GPU-PSO: Parallel Particle Swarm Optimization Approaches on Graphical Processing Unit for Constraint Reasoning: Case of Max-CSPs

Abstract

Abstract Constraint Satisfaction Problems (CSPs) occur now in different domains. Several methods are used to solve them. In particular, Particle Swarm Optimization (PSO) allows to solve efficiently CSPs by significantly reducing the calculation time to explore the search space of solutions. However, this metaheuristic is excessively costing when facing large instances. In this paper we address the Maximal Constraint Satisfaction Problems (Max-CSPs). We introduce a new resolution approach that allows solving efficiently the Max-CSPs even with large instances. Our purpose is to implement a PSO based method by using the GPU architecture as a parallel computing framework. In particular, we focus on the implementation of two parallel novel approaches. The first one is a parallel GPU-PSO for Max-CSPs (GPU-PSO) and the second one is a GPU distributed PSO for Max-CSPs (GPU-DPSO). Our experimental results show the efficiency of the two proposed approaches and their ability to exploit GPU architecture.