A Coevolutionary Response Framework for Dynamic Constrained Multi-Objective Optimization Problems

Dynamic constrained multi-objective optimization problems (DCMOPs) present significant challenges due to the evolving nature of both objectives and constraints. These problems require optimization algorithms that can efficiently adapt to dynamic environments while maintaining a balance between convergence and diversity. To address these challenges, we propose a novel cooperative response dynamic constrained multi-objective optimization (CRDCMO) framework. The framework introduces two key strategies: (1) population reinitialization guided by historical environmental information, tailored to different types of environmental changes, and (2) dynamic adjustment of auxiliary population tasks, optimizing resource allocation with a focus on tracking the constrained Pareto-optimal front (CPF). These strategies enhance the algorithm’s adaptability to environmental changes and improve CPF tracking efficiency. The CRDCMO framework is extensively evaluated on several benchmark test suites, as well as a real-world energy optimization problem. Experimental results demonstrate that CRDCMO outperforms seven state-of-the-art algorithms, underscoring its effectiveness and robustness in dynamic environments. This framework not only provides a comprehensive solution for DCMOPs but also contributes to the advancement of dynamic optimization algorithms.