Guangyin Jin

A hierarchical reinforcement learning based subtask-coordinated scheduling method for constrained multi-objective evolutionary algorithm

Experimental results demonstrate that the proposed method consistently outperforms nine state-of-the-art constrained multi-objective evolutionary algorithms (CMOEAs) in terms of convergence, feasibility, and diversity, thereby confirming its effectiveness and strong general applicability.