基于多元信息引导的人工蜂群算法
Artificial Bee Colony Algorithm Based on Multiple Information Guidance
周新宇 1刘颖 2吴艳林 1郭京蕾3
作者信息
- 1. 江西师范大学计算机信息工程学院,江西南昌 330022
- 2. 江西师范大学计算机信息工程学院,江西南昌 330022;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
- 折叠
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.
Abstract
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 ABC-MIG 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Key words
artificial bee colony algorithm/superior individuals/multiple information/solution search equation/neighborhood search引用本文复制引用
基金项目
国家自然科学基金(61966019)
江西省自然科学基金(20192BAB207030)
中央高校基本科研业务费资助项目(CCNU20TS026)
出版年
2024
国家科技期刊平台
NSTL
万方数据