文献类型：期刊文献

英文题名：A multi-population evolutionary algorithm with single-objective guide for many-objective optimization

作者：Liu, Haitao[1,2];Du, Wei[3];Guo, Zhaoxia[1,4]

机构：[1]Sichuan Univ, Business Sch, Chengdu 610065, Sichuan, Peoples R China;[2]Natl Univ Singapore, Dept Ind Syst Engn, Singapore 119077, Singapore;[3]East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China;[4]Sichuan Univ, Soft Sci Inst, Chengdu 610064, Sichuan, Peoples R China

年份：2019

卷号：503

起止页码：39

外文期刊名：INFORMATION SCIENCES

收录：;EI(收录号：20192707137868);WOS:【SCI-EXPANDED(收录号:WOS:000483425200003)】；

基金：The authors would like to thank the financial supports from the National Natural Science Foundation of China (Grant No. 71872118), the MOE (Ministry of Education in China) Project of Humanities and Social Sciences (Grant No. 18YJC630045) and Sichuan University (Grant No.s 2018hhs-37, SKSYL201819, skqx201725).

语种：英文

外文关键词：Multi-population evolutionary algorithm; Single-objective guide; Many-objective optimization

摘要：This paper develops a multi-population evolutionary algorithm with single-objective guide to tackle many-objective optimization problems. It exploits the merits of both multiple populations and single-objective optimization to balance diversity and convergence of the evolution process. Specifically, the single-objective guide process helps to construct the better ideal point and reference points. A novel objective space partitioning mechanism is developed to transform a many-objective optimization problem into multiple subproblems, each of which is tackled by a subpopulation. A novel information sharing mechanism between subpopulations is proposed to balance diversity and convergence. Finally the subpopulations are merged together and deal with many-objective optimization problems to further enhance the convergence. We have compared the performance of the proposed algorithm with nine state-of-the-art algorithms on 85 test instances of 21 benchmark problems with up to 15 objectives. Experimental results show that the proposed algorithm has the superior performance in solving multi- and many-objective optimization problems. (C) 2019 Elsevier Inc. All rights reserved.