具有自适应趋向性和引导因子的人工蜂群算法  

An artificial bee colony algorithm with adaptive chemotaxis and guiding factors

作　　者：臧培荃 孙晨骜 顾晓峰[1,2] 吴滨[1,2] 周长喜[1,2] 

机构地区：[1]轻工过程先进控制教育部重点实验室,江苏无锡214122 [2]江南大学物联网工程学院,江苏无锡214122

出　　处：《计算机工程与科学》2015年第9期1692-1697,共6页Computer Engineering & Science

基　　金：中央高校基本科研业务费专项资金资助项目(JUSRP51323B);江苏省科技厅产学研联合创新资金资助项目(BY2013015-19);江苏高校优势学科建设工程资助项目(PAPD)

摘　　要：针对人工蜂群算法中存在的收敛速度慢、寻优精度低的问题,提出了一种改进的人工蜂群算法。该算法将自适应趋向性加入雇佣蜂的搜索方案中,同时在观察蜂的搜索方案中加入引导因子。通过雇佣蜂对优秀蜜源的动态趋向搜索以及观察蜂在引导因子引领下的协同搜索,显著提高了算法的局部搜索能力。基于八个标准测试函数的仿真结果表明,与基本人工蜂群算法相比,改进后的算法在寻优精度和收敛速度方面均有明显提升。We propose an improved artificial bee colony （ABC） algorithm to overcome the drawbacks of slow convergence rate and low optimization precision of the conventional ABC algorithm. The self-a- daptive chemotaxis and guiding factors are introduced into the search schemes of employed bees and on- looker bees, respectively. Under the guidance of the guiding factors, the local search capability is signif- icantly improved due to the employed bees＇ dynamic search for the high quality nectar sources and the onlooker bees＇ cooperative search for the better nectar sources. Simulation results based on the eight standard functions show that the improved ABC algorithm is superior to the conventional one in both computational accuracy and convergence rate.

关 键 词：人工蜂群算法 局部搜索 搜索方案 自适应趋向性 引导因子 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]