Swarm intelligence and nature inspired algorithms for solving vehicle routing problems: a survey

Themistoklis Stamadianos (), Andromachi Taxidou (), Magdalene Marinaki () and Yannis Marinakis ()
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Themistoklis Stamadianos: Technical University of Crete
Andromachi Taxidou: Technical University of Crete
Magdalene Marinaki: Technical University of Crete
Yannis Marinakis: Technical University of Crete

Operational Research, 2024, vol. 24, issue 3, No 13, 45 pages

Abstract: Abstract Vehicle routing problem (VRP) is a classic NP-hard optimization problem. It is generally accepted that an optimized routing scheme can cause huge difference in the cost in all stages of transportation. Consequently, the VRP has evoked interest among the researchers of the field. Usually, a metaheuristic or an evolutionary algorithm is used for the solution of a VRP variant. In the last years, a number of swarm intelligence algorithms have been used for the solution of the problem. Initially, the two most classic swarm intelligence algorithms, the Ant Colony Optimization and the Particle Swarm Optimization, were used for the solution of this kind of problems. However, in the last years, more and more researchers solved the problem using a different swarm intelligence algorithm. In this paper, we focused in the presentation and analysis of the swarm intelligence algorithms that have been used for the solution of the problem. We give the advantages and disadvantages of each method, we focus in those ones that produced the best results in difficult VRPs and we present directions for the future of this kind of algorithms for the solution of a VRP variant.

Keywords: Vehicle routing problem; Swarm intelligence; Nature inspired algorithms; Particle Swarm Optimization; Ant Colony Optimization; Firefly Algorithm; Bat Algorithm; Honey Bees Mating Optimization Algorithm; Bumble Bees Mating Optimization Algorithm; Cuckoo Search (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s12351-024-00862-5

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