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

TL;DR

This paper presents a hybrid adaptive Iterated Local Search with diversification control for the Capacitated Vehicle Routing Problem, demonstrating superior performance over existing metaheuristics on benchmark instances.

Contribution

Introduces a novel adaptive Iterated Local Search with Path-Relinking and an automatic diversity control mechanism for CVRP.

Findings

Outperforms state-of-the-art CVRP metaheuristics on benchmark instances.

Effective automatic control of diversification enhances solution quality.

Demonstrates robustness across diverse CVRP problem sets.

Abstract

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.