Improved self-adaptive bat algorithm with step-control and mutation mechanisms

作     者：Lyu, Shilei Li, Zhen Huang, Yonglin Wang, Jianhua Hu, Jie 

作者机构：South China Agr Univ Coll Elect Engn Guangzhou 510642 Guangdong Peoples R China Guangdong Engn Res Ctr Monitoring Agr Informat Guangzhou 510642 Guangdong Peoples R China China Agr Res Syst Div Citrus Machinery Guangzhou 510642 Guangdong Peoples R China 

出 版 物：《JOURNAL OF COMPUTATIONAL SCIENCE》 (计算科学杂志)

年 卷 期：2019年第30卷

页      面：65-78页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China [61601189, 61602187] Special Fund of Modern Technology System of Agricultural Industry [CARS-26] Science and Technology Planning Project of Guangdong Province, China [2016A020210088, 2016A020210081] Science and Technology Program of Guangzhou, China [201803020037, 201605030013] Natural Science Foundation of Guangdong Province, China [2016A030310453] 

主　　题：Bat algorithm Self-adaptive Step-control mechanism Mutation mechanism 

摘      要：The bat algorithm (BA) is a new search optimization algorithm, inspired by bats echolocation behavior. However, it is prone to fall into local optima and has low solution accuracy. This study proposes an improved self-adaptive bat algorithm (SABA) with adaptive step-control and mutation mechanisms. The step-control mechanism uses two frequencies to adapt the step sizes used for the global and local searches, and the mutation mechanism could improve the algorithm s ability to avoid local optima. SABA s parameters are analyzed to ensure convergence. Its optimization and convergence performance are experimentally studied using 12 unimodal and multimodal functions;compared with a range of baseline algorithms, it can effectively avoid local optima and exhibits high solution accuracy. Further, its practical performance is evaluated using engineering optimization problems. (C) 2018 Elsevier B.V. All rights reserved.