文献类型：期刊文献

英文题名：Large-Scale Multi-Objective Dual-Population Co-Evolutionary Algorithm Based on Decision Variable Boundary Penalty

作者：Zhou, Zhou[1];Li, Na[2];Bao, Hu[1];Li, Yan[2];Guo, Xin[2];Zheng, Ruochen[2];Dong, Wenbo[1];Ding, Weichao[1]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai, Peoples R China;[2]Sinopec Res Inst Petr Proc Co LTD, Beijing, Peoples R China

年份：2025

卷号：37

期号：27-28

外文期刊名：CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE

收录：;EI(收录号：20254719567706);WOS:【SCI-EXPANDED(收录号:WOS:001626088000004)】；

基金：This work was supported by National Energy R&D Center of Petroleum Refining Technology (RIPP, SINOPEC), Shanghai Pilot Program for Basic Research (22TQ1400100-16), National Natural Science Foundation of China (No. 62403201), and Nature Science Foundation of Shanghai (24ZR1415200, 23ZR1414900, 22ZR1416500).

语种：英文

外文关键词：decision variable boundary penalty; decision variable quantitative analysis; dual-population co-evolution; large-scale multi-objective optimization

摘要：Large-scale multi-objective optimization problems are widely used in expert systems and applications, which mainly consider the simultaneous optimization of multiple conflicting objectives under large-scale decision variables. Existing methods typically classify decision variables as either convergence- or diversity-related, neglecting their inherent characteristics and thus failing to balance convergence and diversity effectively. In order to address above issues, this article proposes a Large-scale Multi-objective Dual-population Co-evolutionary Algorithm based on decision variable boundary penalty (LMDCA). The proposed algorithm first uses a boundary penalty based cross decision variable analysis method to quantitatively analyze the decision variables, which can quantify the contribution values of convergence variables on different objective functions for grouping. Then, according to different variable groups, different optimization strategies are adopted to more accurately approximate the Pareto front of each objective. Subsequently, the convergence and diversity populations were constructed by combining the dual-population co-evolutionary framework, in which three strategies of directional restriction of mating choice, environmental selection and information compensation were designed for co-interaction within each of the populations to ensure the integrity of population evolution information. We conducted extensive comparisons with current algorithms on multiple benchmark datasets and real-world problems. The experimental results show that the proposed algorithm is superior to the compared algorithms and exhibits strong competitiveness in practical applications.