On enhancing efficiency and accuracy of particle swarm optimization algorithms

The particle swarm optimization (PSO) algorithm has been recently introduced in the non–linear programming, becoming widely studied and used in a variety of applications. Starting from its original formulation, many variants for improvement and specialization of the PSO have been already proposed, but without any definitive result, thus research in this area is nowadays still rather active. This paper goes in this direction, by proposing some modifications to the basic PSO algorithm, aiming at enhancements in aspects that impact on the efficiency and accuracy of the optimization algorithm. In particular, variants of PSO based on fuzzy logics and Bayesian theory have been developed, which show better, or competitive, performances when compared to both the basic PSO formulation and a few other optimization algorithms taken from the literature.