  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.
