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New particle swarm optimizer with sigmoid increasing inertia weight

The inertia weight of particle swarm optimization (PSO) is a mechanism to control the exploration and exploitation abilities of the swarm and as mechanism to eliminate the need for velocity clamping. The present paper proposes a new PSO optimizer with sigmoid increasing inertia weight. Four standard...

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Main Authors: Malik , Reza Firsandaya, Abdul Rahman, Tharek, Mohd. Hashim, Siti Zaiton, Ngah, Razali
Format: Article
Published: 2007
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://eprints.utm.my/id/eprint/17094/
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.228.420&rep=rep1&type=pdf
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spelling my.utm.170942017-10-23T13:15:26Z http://eprints.utm.my/id/eprint/17094/ New particle swarm optimizer with sigmoid increasing inertia weight Malik , Reza Firsandaya Abdul Rahman, Tharek Mohd. Hashim, Siti Zaiton Ngah, Razali TK Electrical engineering. Electronics Nuclear engineering The inertia weight of particle swarm optimization (PSO) is a mechanism to control the exploration and exploitation abilities of the swarm and as mechanism to eliminate the need for velocity clamping. The present paper proposes a new PSO optimizer with sigmoid increasing inertia weight. Four standard non-linear benchmark functions are used to confirm its validity. The comparison has been simulated with sigmoid decreasing and linearly increasing inertia weight. From experiments, it shows that PSO with increasing inertia weight gives better performance with quick convergence capability and aggressive movement narrowing towards the solution region. 2007 Article PeerReviewed Malik , Reza Firsandaya and Abdul Rahman, Tharek and Mohd. Hashim, Siti Zaiton and Ngah, Razali (2007) New particle swarm optimizer with sigmoid increasing inertia weight. International Journal of Computer Science and Security , 1 (6). pp. 35-44. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.228.420&rep=rep1&type=pdf
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Malik , Reza Firsandaya
Abdul Rahman, Tharek
Mohd. Hashim, Siti Zaiton
Ngah, Razali
New particle swarm optimizer with sigmoid increasing inertia weight
description The inertia weight of particle swarm optimization (PSO) is a mechanism to control the exploration and exploitation abilities of the swarm and as mechanism to eliminate the need for velocity clamping. The present paper proposes a new PSO optimizer with sigmoid increasing inertia weight. Four standard non-linear benchmark functions are used to confirm its validity. The comparison has been simulated with sigmoid decreasing and linearly increasing inertia weight. From experiments, it shows that PSO with increasing inertia weight gives better performance with quick convergence capability and aggressive movement narrowing towards the solution region.
format Article
author Malik , Reza Firsandaya
Abdul Rahman, Tharek
Mohd. Hashim, Siti Zaiton
Ngah, Razali
author_facet Malik , Reza Firsandaya
Abdul Rahman, Tharek
Mohd. Hashim, Siti Zaiton
Ngah, Razali
author_sort Malik , Reza Firsandaya
title New particle swarm optimizer with sigmoid increasing inertia weight
title_short New particle swarm optimizer with sigmoid increasing inertia weight
title_full New particle swarm optimizer with sigmoid increasing inertia weight
title_fullStr New particle swarm optimizer with sigmoid increasing inertia weight
title_full_unstemmed New particle swarm optimizer with sigmoid increasing inertia weight
title_sort new particle swarm optimizer with sigmoid increasing inertia weight
publishDate 2007
url http://eprints.utm.my/id/eprint/17094/
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.228.420&rep=rep1&type=pdf
_version_ 1643646727702446080
score 13.211869

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