Python实现人工蜂群算法：源码解析与优化应用
发布时间：2026-10-15| 5人浏览过
本文详细介绍如何使用Python实现人工蜂群算法（Artificial Bee Swarm, ABS）进行函数优化。内容涵盖核心类ABSIndividual与ArtificialBeeSwarm的代码结构、参数配置及运行示例，并提供完整源码参考，帮助开发者快速掌握该智能优化算法的实现细节与应用方法。
人工蜂群算法个体类实现
首先定义个体类 。该类用于表示人工蜂群算法中的单个个体，包含变量维度、边界、适应度、尝试次数等属性，并提供生成随机染色体和计算适应度的方法。
import 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)人工蜂群算法主类逻辑
接着定义算法主类 。该类实现了人工蜂群算法的核心逻辑，包括初始化种群、管理食物源、执行雇佣蜂、观察蜂和侦察蜂阶段的操作，并记录最优值和平均值的进化轨迹。
import 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()算法运行与参数配置
运行程序示例 。通过设置变量边界、种群大小、最大迭代次数及算法参数，实例化算法类并调用求解方法，完成优化任务。
if __name__ == "__main__":
bound = np.tile([[-600], [600]], 25)
abs = ABS(60, 25, bound, 1000, [100, 0.5])
abs.solve()目标函数引用说明
目标函数参考 [[wp:t3]]。具体使用的目标函数定义可参见简单遗传算法-python实现一文中的ObjFunction模块，本算法直接调用该模块进行适应度评估。
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