文献类型：期刊文献

英文题名：One-to-one ensemble mechanism for decomposition-based multi-Objective optimization

作者：Lin, Anping[1];Yu, Peiwen[2];Cheng, Shi[3];Xing, Lining[4,5]

机构：[1]Xiangnan Univ, Sch Phys & Elect Elect Engn, Chenzhou 423000, Peoples R China;[2]Guangdong Ocean Univ, Maritime Coll, Zhanjiang 524000, Peoples R China;[3]Shaanxi Normal Univ, Sch Comp Sci, Xian 710119, Peoples R China;[4]Xidian Univ, Sch Elect Engn, Xian 710071, Peoples R China;[5]Huazhong Univ Sci & Technol, State Key Lab Digital Mfg Equipment & Technol, Wuhan 430074, Peoples R China

年份：2022

卷号：68

外文期刊名：SWARM AND EVOLUTIONARY COMPUTATION

收录：SCI-EXPANDED(收录号：WOS:000719353800001)、、EI(收录号：20214611150328)、Scopus(收录号：2-s2.0-85118862003)、WOS

基金：This work was supported by the Scientific Research Fund of Hunan Provincial Education Department (No. 20A460), the National Natural Science Foundation, China (No. 61773120), the Innovation Team of Guangdong Provincial Department of Education (No. 2018KCXTD031), the Special Projects in Key Fields of Universities in Guangdong (2021ZDZX1019), State Key Laboratory of Digital Manufacturing Equipment and Technology (DMETKF2020030), and the Scientific Research Start-up Fund for High-level Talents in Xiangnan University.

语种：英文

外文关键词：Multi-objective optimization; Evolutionary algorithm; Ensemble mechanism; Complicated Pareto set

外文摘要：Multi-objective evolutionary algorithms based on decomposition (MOEA/Ds) have been generally recognized as competitive techniques for solving multi-objective optimization problems (MOPs) with complicated Paretooptimal sets. To date, ensemble methods have been developed for adaptively selecting evolution operators to enhance the performance of MOEA/Ds. However, most established ensemble methods ignore the variance of the characteristics of complicated MOPs throughout both the decision and objective spaces, and subproblems inevitably have distinct characteristics. Keeping these observations in mind, we propose a one-to-one ensemble mechanism, namely OTOEM, for adaptively associating each subproblem of an MOEA/D with a suitable evolution operator, which differs substantially from the established ensemble methods, in which all the subproblems of the MOEA/D are associated with the same evolution operator during each generation. Another novel feature of the OTOEM is that both the local and global credits of an evolutionary operator are considered in measuring its suitability for subproblems. Moreover, an adaptive rule is designed to stimulate evolution operators with higher overall credits to generate more new solutions and guarantee the continuity of the covariance matrix adaptation evolution strategy. The performance of the proposed OTOEM is evaluated by comparing it with eleven baseline MOEAs on 26 complicated MOPs, and empirical results demonstrate its powerful performance in terms of two widely used metrics, namely, the inverted generational distance and hypervolume.