
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.


coremsg

r2-indicator-and-deep-reinforcement-learning-enhanced-adaptive-multi-objective-evolutionary-algorithm-for-solving-complex-optimization-problems


R2 Indicator and Deep Reinforcement Learning Enhanced Adaptive Multi-Objective Evolutionary Algorithm for Solving Complex Optimization Problems


title_rewrite


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.


collaborative-learning-of-pareto-sets-for-multiple-multi-objective-optimization-problems


Collaborative Learning of Pareto Sets for Multiple Multi-Objective Optimization Problems
