文献类型：期刊文献

英文题名：A Dual-Stage and Dual-Population Algorithm Based on Chemical Reaction Optimization for Constrained Multi-Objective Optimization

作者：Zhang, Tianyu[1];Guo, Xin[2];Li, Yan[2];Li, Na[2];Zheng, Ruochen[2];Dong, Wenbo[1];Ding, Weichao[1]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200231, Peoples R China;[2]Sinopec Res Inst Petr Proc Co Ltd, Beijing 100083, Peoples R China

年份：2025

卷号：13

期号：8

外文期刊名：PROCESSES

收录：;EI(收录号：20253519078200);WOS:【SCI-EXPANDED(收录号:WOS:001557608200001)】；

基金：This research was funded by the 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).

语种：英文

外文关键词：constrained multi-objective optimization; chemical reaction optimization; two-species synergy; multi-stage evolution

摘要：Constrained multi-objective optimization problems (CMOPs) require optimizing multiple conflicting objectives while satisfying complex constraints. These constraints generate infeasible regions that challenge traditional algorithms in balancing feasibility and Pareto frontier diversity. chemical reaction optimization (CRO) effectively balances global exploration and local exploitation through molecular collision reactions and energy management, thereby enhancing search efficiency. However, standard CRO variants often struggle with CMOPs due to the absence of specialized constraint-handling mechanisms. To address these challenges, this paper integrates the CRO collision reaction mechanism with an existing evolutionary computational framework to design a dual-stage and dual-population chemical reaction optimization (DDCRO) algorithm. This approach employs a staged optimization strategy, which divides population evolution into two phases. The first phase focuses on objective optimization to enhance population diversity, and the second prioritizes constraint satisfaction to accelerate convergence toward the constrained Pareto front. Furthermore, to leverage the infeasible solutions' guiding potential during the search, DDCRO adopts a two-population strategy. At each stage, the main population tackles the original constrained problem, while the auxiliary population addresses the corresponding unconstrained version. A weak complementary mechanism facilitates information sharing between populations, which enhances search efficiency and algorithmic robustness. Comparative tests on multiple test suites reveal that DDCRO achieves optimal IGD/HV values in 53% of test problems. The proposed algorithm outperforms other state-of-the-art algorithms in both convergence and population diversity.