A Prediction and Weak Coevolution-Based Dynamic Constrained Multiobjective Optimization

• Published in: IEEE transactions on evolutionary computation Vol. 29; no. 4; pp. 1328 - 1342
• Main Authors: Gong, Dunwei, Rong, Miao, Hu, Na, Wang, Yan, Pedrycz, Witold, Yang, Shengxiang
• Format: Journal Article
• Language: English
• Published: IEEE 01.08.2025
• Subjects: Constraint; Convergence; dynamic multiobjective optimization; Evolutionary computation; exploration and exploitation; Heuristic algorithms; Optimization; Power system dynamics; prediction; Sociology; Statistics; weak coevolution
• ISSN: 1089-778X; 1941-0026
• DOI: 10.1109/TEVC.2024.3418470

Dynamic multiobjective evolutionary algorithms (DMOEAs) have gained great popularity in dealing with the dynamic multiobjective optimization problems (DMOPs). However, the existing studies have difficulties in tackling DMOPs subject to (dynamic) constraints. In this article, we propose a prediction and weak coevolutionary multiobjective optimization algorithm (PWDCMO) to handle the dynamic constrained multiobjective optimization problems (DCMOPs), where a prediction strategy is employed to forecast potential optimal regions under the new environment, with a weak coevolutionary constrained multiobjective optimization (CCMO) as the optimizer aiming at balancing exploration and convergence. The proposed method is compared with the four popular dynamic constrained multiobjective evolutionary algorithms (DCMOEAs) on six test instances from two various test suites with their convergence and the overall performance being discussed. Furthermore, the performance of the proposed prediction strategy is also investigated to observe its impact on the final results. Additionally, the PWDCMO is employed in the optimization of an integrated coal mine energy system (ICMES) to validate the proficiency in addressing real world problems. Experimental results demonstrate the superiority of PWDCMO.