A novel discrete particle swarm optimization for p-median problem

p-Median problem is a well-known discrete optimization problem aiming to locate p number of facilities that satisfies the demand of multiple places with minimum cost. Even though continuous particle swarm optimization (PSO) has been successfully applied to many areas in recent years, discrete PSO algorithm is in its infancy. In this paper, a new discrete particle swarm optimization algorithm (NDPSO) is proposed for the p-median problem. Although presented algorithm has all major characteristics of the classical particle swarm optimization (PSO), its search strategy is different. The algorithm aims to minimize the distance between demand points and facilities. The algorithm has been tested on benchmarking problem instances from OR library and its performance is compared with other algorithms in the literature such as neural model, reduced variable neighborhood search, and simulated annealing. The presented method is also compared with two other existing discrete PSO algorithms in the literature. The experiments have shown that the proposed algorithm highly outperforms all the methods compared with better computational time.