A hybrid network with spatial attention mechanism for solving large-scale TSP

Lin Ke; Xiaoxiao Yang; Zhibin Chen

doi:10.1117/12.3004816

9 October 2023 A hybrid network with spatial attention mechanism for solving large-scale TSP

Lin Ke, Xiaoxiao Yang, Zhibin Chen

Proceedings Volume 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023); 1279102 (2023) https://doi.org/10.1117/12.3004816
Event: Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 2023, Qingdao, SD, China

Abstract

In combinatorial optimization, Traveling Salesman Problem (TSP) is a well-known issue and one of the key problem in the logistics industry in recent years. The speed, accuracy, and generalization ability of the traditional method and the algorithm design of the specific problem are greatly affected. With the wide application of deep reinforcement learning (DRL) in industry, the automatic design of learning algorithms using DRL models has become a recent research hotspot. This paper proposes a hybrid network model with a spatial attention mechanism to resolve large-scale TSP in order to enhance the generalization capability of DRL-based model on large-scale TSP. Spatial attention is beneficial to capture the global connection between nodes and then to calculate and extract key features by weighting all local features, which is beneficial to improving the generalization ability of the model. The experimental results demonstrate that our model can significantly increase the route problem's optimization effectiveness.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Lin Ke, Xiaoxiao Yang, and Zhibin Chen "A hybrid network with spatial attention mechanism for solving large-scale TSP", Proc. SPIE 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 1279102 (9 October 2023); https://doi.org/10.1117/12.3004816

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