基于引领个体策略的人工蜂群算法  

An Artificial Bee Colony Algorithm with a Leading Point

作　　者：高楚仪 付峥 吴冬梅[2] GAO Chu-yi;FU Zheng;WU Dong-mei(NO.9 Middle School of Nanjing,Nanjing 210046,China;College of Automation,Nanjing University of Posts and Telecommunications,Nanjing 210046,China.)

机构地区：[1]南京市九中,江苏南京210018 [2]南京邮电大学自动化学院,江苏南京210046

出　　处：《电脑与信息技术》2018年第6期5-7,共3页Computer and Information Technology

基　　金：国家自然科学基金资助项目(项目编号:61571236);江苏省自然科学基金资助项目(项目编号:BK20130873);江苏省研究生科研创新计划资助项目(项目编号:KYCX17_0795)

摘　　要：近年来,蜂群进化算法由于其较强的全局搜索能力收到了广泛关注,但研究发现该算法收敛速度不快。文章首先通过从具有较好寻优结果的个体中任意选取一个个体作为当前进化个体的引领个体,从而加快其收敛速度。其次,通过引入一个混沌变异因子控制改进以后个体的寻优范围以平衡个体的全局和局部搜索能力。改进以后的蜂群进化算法同多个比较算法在典型的标准测试函数验证了算法有效性。Recent years, based on the research, artificial bee colony algorithm （ABC） attracts more attention due to itspower global search ability but slow convergence rate. In our algorithm, we first presenta leading point, which is selectedfrom the individuals with excellent performance in the current iteration, to leading the search direction of the currentupdating individual. This method could accelerate the convergence rate of population. Furthermore, we proposea logisticmap chaotic operator to enlarge the search space which could balance the global and local search abilities. Compared withother evolutionary algorithms, we verify the effectiveness of our algorithm on the traditional benchmark functions.

关 键 词：人工蜂群进化算法 全局搜索能力 收敛速度 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]