MODRL/D-EL: Multiobjective Deep Reinforcement Learning with Evolutionary Learning for Multiobjective Optimization

• URL: http://arxiv.org/abs/2107.07961v1
• Date: Fri, 16 Jul 2021 15:22:20 GMT
• Title: MODRL/D-EL: Multiobjective Deep Reinforcement Learning with Evolutionary Learning for Multiobjective Optimization
• Authors: Yongxin Zhang, Jiahai Wang, Zizhen Zhang, Yalan Zhou
• Abstract summary: 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. The experimental results on MO-VRPTW instances demonstrate the superiority of the proposed algorithm over other learning-based and iterative-based approaches.
• Score: 10.614594804236893
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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