Hybrid Genetic Search for the CVRP: Open-Source Implementation and SWAP* Neighborhood

TL;DR

This paper introduces an open-source implementation of a hybrid genetic algorithm for the capacitated vehicle routing problem, featuring a novel SWAP* neighborhood that enhances local search performance and maintains state-of-the-art results.

Contribution

It provides a simple, open-source version of the HGS algorithm with methodological improvements, including the SWAP* neighborhood, for solving the CVRP efficiently.

Findings

The open-source HGS performs competitively on benchmark instances.

SWAP* neighborhood significantly improves local search effectiveness.

HGS remains a leading metaheuristic in solution quality and speed.

Abstract

The vehicle routing problem is one of the most studied combinatorial optimization topics, due to its practical importance and methodological interest. Yet, despite extensive methodological progress, many recent studies are hampered by the limited access to simple and efficient open-source solution methods. Given the sophistication of current algorithms, reimplementation is becoming a difficult and time-consuming exercise that requires extensive care for details to be truly successful. Against this background, we use the opportunity of this short paper to introduce a simple -- open-source -- implementation of the hybrid genetic search (HGS) specialized to the capacitated vehicle routing problem (CVRP). This state-of-the-art algorithm uses the same general methodology as Vidal et al. (2012) but also includes additional methodological improvements and lessons learned over the past decade of research. In particular, it includes an additional neighborhood called SWAP* which consists in exchanging two customers between different routes without an insertion in place. As highlighted in our study, an efficient exploration of SWAP* moves significantly contributes to the performance of local searches. Moreover, as observed in experimental comparisons with other recent approaches on the classical instances of Uchoa et al. (2017), HGS still stands as a leading metaheuristic regarding solution quality, convergence speed, and conceptual simplicity.