Combining genetic local search into a multi-population Imperialist Competitive Algorithm for the Capacitated Vehicle Routing Problem

Abstract

The Vehicle Routing Problem (VRP) is one of the most significant problems in operational research today. VRP has a vast range of application fields such as transportation, logistics, manufacturing, relief systems and communication. To suit the needs of different real-world VRP scenarios, many models of VRP have been developed — CVRP (Capacitated VRP) being the classical form. In this article, a hybrid metaheuristic algorithm, ICAHGS, is proposed for solving CVRP. The present study proposes a refined Imperialist Competitive Algorithm (ICA) as the primary evolutionary and multi-population method for addressing the Capacitated Vehicle Routing Problem (CVRP). In order to further optimize the search process, a Hybrid Genetic Search (HGS-CVRP) algorithm is applied as an enhanced local search and population management strategy within the ICA framework. Additionally, the internal restart step of the HGS-CVRP algorithm is replaced with a multi-step restart mechanism for intensification improvement. One notable aspect of the proposed method is its ability to facilitate parallel processing, with each empire able to be processed on a separate processor. This structure allows for increased computational efficiency in addressing the CVRP. To assess the effectiveness of the proposed algorithm, it has been compared to several state-of-the-art algorithms from the literature. The results of this comparison, which include both classical benchmark instances and real-world applications, demonstrate the competitive performance of the proposed algorithm.