = self.bound[0, k]
# if vi.chrom[k] > self.bound[1, k]:
# vi.chrom[k] = self.bound[1, k]
vi.chrom[
k] += np.random.uniform(low=-1, high=1.0, size=1) * (vi.chrom[k] - self.population[j].chrom[k])
if vi.chrom[k] < self.bound[0, k]:
vi.chrom[k] = self.bound[0, k]
if vi.chrom[k] > self.bound[1, k]:
vi.chrom[k] = self.bound[1, k]
vi.calculateFitness()
if vi.fitness > self.fitness[fi]:
self.population[fi] = vi
self.fitness[fi] = vi.fitness
if vi.fitness > self.best.fitness:
self.best = vi
vi.calculateFitness()
if vi.fitness > self.fitness[i]:
self.population[i] = vi
self.fitness[i] = vi.fitness
if vi.fitness > self.best.fitness:
self.best = vi
else:
self.population[i].trials += 1
def onlookerBeePhase(self):
'''
onlooker bee phase
'''
accuFitness = np.zeros((self.foodSource, 1))
maxFitness = np.max(self.fitness)
for i in xrange(0, self.foodSource):
accuFitness[i] = 0.9 * self.fitness[i] / maxFitness + 0.1
for i in xrange(0, self.foodSource):
for fi in xrange(0, self.foodSource):
r = random.random()
if r < accuFitness[i]:
k = np.random.random_integers(0, self.vardim - 1)
j = np.random.random_integers(0, self.foodSource - 1)
while j == fi:
j = np.random.random_integers(0, self.foodSource - 1)
vi = copy.deepcopy(self.population[fi])
# vi.chrom = vi.chrom + np.random.uniform(-1, 1, self.vardim) * (
# vi.chrom - self.population[j].chrom) + np.random.uniform(0.0, self.params[1], self.vardim) * (self.best.chrom - vi.chrom)
# for k in xrange(0, self.vardim):
# if vi.chrom[k] < self.bound[0, k]:
# vi.chrom[k] = self.bound[0, k]
# if vi.chrom[k] > self.bound[1, k]:
# vi.chrom[k] = self.bound[1, k]
vi.chrom[
k] += np.random.uniform(low=-1, high=1.0, size=1) * (vi.chrom[k] - self.population[j].chrom[k])
if vi.chrom[k] < self.bound[0, k]:
vi.chrom[k] = self.bound[0, k]
if vi.chrom[k] > self.bound[1, k]:
vi.chrom[k] = self.bound[1, k]
vi.calculateFitness()
if vi.fitness > self.fitness[fi]:
self.population[fi] = vi
self.fitness[fi] = vi.fitness
if vi.fitness > self.best.fitness:
self.best = vi
else:
self.population[fi].trials += 1
break
def scoutBeePhase(self):
'''
scout bee phase
'''
for i in xrange(0, self.foodSource):
if self.population[i].trials > self.params[0]:
self.population[i].generate()
self.population[i].trials = 0
self.population[i].calculateFitness()
self.fitness[i] = self.population[i].fitness
def solve(self):
'''
the evolution process of the abs algorithm
'''
self.t = 0
self.initialize()
self.evaluation()
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
self.employedBeePhase()
self.onlookerBeePhase()
self.scoutBeePhase()
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 printResult(self):
'''
plot the result of abs 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 Bee Swarm algorithm for function optimization")
plt.legend()
plt.show()
运行程序：
? 1 2 3 4 5if __name__ == "__main__":
bound = np.tile([[-600], [600]], 25)
abs = ABS(60, 25, bound, 1000, [100, 0.5])
abs.solve()
ObjFunction见简单遗传算法-python实现。
以上就是python实现人工蜂群算法的详细内容，更多关于python 人工蜂群算法的资料请关注服务器之家其它相关文章！
原文链接：https://www.cnblogs.com/biaoyu/p/4857904.html