Dynamic Multi-objective Optimization Problem (DMOP) is emerging in recent years as a major real-world optimization problem receiving considerable attention. Tracking the movement of Pareto front efficiently and effectively over time has been a central issue in solving DMOPs. In this paper, a reinforcement learning-based dynamic multi-objective evolutionary algorithm, called RL-DMOEA, which seamlessly integrates reinforcement learning framework and three change response mechanisms, is proposed for solving DMOPs. The proposed algorithm relocates the individuals based on the severity degree of environmental changes, which is estimated through the corresponding changes in the objective space of their decision variables. When identifying different severity degree of environmental changes, the proposed RL-DMOEA approach can learn better evolutionary behaviors from environment information, based on which apply the appropriate response mechanisms. Specifically, these change response mechanisms including the knee-based prediction, center-based prediction and indicator-based local search, are devised to promote both convergence and diversity of the algorithm under different severity of environmental changes. To verify this idea, the proposed RL-DMOEA is evaluated on CEC 2015 test problems involving various problem characteristics. Empirical studies on chosen state-of-the-art designs validate that the proposed RL-DMOEA is effective in addressing the DMOPs.

近年来，动态多目标优化问题（DMOP）逐渐成为人们关注的主要现实优化问题。长期有效地跟踪帕累托战线的移动一直是解决DMOP的核心问题。本文提出了一种基于强化学习的动态多目标进化算法RL-DMOEA，该算法将强化学习框架与三种变化响应机制无缝集成，用于求解DMOP。所提出的算法根据环境变化的严重程度对个体进行重新定位，这是通过决策变量的目标空间的相应变化来估计的。在确定不同程度的环境变化严重程度时，提出的RL-DMOEA方法可以从环境信息中学习更好的进化行为，并在此基础上应用适当的响应机制。具体来说，这些变化响应机制包括基于膝盖的预测，基于中心的预测和基于指标的局部搜索，旨在在不同的环境变化严重性下促进算法的收敛性和多样性。为了验证这一想法，在涉及各种问题特征的CEC 2015测试问题上对提出的RL-DMOEA进行了评估。对所选最新设计的经验研究证实，所提出的RL-DMOEA在解决DMOP方面是有效的。设计了基于中心的预测和基于指标的局部搜索，以在不同环境变化的严重性下促进算法的收敛性和多样性。为了验证这一想法，在涉及各种问题特征的CEC 2015测试问题上对提出的RL-DMOEA进行了评估。对所选最新设计的经验研究证实，提出的RL-DMOEA可有效解决DMOP。设计了基于中心的预测和基于指标的局部搜索，以在不同环境变化的严重性下促进算法的收敛性和多样性。为了验证这一想法，在涉及各种问题特征的CEC 2015测试问题上对提出的RL-DMOEA进行了评估。对所选最新设计的经验研究证实，所提出的RL-DMOEA在解决DMOP方面是有效的。