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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2026  > In Press, Accepted Manuscript
Q. Chen, Q. Zhao, R. Yuan, and R. Wang, “Multi-population co-evolutionary algorithm using an adaptive integration of response strategies for dynamic constrained multi-objective optimization,” IEEE/CAA J. Autom. Sinica, early access, 2026. doi: 10.1109/JAS.2026.126224
Citation: Q. Chen, Q. Zhao, R. Yuan, and R. Wang, “Multi-population co-evolutionary algorithm using an adaptive integration of response strategies for dynamic constrained multi-objective optimization,” IEEE/CAA J. Autom. Sinica, early access, 2026. doi: 10.1109/JAS.2026.126224
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Multi-Population Co-Evolutionary Algorithm Using an Adaptive Integration of Response Strategies for Dynamic Constrained Multi-Objective Optimization

doi: 10.1109/JAS.2026.126224
Funds:  This work was supported by the Science and Technology Project for Young and Middle-aged Talents of Hunan (2023TJ-Z03) and the Science Fund for Creative Research Groups of the National Natural Science Foundation of China (72421002)
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    Abstract
  • Dynamic constrained multi-objective optimization problems (DCMOPs) encompass multiple objective functions, constraints, and parameters that are subject to temporal variation. The feasible region and the constrained Pareto-optimal front (CPF) undergo complex changes, posing significant challenges for evolutionary algorithms. However, most existing dynamic constrained multi-objective evolutionary algorithms (DCMOEAs) do not fully exploit information from potentially valuable infeasible solutions and struggle to adapt effectively to complex environmental changes using a single response strategy. To address these challenges, this paper proposes a multi-population co-evolutionary algorithm using an adaptive integration of response strategies for DCMOPs. Specifically, an adaptive integration of response strategies is developed to dynamically adjust the number of solutions generated by diversity-, memory-, and prediction-based response strategies in accordance with the severity and pattern of environmental changes. Additionally, a multi-population co-evolutionary approach is introduced to effectively utilize information from valuable infeasible solutions, enabling thorough exploration of optimal regions in static optimization. Extensive experiments on two benchmark test suites and a real-world power management problem demonstrate that the proposed method outperforms nine state-of-the-art DCMOEAs in solving DCMOPs across four metrics.

     

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