Dynamic clan particle swarm optimization

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

doi:10.1109/ISDA.2009.10

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 proceeding › Conference contribution › Academic › peer-review

20 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 language	English
Title of host publication	2009 Ninth International Conference on Intelligent Systems Design and Applications
Publisher	Institute of Electrical and Electronics Engineers
Pages	249-254
Number of pages	6
ISBN (Print)	978-1-4244-4735-0
DOIs	https://doi.org/10.1109/ISDA.2009.10
Publication status	Published - 28 Dec 2009
Externally published	Yes
Event	9th International Conference on Intelligent Systems Design and Applications, ISDA 2009 - Pisa, Italy Duration: 30 Nov 2009 → 2 Dec 2009 Conference number: 9

Conference

Conference	9th International Conference on Intelligent Systems Design and Applications, ISDA 2009
Abbreviated title	ISDA
Country/Territory	Italy
City	Pisa
Period	30/11/09 → 2/12/09

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10.1109/ISDA.2009.10

Cite this

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title = "Dynamic clan particle swarm optimization",

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.",

author = "C.J.A. Bastos-Filho and D.F. Carvalho and E.M.N. Figueiredo and {de Miranda}, P.B.C.",

year = "2009",

month = dec,

day = "28",

doi = "10.1109/ISDA.2009.10",

language = "English",

isbn = "978-1-4244-4735-0",

pages = "249--254",

booktitle = "2009 Ninth International Conference on Intelligent Systems Design and Applications",

publisher = "Institute of Electrical and Electronics Engineers",

address = "United States",

note = "9th International Conference on Intelligent Systems Design and Applications, ISDA 2009, ISDA ; Conference date: 30-11-2009 Through 02-12-2009",

}

Bastos-Filho, CJA, Carvalho, DF, Figueiredo, EMN & de Miranda, PBC 2009, Dynamic clan particle swarm optimization. in 2009 Ninth International Conference on Intelligent Systems Design and Applications., 5364803, Institute of Electrical and Electronics Engineers, pp. 249-254, 9th International Conference on Intelligent Systems Design and Applications, ISDA 2009, Pisa, Italy, 30/11/09. https://doi.org/10.1109/ISDA.2009.10

Dynamic clan particle swarm optimization. / Bastos-Filho, C.J.A.; Carvalho, D.F.; Figueiredo, E.M.N. et al.
2009 Ninth International Conference on Intelligent Systems Design and Applications. Institute of Electrical and Electronics Engineers, 2009. p. 249-254 5364803.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Dynamic clan particle swarm optimization

AU - Bastos-Filho, C.J.A.

AU - Carvalho, D.F.

AU - Figueiredo, E.M.N.

AU - de Miranda, P.B.C.

N1 - Conference code: 9

PY - 2009/12/28

Y1 - 2009/12/28

N2 - 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.

AB - 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.

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M3 - Conference contribution

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SN - 978-1-4244-4735-0

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EP - 254

BT - 2009 Ninth International Conference on Intelligent Systems Design and Applications

PB - Institute of Electrical and Electronics Engineers

T2 - 9th International Conference on Intelligent Systems Design and Applications, ISDA 2009

Y2 - 30 November 2009 through 2 December 2009

ER -

Dynamic clan particle swarm optimization

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