增强寻优能力的自适应人工蜂群算法  

Adaptive artificial bee colony algorithm with enhanced search ability

作　　者：张泰[1] 屠思远[1] 吴滨[1] 顾晓峰[1] Zhang Tai;Tu Siyuan;Wu Bin;Gu Xiaofeng(Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education, Dept, of Electronic Engineering, Jiangnan University,Wuxi Jiangsu 214122, China)

机构地区：[1]江南大学电子工程系轻工过程先进控制教育部重点实验室,江苏无锡214122

出　　处：《计算机应用研究》2016年第10期2946-2948,2981,共4页Application Research of Computers

基　　金：江苏省科技厅产学研联合创新资金资助项目(BY2013015-19);中央高校基本科研业务费专项资金资助项目(JUSRP51323B);江苏省普通高校研究生实践创新计划资助项目(SJZZ_0148)

摘　　要：针对人工蜂群算法在求解函数优化问题中存在收敛精度不高、收敛速度较慢的问题,提出了一种改进的增强寻优能力的自适应人工蜂群算法。该算法利用逻辑自映射函数产生混沌序列对雇佣蜂搜索行为进行混沌优化,并引入萤火虫算法中的自适应步长策略动态调整观察蜂的搜索行为,从而提升了算法的局部搜索能力。基于标准测试函数的仿真结果表明,改进后的人工蜂群算法在寻优精度和收敛速度上均有明显提高。In order to overcome the drawbacks o f low com putational accuracy and slow convergence rate o f conventional a r tificia l bee colony ( A B C ) algo rithm d u rin g solving the fu n ctio n op tim ization problem s, this paper proposed a self-adaptive A BCa lgo rithm w ith enhanced search a b ility . The m o dified a lgo rithm used the logic self-m apping fu n ctio n to generate chaotic sequencesso as to im ple m en t chaos o p tim ization fo r the search behaviors o f em ployed b e es,an d it adopted adaptive step strategyo f fire fly algo rithm to d yn am ically ad ju st the search behaviors o f observer b e e s,th e approach im proved the lo ca l search a b ility .S im ulations based on standard testing fun ctions in d ica te th a t the m o dified A B C algo rithm e xh ib its obviously im proved op tim ization accuracy and convergence rate.

关 键 词：人工蜂群算法 混沌优化 自适应步长策略 局部搜索 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]