Revisit the Algorithm Selection Problem for TSP with Spatial Information Enhanced Graph Neural Networks

Ya Song, Laurens Bliek, Yingqian Zhang

Abstract: Algorithm selection is a well-known problem where researchers investigate how to construct useful features representing the problem instances and then apply feature-based machine learning models to predict the best algorithm for each instance. However, even for simple optimization problems like Euclidean Traveling Salesman Problem (TSP), there lacks a general and effective feature representation for problem instances. The important features of TSP are relatively well understood in the literature, based on extensive domain knowledge and post-analysis of the solutions. In recent years, Convolutional Neural Network (CNN) has gained popularity for TSP algorithm selection. Compared to traditional feature-based models, CNN has an automatic feature-learning ability and demands less domain expertise. However, it is still required to generate intermediate representations, i.e., multiple images to represent TSP instances first. In this paper, we revisit algorithm selection for TSP and propose GINES, a new Graph Neural Network (GNN) that uses city coordinates and distances as input. GINES introduces a novel message-passing mechanism and local feature extractor to learn TSP’s spatial information. Evaluation of two benchmarks shows GINES outperforms CNN and GINE models and surpasses traditional feature-based methods on one dataset. Our codes and datasets are available at https://github.com/lurenyi233/GINES TSP.

External IDs:doi:10.5220/0013153400003890