An Auxiliary Problem-Assisted Evolutionary Algorithm With Dynamics Regulation for Complex Constrained Multiobjective Optimization
提出DREMCO算法，通过主种群和带动态调节的两阶段辅助种群，利用无约束帕累托前沿辅助识别约束帕累托前沿，在五个基准套件和八个实际问题上验证了有效性。
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.