Combinatorial expanding neighborhood topology particle swarm optimization for the vehicle routing problem with stochastic demands

• Authors: Yannis Marinakis;Magdalene Marinaki
• Venue: Proceedings of the 15th annual conference on Genetic and evolutionary computation
• Year: 2013

This paper introduces a new algorithmic nature inspired approach that uses Particle Swarm Optimization (PSO) with different neighborhood topologies for successfully solving one of the most computationally complex problems, the Vehicle Routing Problem with Stochastic Demands. The proposed method (the Combinatorial Expanding Neighborhood Topology Particle Swarm Optimization (CENTPSO)) by using an expanding neighborhood topology manages to increase the performance of the algorithm. The algorithm starts from a small size neighborhood. In each iteration the size of the neighborhood is increased and it ends to a neighborhood that includes all the swarm. By doing this, it manages to take advantage of the exploration abilities of a global neighborhood structure and of the exploitation abilities of a local neighborhood structure. A different way is proposed to calculate the position of each particle which will not lead to any loose of information and will speed up the whole procedure. This is achieved by a replacement of the equation of positions with a novel procedure that includes a Path Relinking Strategy and by a different role of the velocities of the particles. The algorithm is tested on a set of benchmark instances from the literature finding new best solutions in 27 of 40 instances.