# A hybrid adaptive Iterated Local Search with diversification control to the Capacitated Vehicle Routing Problem

arXiv: https://arxiv.org/abs/2012.11021 · HTML: https://mlsanity.com/paper/2012.11021/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2020.

## Built on by (most cited first, 4 of 4)

- [ARS: Automatic Routing Solver with Large Language Models](https://mlsanity.com/paper/2502.15359/) (2025)
- [Heuristics for Vehicle Routing Problem: A Survey and Recent Advances](https://mlsanity.com/paper/2303.04147/) (2023)
- [Survey on Neural Routing Solvers](https://mlsanity.com/paper/2602.21761/) (2026)
- [Enhancing CVRP Solver through LLM-driven Automatic Heuristic Design](https://mlsanity.com/paper/2602.23092/) (2026)

## Abstract (arXiv)

Metaheuristics are widely employed to solve hard optimization problems, like vehicle routing problems (VRP), for which exact solution methods are impractical. In particular, local search-based metaheuristics have been successfully applied to the capacitated VRP (CVRP). The CVRP aims at defining the minimum-cost delivery routes for a given set of identical vehicles since each vehicle only travels one route and there is a single (central) depot. The best metaheuristics to the CVRP avoid getting stuck in local optima by embedding specific hill-climbing mechanisms such as diversification strategies into the solution methods. This paper introduces a hybridization of a novel adaptive version of Iterated Local Search with Path-Relinking (AILS-PR) to the CVRP. The major contribution of this paper is an automatic mechanism to control the diversity step of the metaheuristic to allow it to escape from local optima. The results of experiments with 100 benchmark CVPR instances show that AILS-PR outperformed the state-of-the-art CVRP metaheuristics.