Comparison of Fine Grained and Coarse Grained Parallel Models in Particle Swarm Optimization Algorithm

Many optimization problems are generally complex and required to be solved in parallel architectures due to theircomputational costs. The main issue about the parallelism is that the parallel architectures may affect the performancebecause the original models are constructed based upon the sequential architectures. Therefore, the parallelizationapproaches should consider the efficiency in addition to reducing computational cost. The objective of this paper is two-fold.First goal is presenting a parallelization approaches and investigating the performance efficiency of the parallel models.Second purpose is implementing the models in parallel programming environments and examining the time efficiency. In thisstudy, two parallel models are developed for Particle Swarm Optimization (PSO) algorithm: fine-grained and coarse-grainedmodels. The models are tested on some benchmark problems. Results demonstrate that the parallel models considered canbe efficiently used for improving both performance and speed-up