基于多元信息引导的人工蜂群算法  

Artificial Bee Colony Algorithm Based on Multiple Information Guidance

作　　者：周新宇[1] 刘颖 吴艳林 郭京蕾[3] ZHOU Xin-yu;LIU Ying;WU Yan-lin;GUO Jing-lei(School of Computer and Information Engineering,Jiangxi Normal University,Nanchang,Jiangxi 330022,China;School of Computer and Communication Engineering,Changsha University of Science and Technology,Changsha,Hunan 410114,China;School of Computer Science,Central China Normal University,Wuhan,Hubei 430079,China)

机构地区：[1]江西师范大学计算机信息工程学院,江西南昌330022 [2]长沙理工大学计算机与通信工程学院,湖南长沙410114 [3]华中师范大学计算机学院,湖北武汉430079

出　　处：《电子学报》2024年第4期1349-1363,共15页Acta Electronica Sinica

基　　金：国家自然科学基金项目(No.61966019);江西省自然科学基金项目(No.20192BAB207030);中央高校基本科研业务费资助项目(No.CCNU20TS026)。

摘　　要：利用优秀个体增强解搜索方程的开采能力是改进人工蜂群算法的一种主流思路.然而,现有相关工作往往仅以适应度信息作为评价个体的唯一标准,易导致算法出现早熟收敛等问题.本文提出一种多元信息引导的人工蜂群算法,分别设计了基于适应度、位置以及相似度信息的3种解搜索方程,并在雇佣蜂阶段和观察蜂阶段采用了不同的使用方式.同时,为保存侦察蜂阶段的搜索经验,采用一种微调后的邻域搜索机制用于处理被放弃蜜源.在CEC2013测试集和一个实际优化问题上进行了大量实验验证,与6种衍生算法和5种知名的相关改进人工蜂群算法进行了对比,结果表明本文算法性能竞争优势明显,在结果精度和收敛速度上均有更好表现.As one of the main ideas to improve the artificial bee colony(ABC)algorithm,the superior individuals are used to enhance the exploitative capability of the solution search equation.However,in the related works,the fitness infor⁃mation is often considered as the sole criterion for evaluating the individuals,which may easily cause some problems,e.g.,the premature convergence.In this work,an improved ABC variant is proposed based on multiple information guidance,called ABC-MIG.In ABC-MIG,three different solution search equations are designed by using the fitness,position,and similarity information,respectively,and these new solution search equations are used in different ways for the employed bee phase and onlooker bee phase.Meanwhile,to save the search experience for the scout bee phase,a modified neighbor⁃hood search strategy is used to handle the abandoned food sources.To verify the effectiveness of ABC-MIG,extensive ex⁃periments are carried out on the CEC2013 test suite and one real-world optimization problem,and six derivative algorithms and five well-known improved ABC variants are included in the performance comparison.The results confirm that ABCMIG has very competitive performance,in terms of the result accuracy and convergence speed.

关 键 词：人工蜂群算法 优秀个体 多元信息 解搜索方程 邻域搜索 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]