Dynamic clan particle swarm optimization

C. J.A. Bastos-Filho, D. F. Carvalho, E. M.N. Figueiredo, P. B.C. De Miranda

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

18 Citations (Scopus)

Abstract

Particle Swarm Optimization (PSO) has been widely used to solve many di.erent real world optimization problems. Many novel PSO approaches have been proposed to improve the PSO performance. Recently, a communication topology based on Clans was proposed. In this paper, we propose the Dynamic Clan PSO topology. In this approach, a novel ability is included in the Clan Topology, named migration process. The goal is to improve the PSO degree of convergence focusing on the distribution of the particles in the search space. A comparison with the Original Clan topology and other well known topologies was performed and our results in five benchmark functions have shown that the changes can provide better results, except for the Rastrigin function.

Original languageEnglish
Title of host publicationISDA 2009 - 9th International Conference on Intelligent Systems Design and Applications
Pages249-254
Number of pages6
DOIs
Publication statusPublished - 1 Dec 2009
Externally publishedYes
Event9th International Conference on Intelligent Systems Design and Applications, ISDA 2009 - Pisa, Italy
Duration: 30 Nov 20092 Dec 2009

Publication series

NameISDA 2009 - 9th International Conference on Intelligent Systems Design and Applications

Conference

Conference9th International Conference on Intelligent Systems Design and Applications, ISDA 2009
CountryItaly
CityPisa
Period30/11/092/12/09

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  • Cite this

    Bastos-Filho, C. J. A., Carvalho, D. F., Figueiredo, E. M. N., & De Miranda, P. B. C. (2009). Dynamic clan particle swarm optimization. In ISDA 2009 - 9th International Conference on Intelligent Systems Design and Applications (pp. 249-254). [5364803] (ISDA 2009 - 9th International Conference on Intelligent Systems Design and Applications). https://doi.org/10.1109/ISDA.2009.10