文献类型：期刊文献

英文题名：Solving High-Dimensional Expensive Multiobjective Optimization Problems by Adaptive Decision Variable Grouping

作者：Li, Yingwei[1];Feng, Xiang[1];Yu, Huiqun[1]

机构：[1]East China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai 200237, Peoples R China

年份：2025

卷号：29

期号：4

起止页码：1041

外文期刊名：IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION

收录：;EI(收录号：20241515873389);WOS:【SCI-EXPANDED(收录号:WOS:001545630400014)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 62276097; in part by the Key Program of National Natural Science Foundation of China under Grant 62136003. This article was approved by Associate Editor Z.-H. Zhan. (Corresponding author: Xiang Feng.)

语种：英文

外文关键词：Evolutionary computation; Optimization; Iron; Vectors; Statistics; Sociology; Convergence; Decision variable grouping; expensive optimization; multiobjective optimization; surrogate-assisted evolutionary algorithm (SAEA)

摘要：Plenty of decision variable grouping-based algorithms have shown satisfactory performance in solving high-dimensional optimization problems. However, most of them are tailored for inexpensive optimization problems. Extending variable grouping method to expensive optimization problems poses many challenges. One of the greatest challenges is that most grouping approaches require additional function evaluations (FEs) to discover interactions among decision variables, which is intolerable for expensive optimization problems as it incurs prohibitive computational costs. To address this issue, an adaptive variable grouping method is proposed in this article, which can achieve relatively accurate grouping results without additional FE consumption. Specifically, variables are grouped based on the contrasts between well-converged solutions and poorly converged solutions. Furthermore, the grouping scheme is adjusted dynamically during the optimization process to improve the grouping accuracy. Besides, an adaptive environmental selection-based sampling strategy is suggested, which attempts to provide the currently required solutions for reevaluation according to the demands of different optimization stages. The proposed algorithm is compared with the other five state-of-the-art multiobjective optimization evolutionary algorithms on both benchmark and real-world problems. The experimental results demonstrate the promising performance and the superior computational efficiency of the proposed algorithm in tackling high-dimensional expensive multiobjective optimization problems.