Utilizing various auxiliary optimization problems (AOPs) to help the optimization for constrained multiobjective problems (CMOPs) has recently drawn substantial attention. However, two key issues remain underexplored: the design of effective AOPs and the efficient allocation of iteration resources for these AOPs. Specifically, the design of AOPs directly affects the ability to identify high-quality solutions, while an effective allocation mechanism can reduce wasted iterations on less promising AOPs. In this study, we propose a novel algorithm, DRLAOP, to tackle these challenges. DRLAOP begins by analyzing the intrinsic optimization requirements of CMOPs and designs AOPs accordingly. Then, it employs a DRL-guided iteration resource allocation (DRL-IRA) mechanism to dynamically map the optimization landscape and allocate iteration resources to the most promising AOPs. Comparative experiments are carried out on 33 benchmark CMOP instances and nine real-world applications, with 19 state-of-the-art algorithms. The results demonstrate that DRLAOP consistently outperforms or matches the performance of its peers, validating that DRLAOP not only excels in discovering optimal solutions but also ensures efficient use of iteration resources.

Effectively managing convergence, diversity, and feasibility constitutes a fundamental trinity of tasks in optimizing constrained multiobjective optimization problems (CMOPs). Nevertheless, contemporary constrained multiobjective evolutionary algorithms (CMOEAs) frequently encounter challenges in reconciling these imperatives simultaneously. Drawing inspiration from overwhelming success in artificial intelligence, we propose a deep reinforcement learning-guided coevolutionary algorithm (DRLCEA) to tackle this predicament. DRLCEA employs two populations to optimize the original and unconstrained versions of the CMOP, respectively and then fosters cooperation between them according to the guidance of DRL. The established DRL employs two evaluation metrics to appraise population convergence, diversity, and feasibility, thus remarkably proficient in reflecting and steering the coevolution . Therefore, the proposed DRLCEA could effectively locate the feasible regions and approximate the constrained Pareto front. We assess the proposed algorithm on 32 benchmark CMOPs and one real-world UAV emergency track planning (UETP) application. Experimental results undoubtedly demonstrate the superiority and robustness of the proposed DRLCEA.