Effects of Communication Range, Noise and Help Request Signal on Particle Swarm Optimization with Area Extension (AEPSO)

Particle Swarm Optimization (PSO) method is an Evolutionary algorithm, which outperformed other evolutionary algorithms, such as; GA. PSO method is inspired by animal's group work and social behaviors. Particle Swarm Optimization with Area Extension (AEPSO) was introduced to solve the weaknesse...

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Main Authors: Atyabi, Adham, Phon-Amnuaisuk, Somnuk, Ho, Chin Kuan
Format: Conference or Workshop Item
Published: 2007
Subjects:
Online Access:http://shdl.mmu.edu.my/3176/
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author Atyabi, Adham
Phon-Amnuaisuk, Somnuk
Ho, Chin Kuan
author_facet Atyabi, Adham
Phon-Amnuaisuk, Somnuk
Ho, Chin Kuan
author_sort Atyabi, Adham
building MMU Institutional Repository
collection Online Access
description Particle Swarm Optimization (PSO) method is an Evolutionary algorithm, which outperformed other evolutionary algorithms, such as; GA. PSO method is inspired by animal's group work and social behaviors. Particle Swarm Optimization with Area Extension (AEPSO) was introduced to solve the weaknesses of Basic PSO in static, dynamic optimization tasks (i.e. a group of robots disarm a set of time bomb placed at random in environment). This paper, investigated the effectiveness of AEPSO in a Real-Time problem with a noisy environment. We also explored the effectiveness of different communication ranges and help request on AEPSO.
first_indexed 2025-11-14T18:09:42Z
format Conference or Workshop Item
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institution Multimedia University
institution_category Local University
last_indexed 2025-11-14T18:09:42Z
publishDate 2007
recordtype eprints
repository_type Digital Repository
spelling mmu-31762011-10-07T06:33:38Z http://shdl.mmu.edu.my/3176/ Effects of Communication Range, Noise and Help Request Signal on Particle Swarm Optimization with Area Extension (AEPSO) Atyabi, Adham Phon-Amnuaisuk, Somnuk Ho, Chin Kuan T Technology (General) QA75.5-76.95 Electronic computers. Computer science Particle Swarm Optimization (PSO) method is an Evolutionary algorithm, which outperformed other evolutionary algorithms, such as; GA. PSO method is inspired by animal's group work and social behaviors. Particle Swarm Optimization with Area Extension (AEPSO) was introduced to solve the weaknesses of Basic PSO in static, dynamic optimization tasks (i.e. a group of robots disarm a set of time bomb placed at random in environment). This paper, investigated the effectiveness of AEPSO in a Real-Time problem with a noisy environment. We also explored the effectiveness of different communication ranges and help request on AEPSO. 2007-11 Conference or Workshop Item NonPeerReviewed Atyabi, Adham and Phon-Amnuaisuk, Somnuk and Ho, Chin Kuan (2007) Effects of Communication Range, Noise and Help Request Signal on Particle Swarm Optimization with Area Extension (AEPSO). In: IEEE/WIC/ACM International Conference on Intelligent Agent Technology and Web Intelligence, 02-05 NOV 2007 , Fremont, CA. http://dx.doi.org/10.1109/IAT.2007.92 doi:10.1109/IAT.2007.92 doi:10.1109/IAT.2007.92
spellingShingle T Technology (General)
QA75.5-76.95 Electronic computers. Computer science
Atyabi, Adham
Phon-Amnuaisuk, Somnuk
Ho, Chin Kuan
Effects of Communication Range, Noise and Help Request Signal on Particle Swarm Optimization with Area Extension (AEPSO)
title Effects of Communication Range, Noise and Help Request Signal on Particle Swarm Optimization with Area Extension (AEPSO)
title_full Effects of Communication Range, Noise and Help Request Signal on Particle Swarm Optimization with Area Extension (AEPSO)
title_fullStr Effects of Communication Range, Noise and Help Request Signal on Particle Swarm Optimization with Area Extension (AEPSO)
title_full_unstemmed Effects of Communication Range, Noise and Help Request Signal on Particle Swarm Optimization with Area Extension (AEPSO)
title_short Effects of Communication Range, Noise and Help Request Signal on Particle Swarm Optimization with Area Extension (AEPSO)
title_sort effects of communication range, noise and help request signal on particle swarm optimization with area extension (aepso)
topic T Technology (General)
QA75.5-76.95 Electronic computers. Computer science
url http://shdl.mmu.edu.my/3176/
http://shdl.mmu.edu.my/3176/
http://shdl.mmu.edu.my/3176/