A deep reinforcement learning algorithm framework for solving multi-objective traveling salesman problem based on feature transformation

• Published in: Neural networks Vol. 176; p. 106359
• Main Authors: Zhao, Shijie, Gu, Shenshen
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.08.2024
• Subjects: Algorithms; Deep Learning; Deep reinforcement learning; Feature transformation; Graph pointer network; Multi-objective traveling salesman problem; Neural Networks, Computer; Problem Solving; Reinforcement, Psychology; Robotics - methods; Multi-objective traveling salesman problem; Deep reinforcement learning; Feature transformation; Graph pointer network
• ISSN: 0893-6080; 1879-2782; 1879-2782
• DOI: 10.1016/j.neunet.2024.106359

As a special type of multi-objective combinatorial optimization problems (MOCOPs), the multi-objective traveling salesman problem (MOTSP) plays an important role in practical fields such as transportation and robot control. However, due to the complexity of its solution space and the conflicts between different objectives, it is difficult to obtain satisfactory solutions in a short time. This paper proposes an end-to-end algorithm framework for solving MOTSP based on deep reinforcement learning (DRL). By decomposing strategies, solving MOTSP is transformed into solving multiple single-objective optimization subproblems. Through linear transformation, the features of the MOTSP are combined with the weights of the objective function. Subsequently, a modified graph pointer network (GPN) model is used to solve the decomposed subproblems. Compared with the previous DRL model, the proposed algorithm can solve all the subproblems using only one model without adding weight information as input features. Furthermore, our algorithm can output a corresponding solution for each weight, which increases the diversity of solutions. In order to verify the performance of our proposed algorithm, it is compared with four classical evolutionary algorithms and two DRL algorithms on several MOTSP instances. The comparison shows that our proposed algorithm outperforms the compared algorithms both in terms of training time and the quality of the resulting solutions.