Learning-based heuristics for solving combinatorial optimization problems has recently attracted much academic attention. While most of the existing works only consider the single objective problem with simple constraints, many real-world problems have the multiobjective perspective and contain a rich set of constraints. This paper proposes a multiobjective deep reinforcement learning with evolutionary learning algorithm for a typical complex problem called the multiobjective vehicle routing problem with time windows (MO-VRPTW). In the proposed algorithm, the decomposition strategy is applied to generate subproblems for a set of attention models. The comprehensive context information is introduced to further enhance the attention models. The evolutionary learning is also employed to fine-tune the parameters of the models. The experimental results on MO-VRPTW instances demonstrate the superiority of the proposed algorithm over other learning-based and iterative-based approaches.

中文翻译：

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MODRL/D-EL：多目标深度强化学习与多目标优化的进化学习

用于解决组合优化问题的基于学习的启发式方法最近引起了学术界的广泛关注。虽然大多数现有工作仅考虑具有简单约束的单目标问题，但许多现实世界问题具有多目标视角并包含一组丰富的约束。本文提出了一种多目标深度强化学习和进化学习算法，用于解决一个典型的复杂问题，称为带时间窗的多目标车辆路径问题 (MO-VRPTW)。在所提出的算法中，分解策略用于为一组注意力模型生成子问题。引入了全面的上下文信息以进一步增强注意力模型。进化学习也被用来微调模型的参数。