文献类型：期刊文献

英文题名：An adaptive switching-based evolutionary algorithm for many-objective optimization

作者：Chen, Sanyan[1];Wang, Xuewu[1];Gao, Jin[1];Du, Wei[1];Gu, Xingsheng[1]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

年份：2022

卷号：248

外文期刊名：KNOWLEDGE-BASED SYSTEMS

收录：;EI(收录号：20222012123279);WOS:【SCI-EXPANDED(收录号:WOS:000830188500014)】；

基金：This work is supported by the Natural Science Foundation of China (Grant Nos. 61973120 and 62076095), the Programme of Introducing Talents of Discipline to Universities (the 111 Project), China under Grant No. B17017, and Fundamental Research Funds of the Central Universities, China under Grant No. 222201917006.

语种：英文

外文关键词：Convergence; Diversity; Adaptive switching; Evolutionary algorithm; Many-objective optimization

摘要：Pareto-based evolutionary algorithms are challenging in dealing with many-objective problems encountering many incomparable nondominated solutions. To reduce selection pressure and improve diversity, this paper proposes an adaptive switching strategy-based evolutionary algorithm for manyobjective optimization. This strategy contains two deletion criteria, which are switched adaptively between generations, aiming to delete poor solutions one by one in environmental selection. The first criterion is devised to delete the solution with poor convergence among the two most similar solutions. The second criterion is developed to delete the worse solution according to a designed indicator that takes into account both convergence and diversity. Finally, comparisons with five state-of-theart many-objective evolutionary algorithms on some widely used benchmark problems and the water resource planning problem are given to illustrate the effectiveness and advantages of the proposed algorithm. (C) 2022 Elsevier B.V. All rights reserved.