Learning Enhanced Optimisation for Routing Problems

TL;DR

This paper introduces L2GLS, a reinforcement learning-based method that enhances local search for routing problems, achieving state-of-the-art results on large TSP and CVRP instances by adaptively guiding search efforts.

Contribution

It proposes a novel learning-based approach combining penalty terms and reinforcement learning to improve local search in routing problems, surpassing existing machine learning methods.

Findings

L2GLS outperforms previous ML methods on large TSP and CVRP instances.

The approach effectively escapes local optima using adaptive penalty adjustments.

L2GLS achieves new state-of-the-art results in routing problem solutions.

Abstract

Deep learning approaches have shown promising results in solving routing problems. However, there is still a substantial gap in solution quality between machine learning and operations research algorithms. Recently, another line of research has been introduced that fuses the strengths of machine learning and operational research algorithms. In particular, search perturbation operators have been used to improve the solution. Nevertheless, using the perturbation may not guarantee a quality solution. This paper presents "Learning to Guide Local Search" (L2GLS), a learning-based approach for routing problems that uses a penalty term and reinforcement learning to adaptively adjust search efforts. L2GLS combines local search (LS) operators' strengths with penalty terms to escape local optimals. Routing problems have many practical applications, often presetting larger instances that are still challenging for many existing algorithms introduced in the learning to optimise field. We show that L2GLS achieves the new state-of-the-art results on larger TSP and CVRP over other machine learning methods.