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ABS_Zsp.py
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#-*-coding:utf-8 -*-
import numpy as np
import random
import copy
import matplotlib.pyplot as plt
import operator
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. #适应度
self.trials = 0 #在同一蜜源附近搜索次数
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 = self.objfunction(self.chrom)
def objfunction(self, x):
value = 0
for j in range(self.vardim / 4):
i = j + 1
value = value + (x[4 * i - 3 - 1] + 10 * x[4 * i - 2 - 1]) ** 2
value = value + 5 * ((x[4 * i - 1 - 1] - x[4 * i - 1]) ** 2)
value = value + (x[4 * i - 2 - 1] - 2 * x[4 * i - 1 - 1]) ** 4
value = value + 10 * ((x[4 * i - 3 - 1] - x[4 * i - 1]) ** 4)
return value
class ArtificialBeeSwarm:
'''
the class for artificial bee swarm algorithm
'''
def __init__(self, sizepop, vardim, bound, MAXGEN, limit):
'''
sizepop: 蜂群大小规模
vardim: 变量维度
bound: 边界
MAXGEN: 最大循环次数
params: 最大探索次数
'''
self.sizepop = sizepop
self.vardim = vardim
self.bound = bound
self.foodSource = self.sizepop / 2 #采蜜蜂个数选择为蜜蜂个数的一半
self.MAXGEN = MAXGEN
self.limit = limit
self.population = []
def initialize(self):
#初始化蜂群
for i in xrange(0, self.sizepop):
ind = ABSIndividual(self.vardim, self.bound)
ind.generate()
self.population.append(ind)
def evaluation(self):
#计算蜂群内各蜜蜂的适应度
for i in xrange(0, self.sizepop):
self.population[i].calculateFitness()
def bee_one(self):
#采蜜蜂活动
for i in xrange(0, self.foodSource):
k = np.random.random_integers(0, self.vardim - 1) # 随机选择一个维度
j = np.random.random_integers(0, self.foodSource - 1) # 随机选择一只采蜜蜂
while j == i:
j = np.random.random_integers(0, self.foodSource - 1)
vi = copy.deepcopy(self.population[i])
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.population[i].fitness:
self.population[i] = vi
self.population[i].trials = 0
if vi.fitness > self.best.fitness:
self.best = vi
else:
self.population[i].trials += 1
def bee_two(self):
#跟随蜂的活动
sumfit = 0 #采蜜蜂总适应度
sum_fit = np.zeros(self.foodSource)
for i in xrange(0, self.foodSource): #生成一个转盘，用来随机选择将要跟随的采蜜群
sumfit = sumfit + self.population[i].fitness
sum_fit[i] = sumfit
for i in xrange(0,self.sizepop-self.foodSource):
self.population[i + self.foodSource].trials = 0
rand_value = np.random.uniform(low=0,high=sumfit,size=1)
for index in xrange(0,self.foodSource):
if rand_value < sum_fit[index]:
j1 = index #选择要跟随的采蜜蜂
break
k = np.random.random_integers(0, self.vardim - 1) #随机选择一维
j = np.random.random_integers(0, self.foodSource - 1) # 随机选择一只采蜜蜂
while j == j1:
j = np.random.random_integers(0, self.foodSource - 1)
vi = copy.deepcopy(self.population[j1])
vj = copy.deepcopy(self.population[j1])
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.population[j1].fitness:
self.population[i+self.foodSource] = vi
self.population[i+self.foodSource].trials = 0
if vi.fitness > self.best.fitness:
self.best = vi
else:
self.population[i+self.foodSource] = vj
self.population[i+self.foodSource].trials += 1
def scoutBeePhase(self):
#计算各蜜蜂在同一个地点搜索次数
for i in xrange(0, self.sizepop):
if self.population[i].trials > self.limit:
self.population[i].generate()
self.population[i].trials = 0
self.population[i].calculateFitness()
def solve(self):
#人工蜂群计算过程
self.t = 0 #循环次数
self.initialize() #初始化种群
self.evaluation() #计算适应度
cmpfun = operator.attrgetter("fitness")
self.population.sort(key=cmpfun, reverse=True) #按照适应度排序，前一半为采蜜蜂，后一半为跟随蜂
self.best = copy.deepcopy(self.population[0]) #最优适应度位置
print("Generation %d: optimal function value is: %f;" % (
self.t, self.best.fitness))
while self.t < self.MAXGEN :
self.t += 1
self.bee_one() #population 前一半为采蜜蜂，采蜜粉活动
self.bee_two() #population 后一半为跟随蜂，跟随蜂活动
self.scoutBeePhase()
self.population.sort(key=cmpfun, reverse=True) # 按照适应度排序，前一半为采蜜蜂，后一半为跟随蜂
if self.population[0].fitness > self.best.fitness:
self.best = copy.deepcopy(self.population[0]) # 最优适应度位置
print("Generation %d: optimal function value is: %f;" % (
self.t, self.best.fitness))
print "Optimal solution is:"
print self.best.chrom
if __name__ == "__main__":
bound = np.tile([[-1], [1]], 20)
abs = ArtificialBeeSwarm(60, 20, bound, 1000, 100)
abs.solve()