Revisiting population structure and particle swarm performance

Carlos M. Fernandes, Nuno Fachada, Juan L.J. Laredo, Juan Julian Merelo, Pedro A. Castillo, Agostinho Rosa

Resultado de pesquisarevisão de pares

2 Citações (Scopus)
2 Transferências (Pure)

Resumo

Population 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.
Idioma originalInglês
Título da publicação do anfitriãoIJCCI 2018 - Proceedings of the 10th International Joint Conference on Computational Intelligence
EditoresChristophe Sabourin, Juan Julian Merelo, Alejandro Linares Barranco, Kurosh Madani, Kevin Warwick
EditoraSciTePress
Páginas248-254
Número de páginas7
ISBN (eletrónico)9789897583278
ISBN (impresso)9789897583278
DOIs
Estado da publicaçãoPublicadas - 2018
Evento10th International Joint Conference on Computational Intelligence, IJCCI 2018 - Seville
Duração: 18 set. 201820 set. 2018

Série de publicação

NomeInternational Joint Conference on Computational Intelligence
Volume1
ISSN (eletrónico)2184-3236

Conferência

Conferência10th International Joint Conference on Computational Intelligence, IJCCI 2018
País/TerritórioSpain
CidadeSeville
Período18/09/1820/09/18

Keywords

  • POPULAÇÃO
  • GRÁFICOS
  • GRAPHICS
  • POPULATION
  • OTIMIZAÇÃO POR ENXAME DE PARTÍCULAS

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