文献类型：期刊文献

英文题名：A two-stage large-scale multiobjective evolutionary algorithm based on offset direction sampling and dual-layer competition

作者：Yang, Zhen[1,2];Li, Xun[3];Zhang, Xingyi[4,5];Jiang, Yunliang[3,6];Li, Zhongmei[7,8];Zhou, Lulin[3]

机构：[1]Huzhou Coll, Sch Elect Informat, Huzhou 313000, Peoples R China;[2]Huzhou Inst Ind Control Technol, Huzhou 313099, Peoples R China;[3]Huzhou Univ, Sch Informat Engn, Huzhou 313000, Peoples R China;[4]Anhui Univ, Informat Mat & Intelligent Sensing Lab Anhui Prov, Hefei 230601, Peoples R China;[5]Anhui Univ, Sch Comp Sci & Technol, Hefei 230601, Peoples R China;[6]Zhejiang Normal Univ, Zhejiang Key Lab Intelligent Educ Technol & Applic, Jinhua 321004, Peoples R China;[7]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[8]East China Univ Sci & Technol, State Key Lab Ind Control Technol, Shanghai 200237, Peoples R China

年份：2026

卷号：303

外文期刊名：EXPERT SYSTEMS WITH APPLICATIONS

收录：;EI(收录号：20260720051845);WOS:【SCI-EXPANDED(收录号:WOS:001640999100003)】；

基金：This work was supported by the National Natural Science Foundation of China (Grant No. U22A20102), the Natural Science Foundation of Zhejiang Province, China (Grant No. LY24F030012), the Scientific Research Foundation of Zhejiang Education Department, China (Grant No. Y202351138), the Open Research Project of the State Key Laboratory of Industrial Control Technology, China (Grant No. ICT2025D03), the Natural Science Foundation of Huzhou City, China (Grant No. 2024YZ33), and the "Pioneer" and "Leading Goose" R&D Program of Zhejiang Province, China (Grant No. 2023C01150).

语种：英文

外文关键词：Multiobjective optimization; Large-scale; Offset direction sampling; Dual-layer competition

摘要：Large-scale multiobjective optimization problems are characterized by high-dimensional decision spaces and complex search landscapes. These challenges create a dilemma for balancing convergence and diversity in the objective space under limited function evaluations. To address this issue, this paper proposes a two-stage largescale multiobjective evolutionary algorithm based on offset direction sampling and dual-layer competition. In the rapid convergence stage, a set of high-quality solutions is used to construct search directions in the decision space, followed by sampling along these directions to assist faster convergence of the population. In the precise convergence stage, all individuals are divided into elite and non-elite layers based on non-dominated ranks. Through competition between different layers, two outstanding individuals are identified to further guide the population toward the Pareto-optimal front. Experimental evaluations on benchmark demonstrate significant competitive advantage of the proposed algorithm in addressing large-scale multiobjective optimization challenges involving up to 5000 decision variables.