自适应Tent混沌搜索的人工蜂群算法  

Artificial bee colony algorithm based on self-adaptive Tent chaos search

作　　者：匡芳君[1,2] 徐蔚鸿[1,3] 金忠[1] 

机构地区：[1]南京理工大学计算机科学与工程学院,江苏南京210094 [2]湖南安全技术职业学院电气与信息工程系,湖南长沙410151 [3]长沙理工大学计算机与通信工程学院,湖南长沙410114

出　　处：《控制理论与应用》2014年第11期1502-1509,共8页Control Theory & Applications

基　　金：国家自然科学基金资助项目(61373063;61233011;61125305);湖南省科技计划资助项目(2013FJ4217);湖南省教育厅资助科研项目(13C086)

摘　　要：为了有效改善人工蜂群算法(artificial bee colony algorithm,ABC)的性能,结合Tent混沌优化算法,提出自适应Tent混沌搜索的人工蜂群算法.该算法使用Tent混沌以改善ABC的收敛性能,避免陷入局部最优解,首先应用Tent映射初始化种群,使得初始个体尽可能均匀分布,其次自适应调整混沌搜索空间,并以迄今为止搜索到的最优解产生Tent混沌序列,从而获得最优解.通过对6个复杂高维的基准函数寻优测试,仿真结果表明,该算法不仅加快了收敛速度,提高了寻优精度,与其他最近改进人工蜂群算法相比,其性能整体较优,尤其适合复杂的高维函数寻优.In order to improve the performance of artificial bee colony（ABC） algorithm,a novel ABC algorithm based on self-adaptive Tent chaos search which is combined with Tent chaos algorithm is proposed.The algorithm uses Tent chaos mapping to improve the convergence characteristics and prevent the ABC to get stuck on local solutions.In this algorithm,Tent mapping is applied to diversify the initial individuals in the search space.Tent chaotic sequence based an optimal location is produced,and the self-adaptive adjustment of chaos search scopes can obtain the global optima.Experiments on six complex benchmark functions with high-dimension,simulation results further demonstrate that,the improved algorithm not only accelerates the convergence rate and improves solution precision.Compared with other latest improved artificial colony algorithm,it has a better overall performance,especially for complex high-dimensional functions optimization.

关 键 词：人工蜂群算法 混沌理论 TENT映射 自适应搜索 锦标赛选择策略 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]