A Lightweight CNN-Transformer Model for Learning Traveling Salesman Problems

• URL: http://arxiv.org/abs/2305.01883v2
• Date: Wed, 6 Mar 2024 01:45:16 GMT
• Title: A Lightweight CNN-Transformer Model for Learning Traveling Salesman Problems
• Authors: Minseop Jung, Jaeseung Lee, Jibum Kim
• Abstract summary: CNN-Transformer model is able to better learn spatial features from input data using a CNN embedding layer. The proposed model exhibits the best performance in real-world datasets.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Several studies have attempted to solve traveling salesman problems (TSPs) using various deep learning techniques. Among them, Transformer-based models show state-of-the-art performance even for large-scale Traveling Salesman Problems (TSPs). However, they are based on fully-connected attention models and suffer from large computational complexity and GPU memory usage. Our work is the first CNN-Transformer model based on a CNN embedding layer and partial self-attention for TSP. Our CNN-Transformer model is able to better learn spatial features from input data using a CNN embedding layer compared with the standard Transformer-based models. It also removes considerable redundancy in fully-connected attention models using the proposed partial self-attention. Experimental results show that the proposed CNN embedding layer and partial self-attention are very effective in improving performance and computational complexity. The proposed model exhibits the best performance in real-world datasets and outperforms other existing state-of-the-art (SOTA) Transformer-based models in various aspects. Our code is publicly available at https://github.com/cm8908/CNN_Transformer3.

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