Authors:
Carlos M. Fernandes
1
;
Nuno Fachada
2
;
Juan L. J. Laredo
3
;
Juan Julian Merelo
4
;
Pedro A. Castillo
4
and
Agostinho Rosa
1
Affiliations:
1
LARSyS: Laboratory for Robotics and Systems in Engineering and Science, University of Lisbon, Lisbon and Portugal
;
2
LARSyS: Laboratory for Robotics and Systems in Engineering and Science, University of Lisbon, Lisbon, Portugal, HEI-LAB - Digital Human-Environment and Interactions Labs, Universidade Lusófona, Lisbon and Portugal
;
3
LITIS, University of Le Havre, Le Havre and France
;
4
Departamento de Arquitectura y Tecnología de Computadores, University of Granada, Granada and Spain
Keyword(s):
Particle Swarm Optimization, Population Structure, Regular Graphs, Random Graphs.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Soft Computing
;
Swarm/Collective Intelligence
Abstract:
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.