Auxiliary Optimization With Resource Allocation for Constrained Multiobjective Problems
提出DRLAOP算法，通过设计有效的辅助优化问题并利用深度强化学习动态分配迭代资源，在33个基准问题和9个实际应用中优于或匹敌19种前沿算法。
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.