人工蜂群算法研究综述  

Artificial bee colony algorithm:a survey

作　　者：秦全德[1] 程适[2] 李丽[1] 史玉回[3] 

机构地区：[1]管理科学系深圳大学,广东深圳518060 [2]宁波诺丁汉大学计算机科学系,浙江宁波315100 [3]西交利物浦大学电气电子工程系,江苏苏州215123

出　　处：《智能系统学报》2014年第2期127-135,共9页CAAI Transactions on Intelligent Systems

基　　金：国家自然科学基金资助项目(71240015;61273367);广东高校优秀青年创新人才培养计划资助项目(2012WYM_0116);教育部人文社科青年基金资助项目(13YJC630123)

摘　　要：作为一种较新的群体智能优化算法,人工蜂群算法自提出之时就受到学术界的广泛关注,目前已经在多个领域得到了成功应用。介绍了人工蜂群算法的生物背景和基本原理,在对基本人工蜂群算法的不足进行分析的基础上,归纳了当前人工蜂群算法的改进研究主要集中在算法的参数调整、混合算法和设计新的学习策略3个方面。针对现实的复杂环境,对人工蜂群算法在约束优化和多目标优化的研究进展进行了全面的综述。最后,阐述了人工蜂群算法的应用现状,并提出了人工蜂群算法有待进一步研究的问题。As a new swarm intelligence optimization algorithm, the artificial bee colony （ABC） algorithm has re- ceived wide attention in academic circles since its inception. Currently, the ABC algorithm is being used successful- ly in several real-world fields. Firstly, this article introduces the biological background and principles of the ABC algorithm. On the basis of analyzing the drawbacks of the basic ABC algorithm, we summarized the current studies on improvements of the basic ABC algorithm with regards to three aspects： parameter adjustment, hybrid algo- rithms, and design of new learning strategies. In view of the realistic complex environment, this article introduces the research progress on constrained optimization and multi-objective optimization using the ABC algorithm. Finally, the applications of the ABC algorithm are described and several further research directions are proposed.

关 键 词：群体智能 人工蜂群算法 约束优化 多目标优化 选择算法 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] F062.3[自动化与计算机技术—控制科学与工程]