Solving constrained multiobjective optimization problems (CMOPs) by constrained multiobjective evolutionary algorithms (CMOEAs) has been a timely research topic in recent years. While various improvement strategies have been proposed in existing studies, the dominance-based and decomposition-based frameworks are usually used independently, despite their complementary characteristics on different problem types-dominance excels in feasibility handling while decomposition offers directional guidance-which could jointly enhance search performance when properly integrated. With this in mind, this article proposes a coevolutionary algorithm using both dominance-based and decomposition-based frameworks to coevolve two populations, thereby leveraging their respective advantages. Specifically, the dominance-based population optimizes a dynamic problem derived from the original problem and achieves diversity preservation through a tolerance-based selection strategy, while the decomposition-based population focuses on the unconstrained Pareto front in the early stage and the constrained Pareto front in the later stage through stage identification, objective switching, and relevance-based selection strategy, thereby directly addressing the limitation of isolated framework usage. In addition, populations with different frameworks are capable of sharing information between parents and offspring during offspring generation and environmental selection, respectively, enabling mutual reinforcement that existing single-framework or loosely coupled approaches lack. Experimental results with 11 state-of-the-art CMOEAs on four benchmark suites and five real-world CMOPs demonstrate the performance advantages of the proposed algorithm.

Existing constrained multiobjective evolutionary algorithms (CMOEAs) frequently employ the information provided by the unconstrained Pareto front (UPF) to facilitate the identification of the constrained Pareto front (CPF) for constrained multiobjective optimization problems (CMOPs). However, obtaining a UPF with favorable convergence and diversity is not straightforward, and the obtained UPF is sometimes difficult to effectively assist in the identification of CPF for certain complex CMOPs. To this end, a novel algorithm called DREMCO is proposed, which endeavors to obtain a good UPF and is capable of utilizing the obtained UPF to consistently assist in the identification of CPF. DREMCO consists of a main population for the original problem and a two-phase (propulsion phase and recovery phase) auxiliary population with a dynamics regulation mechanism. In the propulsion phase, the auxiliary population ignores constraints and employs an improved aggregation function to obtain a good UPF, thereby pulling the main population across infeasible regions. In the recovery phase, the auxiliary population uses a penalty function method to converge to CPF and continuously refines the CPF of the main population. Concurrently, a novel phase judgment method is proposed for seamless transition between phases. Furthermore, an information-sharing strategy is proposed, which is capable of sharing information of the parents and offspring in offspring generation and environment selection, respectively. The experimental results with 11 state-of-the-art CMOEAs on five benchmark suites and eight real-world CMOPs demonstrate the efficacy of the proposed algorithm.