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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.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Fernandes, C.; Fachada, N.; Laredo, J.; Merelo, J.; Castillo, P. and Rosa, A. (2018). Revisiting Population Structure and Particle Swarm Performance. In Proceedings of the 10th International Joint Conference on Computational Intelligence (IJCCI 2018) - IJCCI; ISBN 978-989-758-327-8; ISSN 2184-3236, SciTePress, pages 248-254. DOI: 10.5220/0006959502480254

@conference{ijcci18,
author={Carlos M. Fernandes. and Nuno Fachada. and Juan L. J. Laredo. and Juan Julian Merelo. and Pedro A. Castillo. and Agostinho Rosa.},
title={Revisiting Population Structure and Particle Swarm Performance},
booktitle={Proceedings of the 10th International Joint Conference on Computational Intelligence (IJCCI 2018) - IJCCI},
year={2018},
pages={248-254},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006959502480254},
isbn={978-989-758-327-8},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Computational Intelligence (IJCCI 2018) - IJCCI
TI - Revisiting Population Structure and Particle Swarm Performance
SN - 978-989-758-327-8
IS - 2184-3236
AU - Fernandes, C.
AU - Fachada, N.
AU - Laredo, J.
AU - Merelo, J.
AU - Castillo, P.
AU - Rosa, A.
PY - 2018
SP - 248
EP - 254
DO - 10.5220/0006959502480254
PB - SciTePress