A novel hybrid multi-objective bacterial colony chemotaxis algorithm

作     者：Lu, Zhigang Geng, Lijun Huo, Guanghao Zhao, Hao Yao, Weitao Li, Guoqiang Guo, Xiaoqiang Zhang, Jiangfeng 

作者机构：Yanshan Univ Key Lab Power Elect Energy Conservat & Motor Driv Qinhuangdao 066004 Hebei Peoples R China Univ Technol Sydney Sch Elect & Data Engn Sydney NSW Australia Tianjin Elect Power Design Inst Co Ltd China Energy Engn Grp Tianjin 300400 Peoples R China State Grid Tianjin Maintenance Co Tianjin 300000 Peoples R China 

出 版 物：《SOFT COMPUTING》 (Soft Comput.)

年 卷 期：2020年第24卷第3期

页      面：2013-2032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 [61873225, 61374098] Natural Science Foundation of Hebei Province Beijing Tianjin Hebei cooperation project [F2016203507] 

主　　题：Global optimization MOBCC Hybrid algorithm Intelligence computation 

摘      要：In this article, a novel hybrid multi-objective bacterial colony chemotaxis (HMOBCC) algorithm is proposed to solve multi-objective optimization problems. A mechanism of particle swarm optimization is introduced to multi-objective bacterial colony chemotaxis (MOBCC) algorithm to improve the performance of MOBCC algorithm. Also, three other techniques, including dynamic reverse learning operator, external archive multiplying operator and adaptive diversity maintenance operator, are further applied to improve the diversity and convergence of the algorithm. The proposed algorithm is validated using 12 benchmark problems, and three performance measures are implemented for 5 benchmark problems to compare its performance with existing popular algorithms such as MOBCC, multi-objective bacterial colony chemotaxis based on grid algorithm, non-dominated sorting genetic algorithm (NSGA-II) and multi-objective evolutionary algorithm based on decomposition. The results show that the proposed HMOBCC is very effective against existing algorithms.