Trained particle swarm optimization for ad-hoc collaborative computing networks

Shahin Gheitanchi; Falah Ali; Elias Stipidis

Trained particle swarm optimization for ad-hoc collaborative computing networks

Shahin Gheitanchi, Falah Ali, Elias Stipidis

Research output: Chapter in Book/Conference proceeding with ISSN or ISBN › Conference contribution with ISSN or ISBN › peer-review

Abstract

Distributed processing is an essential part of collaborative computing techniques over ad-hoc networks. In this paper, a generalized particle swarm optimization (PSO) model for communication networks is introduced. A modified version of PSO, called trained PSO (TPSO), consisting of distributed particles that are adapted to reduce traffic and computational overhead of the optimization process is proposed. The TPSO technique is used to find the node with the highest processing load in an ad-hoc collaborative computing system. The simulation results show that the TPSO algorithm significantly reduces the traffic overhead, computation complexity and convergence time of particles, in comparison to the PSO.

Original language	English
Title of host publication	AISB 2008 Convention
Subtitle of host publication	Communication, Interaction and Social Intelligence - Proceedings of the AISB 2008 Symposium on Swarm Intelligence Algorithms and Applications
Pages	7-11
Number of pages	5
Publication status	Published - 1 Dec 2008
Event	AISB 2008 Symposium on Swarm Intelligence Algorithms and Applications - Aberdeen, United Kingdom Duration: 1 Apr 2008 → 4 Apr 2008

Conference

Conference	AISB 2008 Symposium on Swarm Intelligence Algorithms and Applications
Country/Territory	United Kingdom
City	Aberdeen
Period	1/04/08 → 4/04/08

Cite this

@inproceedings{e8f4584257c444708a623d53dba1a4ab,

title = "Trained particle swarm optimization for ad-hoc collaborative computing networks",

abstract = "Distributed processing is an essential part of collaborative computing techniques over ad-hoc networks. In this paper, a generalized particle swarm optimization (PSO) model for communication networks is introduced. A modified version of PSO, called trained PSO (TPSO), consisting of distributed particles that are adapted to reduce traffic and computational overhead of the optimization process is proposed. The TPSO technique is used to find the node with the highest processing load in an ad-hoc collaborative computing system. The simulation results show that the TPSO algorithm significantly reduces the traffic overhead, computation complexity and convergence time of particles, in comparison to the PSO.",

author = "Shahin Gheitanchi and Falah Ali and Elias Stipidis",

year = "2008",

month = dec,

day = "1",

language = "English",

isbn = "1902956702",

pages = "7--11",

booktitle = "AISB 2008 Convention",

note = "AISB 2008 Symposium on Swarm Intelligence Algorithms and Applications ; Conference date: 01-04-2008 Through 04-04-2008",

}

Gheitanchi, S, Ali, F & Stipidis, E 2008, Trained particle swarm optimization for ad-hoc collaborative computing networks. in AISB 2008 Convention: Communication, Interaction and Social Intelligence - Proceedings of the AISB 2008 Symposium on Swarm Intelligence Algorithms and Applications. pp. 7-11, AISB 2008 Symposium on Swarm Intelligence Algorithms and Applications, Aberdeen, United Kingdom, 1/04/08.

Trained particle swarm optimization for ad-hoc collaborative computing networks. / Gheitanchi, Shahin; Ali, Falah; Stipidis, Elias.
AISB 2008 Convention: Communication, Interaction and Social Intelligence - Proceedings of the AISB 2008 Symposium on Swarm Intelligence Algorithms and Applications. 2008. p. 7-11.

Research output: Chapter in Book/Conference proceeding with ISSN or ISBN › Conference contribution with ISSN or ISBN › peer-review

TY - GEN

T1 - Trained particle swarm optimization for ad-hoc collaborative computing networks

AU - Gheitanchi, Shahin

AU - Ali, Falah

AU - Stipidis, Elias

PY - 2008/12/1

Y1 - 2008/12/1

N2 - Distributed processing is an essential part of collaborative computing techniques over ad-hoc networks. In this paper, a generalized particle swarm optimization (PSO) model for communication networks is introduced. A modified version of PSO, called trained PSO (TPSO), consisting of distributed particles that are adapted to reduce traffic and computational overhead of the optimization process is proposed. The TPSO technique is used to find the node with the highest processing load in an ad-hoc collaborative computing system. The simulation results show that the TPSO algorithm significantly reduces the traffic overhead, computation complexity and convergence time of particles, in comparison to the PSO.

AB - Distributed processing is an essential part of collaborative computing techniques over ad-hoc networks. In this paper, a generalized particle swarm optimization (PSO) model for communication networks is introduced. A modified version of PSO, called trained PSO (TPSO), consisting of distributed particles that are adapted to reduce traffic and computational overhead of the optimization process is proposed. The TPSO technique is used to find the node with the highest processing load in an ad-hoc collaborative computing system. The simulation results show that the TPSO algorithm significantly reduces the traffic overhead, computation complexity and convergence time of particles, in comparison to the PSO.

UR - http://www.scopus.com/inward/record.url?scp=78249258215&partnerID=8YFLogxK

M3 - Conference contribution with ISSN or ISBN

AN - SCOPUS:78249258215

SN - 1902956702

SN - 9781902956701

SP - 7

EP - 11

BT - AISB 2008 Convention

T2 - AISB 2008 Symposium on Swarm Intelligence Algorithms and Applications

Y2 - 1 April 2008 through 4 April 2008

ER -

Trained particle swarm optimization for ad-hoc collaborative computing networks

Abstract

Conference

Other files and links

Fingerprint

Cite this