Optimization of high-dimensional expensive multi-objective problems using multi-mode radial basis functions

作     者：Shen, Jiangtao Wang, Xinjing He, Ruixuan Tian, Ye Wang, Wenxin Wang, Peng Wen, Zhiwen 

作者机构：Northwestern Polytech Univ Sch Marine Sci & Technol Youyi West Rd Xian 710072 Shaanxi Peoples R China Anhui Univ Inst Phys Sci & Informat Technol Minist Educ Key Lab Intelligent Comp & Signal Proc Jiulong Rd Hefei 230601 Anhui Peoples R China Xian Precis Machinery Res Inst Jinye Rd Xian 710072 Shaanxi Peoples R China 

出 版 物：《COMPLEX & INTELLIGENT SYSTEMS》 (Complex Intell. Syst.)

年 卷 期：2025年第11卷第2期

页      面：1-22页

核心收录：

基　　金：National Natural Science Foundation of China [52205268, 52175251] National Natural Science Foundation of China [JCKY2021206B005] National Defense Basic Scientific Research Program of China [HYGJXM202318] Industry Fund Project of Northwestern Polytechnical University 

主　　题：Multi-objective optimization problem High-dimensional Expensive optimization Surrogate ensemble Structure design of BWBUG 

摘      要：Numerous surrogate-assisted evolutionary algorithms are developed for multi-objective expensive problems with low dimensions, but scarce works have paid attention to that with high dimensions, i.e., generally more than 30 decision variables. In this paper, we propose a multi-mode radial basis functions-assisted evolutionary algorithm (MMRAEA) for solving high-dimensional expensive multi-objective optimization problems. To improve the reliability, the proposed algorithm uses radial basis functions based on three modes to cooperate to provide the qualities and uncertainty information of candidate solutions. Meanwhile, bi-population based on competitive swarm optimizer and genetic algorithm are applied for better exploration and exploitation in high-dimensional search space. Accordingly, an infill criterion based on multi-mode of radial basis functions that comprehensively considers the quality and uncertainty of candidate solutions is proposed. Experimental results on widely-used benchmark problems with up to 100 decision variables demonstrate the effectiveness of our proposal. Furthermore, the proposed method is applied to the structure optimization of the blended-wing-body underwater glider (BWBUG) and gets impressive solutions.