A comparative study of three new parallel models based on the PSO algorithm

Abstract

Meta-heuristic PSO has limits, such as premature convergence and high running time, especially for complex optimization problems. In this paper, a description of three parallel models based on the PSO algorithm is developed, on the basis of combining two concepts: parallelism and neighborhood, which are designed according to three different approaches in order to avoid the two disadvantages of the PSO algorithm. The third model, SPM (Spherical-neighborhood Parallel Model), is designed to improve the obtained results from the two parallel NPM (Neighborhood Parallel Model) and MPM (Multi-PSO Parallel Model) models. The experimental results presented in this paper show that SPM model performed much better than both NPM and MPM models in terms of computing time and solution quality.

Keywords

• optimization
• metaheuristic
• pso
• sa
• parallelization
• computing time