Revisiting population structure and particle swarm performance

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dc.contributor.authorFernandes, Carlos M.
dc.contributor.authorFachada, Nuno
dc.contributor.authorLaredo, Juan L.J.
dc.contributor.authorMerelo, Juan Julian
dc.contributor.authorCastillo, Pedro A.
dc.contributor.authorRosa, Agostinho
dc.contributor.editorSabourin, Christophe
dc.contributor.editorMerelo, Juan Julian
dc.contributor.editorBarranco, Alejandro Linares
dc.contributor.editorMadani, Kurosh
dc.contributor.editorWarwick, Kevin
dc.contributor.institutionHEI-LAB (FCT) - Digital Laboratories for Environments and Human Interactions
dc.date.issued2018
dc.descriptionPublisher Copyright: © 2018 by SCITEPRESS–Science and Technology Publications, Lda. All rights reserved.
dc.description.abstractPopulation 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.en
dc.identifier.citationFernandes, C M, Fachada, N, Laredo, J L J, Merelo, J J, Castillo, P A & Rosa, A 2018, Revisiting population structure and particle swarm performance. in C Sabourin, J J Merelo, A L Barranco, K Madani & K Warwick (eds), IJCCI 2018 - Proceedings of the 10th International Joint Conference on Computational Intelligence. International Joint Conference on Computational Intelligence, vol. 1, SciTePress, pp. 248-254, 10th International Joint Conference on Computational Intelligence, IJCCI 2018, Seville, Spain, 18/09/18. https://doi.org/10.5220/0006959502480254
dc.identifier.doihttps://doi.org/10.5220/0006959502480254
dc.identifier.isbn9789897583278
dc.identifier.isbn9789897583278
dc.identifier.issn2184-3236
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85190465001&partnerID=8YFLogxK
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSciTePress
dc.publisher10th International Joint Conference on Computational Intelligence, IJCCI 2018
dc.relation.ispartofIJCCI 2018 - Proceedings of the 10th International Joint Conference on Computational Intelligence
dc.relation.ispartofseriesInternational Joint Conference on Computational Intelligence
dc.rightsopenAccess
dc.subjectPOPULAÇÃO
dc.subjectGRÁFICOS
dc.subjectGRAPHICS
dc.subjectPOPULATION
dc.subjectOTIMIZAÇÃO POR ENXAME DE PARTÍCULAS
dc.subjectPARTICLE SWARM OPTIMIZATION
dc.titleRevisiting population structure and particle swarm performanceen
dc.typeconferenceObject

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