Fernandes, Carlos M.Fachada, NunoLaredo, Juan L. J.Merelo, J. J.Castillo, P. A.Rosa, Agostinho C.2020-05-272020-05-272018Fernandes, C. M., Fachada, N., Laredo, J. L. J., Merelo, J. J., Castillo, P. A. & Rosa, A. C. (2018). Revisiting Population Structure and Particle Swarm Performance. In Proceedings of the 10th International Joint Conference on Computational Intelligence, IJCCI 2018 (pp. 248-254), Seville, Spain978-989-758-327-8https://doi.org/10.5220/0006959502480254http://hdl.handle.net/10437/10190Proceedings of the 10th International Joint Conference on Computational Intelligence - Volume 1Population structure strongly affects the dynamic behavior and performance of the particle swarm optimization (PSO) algorithm. Most of PSOs use one of two simple sociometric principles for defining the structure. One connects all the members of the swarm to one another. This strategy is often called gbest and results in a connectivity degree k = n, where n is the population size. The other connects the population in a ring with k = 3. Between these upper and lower bounds there are a vast number of strategies that can be explored for enhancing the performance and adaptability of the algorithm. This paper investigates the convergence speed, accuracy, robustness and scalability of PSOs structured by regular and random graphs with 3≤k≤n. The main conclusion is that regular and random graphs with the same averaged connectivity k may result in significantly different performance, namely when k is low.application/pdfengopenAccessPARTICLE SWARM OPTIMIZATIONPOPULATIONGRÁFICOSGRAPHICSPOPULAÇÃOOTIMIZAÇÃO POR ENXAME DE PARTÍCULASconferenceObject