A Lightweight CNN-Transformer Model for Learning Traveling Salesman Problems

Understand

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.