文献类型：期刊文献

英文题名：A Novel Dual-Stage Evolutionary Algorithm for Finding Robust Solutions

作者：Du, Wei[1];Fang, Wenxuan[1];Liang, Chen[1];Tang, Yang[1];Jin, Yaochu[2]

机构：[1]East China Univ Sci & Technol, Minist Educ, Key Lab Smart Mfg Energy Chem Proc, Shanghai 200237, Peoples R China;[2]Westlake Univ, Sch Engn, Hangzhou 310030, Peoples R China

年份：2024

卷号：8

期号：5

起止页码：3589

外文期刊名：IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE

收录：;EI(收录号：20241215767841);WOS:【SCI-EXPANDED(收录号:WOS:001328315000035)】；

语种：英文

外文关键词：Optimization; Statistics; Sociology; Search problems; Perturbation methods; Robustness; Monte Carlo methods; Evolutionary robust optimization; evolutionary algorithm; dual-stage strategy; peak detection

摘要：In robust optimization problems, the magnitude of perturbations is relatively small. Consequently, solutions within certain regions are less likely to represent the robust optima when perturbations are introduced. Hence, a more efficient search process would benefit from increased opportunities to explore promising regions where global optima or good local optima are situated. In this paper, we introduce a novel robust evolutionary algorithm named the dual-stage robust evolutionary algorithm (DREA) aimed at discovering robust solutions. DREA operates in two stages: the peak-detection stage and the robust solution-searching stage. The primary objective of the peak-detection stage is to identify peaks in the fitness landscape of the original optimization problem. Conversely, the robust solution-searching stage focuses on swiftly identifying the robust optimal solution using information obtained from the peaks discovered in the initial stage. These two stages collectively enable the proposed DREA to efficiently obtain the robust optimal solution for the optimization problem. This approach achieves a balance between solution optimality and robustness by separating the search processes for optimal and robust optimal solutions. Experimental results demonstrate that DREA significantly outperforms five state-of-the-art algorithms across 18 test problems characterized by diverse complexities. Moreover, when evaluated on higher-dimensional robust optimization problems (100-D and 200-D), DREA also demonstrates superior performance compared to all five counterpart algorithms.