import numpy as np
from ABSIndividual import ABSIndividual
import random
import copy
import matplotlib.pyplot as plt
class ArtificialBeeSwarm:
# 人工蜂群算法的类
def __init__(self, sizepop, vardim, bound, MAXGEN, params):
self.sizepop = sizepop # 蜂群大小60
self.vardim = vardim # 变量的维度25
self.bound = bound # 上下边界([[-100], [100]], 25)
self.foodSource = self.sizepop // 2 # 蜜源数目30
self.MAXGEN = MAXGEN # 最大循环次数500
self.params = params # 参数（蜜源经过限定的循环次数100后没有改进，则放弃）
self.population = []
self.fitness = np.zeros((self.sizepop, 1)) # 初始化适应度，行60，列1
self.trace = np.zeros((self.MAXGEN, 2)) # 行500，列2
def initialize(self):
# 初始化变量值
for i in range(0, self.foodSource):
ind = ABSIndividual(self.vardim, self.bound) # 调用ABSIndividual.py的ABSIndividual类
ind.generate() # 调用ABSIndividual的generate方法
self.population.append(ind)
def evaluation(self):
# 评估变量值的适应性
for i in range(0, self.foodSource):
self.population[i].calculateFitness()
self.fitness[i] = self.population[i].fitness
def employedBeePhase(self):
# 采蜜蜂
# fi = 0
for i in range(0, self.foodSource):
k = np.random.random_integers(0, self.vardim - 1) # 返回0到24之间的随机整数
j = np.random.random_integers(0, self.foodSource - 1) # 返回0到29之间的随机整数
while j == i:
j = np.random.random_integers(0, self.foodSource - 1)
vi = copy.deepcopy(self.population[i])
# uniform([low,high,size]) 均态分布
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):
# 观察蜂
accuFitness = np.zeros((self.foodSource, 1))
maxFitness = np.max(self.fitness)
for i in range(0, self.foodSource):
accuFitness[i] = 0.9 * self.fitness[i] / maxFitness + 0.1
for i in range(0, self.foodSource):
for fi in range(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[
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):
# 侦查蜂
for i in range(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):
# 求解
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):
# 输入结果
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()
if __name__ == "__main__":
bound = np.tile([[-100], [100]], 25) # 沿x轴复制25倍
abs = ArtificialBeeSwarm(60, 25, bound, 500, [100, 0.5])
abs.solve()
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