GPU-based parallel multi-objective particle swarm optimization for large swarms and high dimensional problems

Abstract

During the last couple of years, parallel MOPSO (Multi-objective Particle Swarm Optimization) with two or more objectives has gained a lot of attention in the literature on GPU computing. A number of implementations have been published for MOPSO on a GPU. However, none of them have been able to capture good enough Pareto fronts fast. In addition, the authors have pointed out their limitations in various aspects such as archive handling, picking up fewer nondominated solutions and so on. Previous literature also lacks evaluation of its MOPSO implementation with large swarms and high dimensional problems. This paper presents a faster implementation of parallel MOPSO on a GPU based on the CUDA architecture. We achieved our faster implementation by using coalescing memory access, a fast pseudorandom number generator, Thrust library, CUB library, an atomic function, parallel archiving and so on. The proposed parallel implementation of MOPSO using a master-slave model provides up to 157 times speedup compared to the corresponding CPU implementation. As the proposed implementation performs very highly even with increased size of problem dimensionality and swarm population, it can be widely used in real world optimization problems.