计算机科学 ›› 2014, Vol. 41 ›› Issue (2): 102-106.
张新明,魏峰,牛丽平,王鲜芳
ZHANG Xin-ming,WEI Feng,NIU Li-ping and WANG Xian-fang
摘要: 针对由于人工蜂群算法(Artificial Bee Colony algorithm,ABC)采用直接映射概率选择食物源而引起收敛速度慢、陷入局部最优等问题,提出一种混合排名映射概率和混沌搜索的人工蜂群算法((Artificial Bee Colony algorithm based on Hybrid rank mapping probability and Chaotic search,ABC-HC))。首先,利用目标函数值的排名来获取选择食物源的排名映射概率,并提出计算排名映射概率的两种方法;然后,在观察蜂阶段,融合这两种计算概率的方法,即不同的搜索阶段采用不同的排名映射方法计算食物源选择概率,构造基于混合排名映射概率的人工蜂群算法,以便能够维持种群的多样性避免陷于局部最优;最后,在侦查蜂阶段,使用混沌搜索替代随机搜索以便进一步提高收敛速度,最终获得较好的全局最优解。对10个标准测试函数进行仿真,结果表明,ABC-HC算法不仅提高了收敛速度,而且更能跳出局部最优,有效地找到全局最优解,优于标准的ABC算法和进化算法。
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