摘要
针对基本的人工蜂群算法(basic Artificial Bee Colony algorithm,ABC)收敛速度慢和容易陷于局部最优等不足,采用混沌算子和逆向学习算子相混合的初始化种群的方法,有效地改进了初始种群的多样性;在雇佣蜂和观察峰的位置更新上,提出了自适应搜索算子.改进后的算法(Improved ABC,IABC)测试了5个标准单峰或多峰函数,结果表明,IABC算法在搜索效率、最优解质量、稳定性均优于ABC算法.
The basic artificial bee colony algorithm has a slow convergence speed,and easily gets trapped in local optimum.An improved algorithm given in this paper combined chaotic operator and inverse operator and then produced initialization population to improve the diversity of initial population.The parameter adaptive search operator was put forward and applied to the position updating of employed bees and onlookers bees.The improved algorithm（Improved ABC,IABC） had been experimented by five standard unimodal or multi-peak functions.The experimental results showed that the IABC algorithm is superior to the ABC algorithm in the search efficiency,the quality of the optimal solution and the stability.
出处
《信阳师范学院学报（自然科学版）》
CAS
北大核心
2013年第3期446-449,共4页
Journal of Xinyang Normal University(Natural Science Edition)
基金
河南省重点科技攻关项目(122102210488
132102210095)
河南省教育厅科学技术研究重点项目(13A520748)
关键词
人工蜂群算法
混沌算子
逆向算子
自适应搜索
artificial bee colony algorithm
chaotic operator
inverse operator
adaptive search
