A hybrid Dragonfly algorithm for the vehicle routing problem with stochastic demands

Abstract

A number of swarm intelligence algorithms have been proposed during the last years. Most of them are suitable for the solution of continuous optimization problems. One of them is the Dragonfly Algorithm that has been proved very efficient in the problems in which it has been applied. However, few of the newly proposed algorithms have been used for the solution of a routing problem. In this paper, a new hybridized version of the Dragonfly Algorithm with the Combinatorial Expanding Neighborhood Topology is proposed and analyzed in details. The proposed Combinatorial Expanding Neighborhood Topology Dragonfly Algorithm is an algorithm that combines a very powerful swarm intelligence algorithm, the Dragonfly algorithm, with a very effective procedure, the Combinatorial Expanding Neighborhood Topology. This algorithm was used for solving a well known routing problem, the Vehicle Routing Problem with Stochastic Demands. The algorithm was tested in 40 benchmark instances from the literature and gave, in some of them new, best solutions. It was, also, compared with 10 other swarm intelligence algorithms from the literature proving its effectiveness, as it was ranked in the first place among all the algorithms.

Keywords

• Dragonfly algorithm
• Vehicle routing problem with stochastic demands
• Combinatorial expanding neighborhood topology
• Path relinking
• Variable neighborhood search