An Adaptive version of Parallel MPSO with OpenMP for Uncapacitated Facility Location Problem

As a consequence of globalization, facility's location selection has become a markedly complicated problem. This problem is one of the most difficult combinatorial NP-hard optimization problems. Historically, this kind of problems have been usually solved by linear programming or metaheuristics methods such as genetic algorithms (GA), simulated annealing (SA), Particle Swarm Optimization (PSO) and tabu searches with optima or near-optima. In this paper, an adaptive parallel multi-population particle swarm optimization (MPSO) algorithm with OpenMP is presented for the Uncapacitated Facility Location problem (UFLP), The linear inertia weight was introduced which made an ideal balance between the capability of global exploration and the capability of local exploitation. The aim of this paper is to implement an adaptive version of parallel MPSO method augmented with OpenMP directives and then applied it to the several benchmark suites offered by OR library. It is shown that dramatic improvement in terms of CPU times is achieved with competitive results by using a parallel programming model in a multi-core desktop.