The core message of this paper is to present a new evolutionary algorithm structure that utilizes a reinforcement learning-based agent to adaptively select the most appropriate evolutionary operators during the optimization process, leading to improved performance in solving complex multi-objective optimization problems.
The proposed Collaborative Pareto Set Learning (CoPSL) framework simultaneously learns the Pareto sets of multiple multi-objective optimization problems in a collaborative manner, leveraging shared representations across the problems to improve efficiency and performance.