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洞見 - Multi-objective optimization
R2 Indicator and Deep Reinforcement Learning Enhanced Adaptive Multi-Objective Evolutionary Algorithm for Solving Complex Optimization Problems
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.
Collaborative Learning of Pareto Sets for Multiple 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.
