Transitional particle swarm optimization

A new variation of particle swarm optimization (PSO) termed as transitional PSO (T-PSO) is proposed here. T-PSO attempts to improve PSO via its iteration strategy. Traditionally, PSO adopts either the synchronous or the asynchronous iteration strategy. Both of these iteration strategies have their o...

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Main Authors:	Nor Azlina, Ab. Aziz, Zuwairie, Ibrahim, Marizan, Mubin, Sophan Wahyudi, Nawawi, Nor Hidayati, Abdul Aziz
Format:	Article
Language:	English
Published:	Institute of Advanced Engineering and Science (IAES) 2017
Subjects:	QA76 Computer software TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/27013/ http://umpir.ump.edu.my/id/eprint/27013/ http://umpir.ump.edu.my/id/eprint/27013/ http://umpir.ump.edu.my/id/eprint/27013/1/Transitional%20particle%20swarm%20optimization.pdf

id	ump-27013
recordtype	eprints
spelling	ump-270132020-03-10T10:04:03Z http://umpir.ump.edu.my/id/eprint/27013/ Transitional particle swarm optimization Nor Azlina, Ab. Aziz Zuwairie, Ibrahim Marizan, Mubin Sophan Wahyudi, Nawawi Nor Hidayati, Abdul Aziz QA76 Computer software TK Electrical engineering. Electronics Nuclear engineering A new variation of particle swarm optimization (PSO) termed as transitional PSO (T-PSO) is proposed here. T-PSO attempts to improve PSO via its iteration strategy. Traditionally, PSO adopts either the synchronous or the asynchronous iteration strategy. Both of these iteration strategies have their own strengths and weaknesses. The synchronous strategy has reputation of better exploitation while asynchronous strategy is stronger in exploration. The particles of T-PSO start with asynchronous update to encourage more exploration at the start of the search. If no better solution is found for a number of iteration, the iteration strategy is changed to synchronous update to allow fine tuning by the particles. The results show that T-PSO is ranked better than the traditional PSOs. Institute of Advanced Engineering and Science (IAES) 2017-06 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/27013/1/Transitional%20particle%20swarm%20optimization.pdf Nor Azlina, Ab. Aziz and Zuwairie, Ibrahim and Marizan, Mubin and Sophan Wahyudi, Nawawi and Nor Hidayati, Abdul Aziz (2017) Transitional particle swarm optimization. International Journal of Electrical and Computer Engineering (IJECE), 7 (3). pp. 1611-1619. ISSN 2088-8708 http://doi.org/10.11591/ijece.v7i3.pp1611-1619 http://doi.org/10.11591/ijece.v7i3.pp1611-1619
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	QA76 Computer software TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	QA76 Computer software TK Electrical engineering. Electronics Nuclear engineering Nor Azlina, Ab. Aziz Zuwairie, Ibrahim Marizan, Mubin Sophan Wahyudi, Nawawi Nor Hidayati, Abdul Aziz Transitional particle swarm optimization
description	A new variation of particle swarm optimization (PSO) termed as transitional PSO (T-PSO) is proposed here. T-PSO attempts to improve PSO via its iteration strategy. Traditionally, PSO adopts either the synchronous or the asynchronous iteration strategy. Both of these iteration strategies have their own strengths and weaknesses. The synchronous strategy has reputation of better exploitation while asynchronous strategy is stronger in exploration. The particles of T-PSO start with asynchronous update to encourage more exploration at the start of the search. If no better solution is found for a number of iteration, the iteration strategy is changed to synchronous update to allow fine tuning by the particles. The results show that T-PSO is ranked better than the traditional PSOs.
format	Article
author	Nor Azlina, Ab. Aziz Zuwairie, Ibrahim Marizan, Mubin Sophan Wahyudi, Nawawi Nor Hidayati, Abdul Aziz
author_facet	Nor Azlina, Ab. Aziz Zuwairie, Ibrahim Marizan, Mubin Sophan Wahyudi, Nawawi Nor Hidayati, Abdul Aziz
author_sort	Nor Azlina, Ab. Aziz
title	Transitional particle swarm optimization
title_short	Transitional particle swarm optimization
title_full	Transitional particle swarm optimization
title_fullStr	Transitional particle swarm optimization
title_full_unstemmed	Transitional particle swarm optimization
title_sort	transitional particle swarm optimization
publisher	Institute of Advanced Engineering and Science (IAES)
publishDate	2017
url	http://umpir.ump.edu.my/id/eprint/27013/ http://umpir.ump.edu.my/id/eprint/27013/ http://umpir.ump.edu.my/id/eprint/27013/ http://umpir.ump.edu.my/id/eprint/27013/1/Transitional%20particle%20swarm%20optimization.pdf
first_indexed	2023-09-18T22:42:22Z
last_indexed	2023-09-18T22:42:22Z
_version_	1777417000086142976

Transitional particle swarm optimization

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