The abstract of the article Particle swarm optimization algorithm: an overview, which was written by Dongshu Wang,Dapei Tan, and Lei Liu reads as follows:
“Particle swarm optimization (PSO) is a population-based stochastic optimization algorithm motivated by intelligent collective behavior of some animals such as flocks of birds or schools of fish. Since presented in 1995, it has experienced a multitude of enhancements. As researchers have learned about the technique, they derived new versions aiming to different demands, developed new applications in a host of areas, published theoretical studies of the effects of the various parameters and proposed many variants of the algorithm. This paper introduces its origin and background and carries out the theory analysis of the PSO. Then, we analyze its present situation of research and application in algorithm structure, parameter selection, topology structure, discrete PSO algorithm and parallel PSO algorithm, multi-objective optimization PSO and its engineering applications. Finally, the existing problems are analyzed and future research directions are presented.”
The authors mention that “parameters of PSO algorithm could be adjusted by the methods such as sensitivity analysis (Bartz-Beielstein et al. 2002), regression trees (Bartz-Beielstein et al. 2004a) and calculate statistics (Bartz-Beielstein et al. 2004b), to promote the performance of PSO algorithm for solving the practical problems.”
Here are the references:
Bartz-Beielstein T, Parsopoulos KE, Vrahatis MN (2002) Tuning PSO parameters through sensitivity analysis. Technical Report CI 124/02, SFB 531. University of Dortmund, Dortmund, Germany, Department of Computer Science
Bartz-Beielstein T, Parsopoulos KE, Vegt MD, Vrahatis MN (2004a) Designing particle swarm optimization with regression trees. Technical Report CI 173/04, SFB 531. University of Dortmund, Dortmund, Germany, Department of Computer Science
Bartz-Beielstein T, Parsopoulos KE, Vrahatis MN (2004b) Analysis of particle swarm optimization using computational statistics. In: Proceedings of the international conference of numerical analysis and applied mathematics (ICNAAM 2004), Chalkis, Greece, pp 34–37
The online version of this article can be accessed here: doi:10.1007/s00500-016-2474-6