文献类型：期刊文献

英文题名：A Survey on Knee-Oriented Multiobjective Evolutionary Optimization

作者：Yu, Guo[1];Ma, Lianbo[2];Jin, Yaochu[3,4];Du, Wenli[1];Liu, Qiqi[4];Zhang, Hengmin[5]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]Northeastern Univ, Software Coll, Shenyang 110819, Peoples R China;[3]Bielefeld Univ, Fac Technol, D-33619 Bielefeld, Germany;[4]Univ Surrey, Dept Comp Sci, Guildford GU2 7XH, England;[5]Univ Macau, Dept Comp & Informat Sci, Macau, Peoples R China

年份：2022

卷号：26

期号：6

起止页码：1452

外文期刊名：IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION

收录：;EI(收录号：20220511569806);WOS:【SCI-EXPANDED(收录号:WOS:000892933300021)】；

基金：This work wassupported in part by the Key Project of Science and Technology Innovation2030 supported by the Ministry of Science and Technology of China underGrant 2018AAA0101302; in part by the National Natural Science Foundationof China under Grant 62136003, Grant 62103150, Grant 61720106008, andGrant 61906067; and in part by the China Postdoctoral Science FoundationGrant 2021M691012.

语种：英文

外文关键词：Knee; multiobjective optimization; preference

摘要：Conventional multiobjective optimization algorithms (MOEAs) with or without preferences are successful in solving multi- and many-objective optimization problems. However, a strong hypothesis underlying their performance is that MOEAs are able to find a representative solution set to cover the entire Pareto-optimal front (PF) and decision makers are able to conveniently and precisely articulate their preference, which is not always easy to fulfill in practice. Accordingly, it is suggested that representative solutions in the naturally interesting regions of the PF rather than the whole PF should be targeted. A large body of research has been proposed to search or identify the knees or knee regions over the past decades. Therefore, this article aims to provide a comprehensive survey of the research on knee-oriented optimization. We start with a discussion of the importance and basic concepts of the knees, followed by a summary of knee-oriented benchmarks and indicators. After that, knee-oriented frameworks and techniques, and real-world applications are presented. Finally, potential challenges are pointed out and a few promising future lines of research are suggested. The survey offers a new perspective to develop MOEAs for solving multi- and many-objective optimization problems.