Improving particle swarm optimization via adaptive switching asynchronous – synchronous update

Particle swarm optimization (PSO) is a population-based metaheuristic optimization algorithm that solves a problem through iterative operations. Traditional PSO iteration strategies can be categorized into two groups: synchronous (S-PSO) or asynchronous (A-PSO) update. In S-PSO, the performance of t...

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Main Authors:	Nor Azlina, Ab. Aziz, Zuwairie, Ibrahim, Marizan, Mubin, Sophan Wahyudi, Nawawi, Mohd Saberi, Mohamad
Format:	Article
Language:	English English
Published:	Elsevier Ltd 2018
Subjects:	TS Manufactures
Online Access:	http://umpir.ump.edu.my/id/eprint/22298/ http://umpir.ump.edu.my/id/eprint/22298/ http://umpir.ump.edu.my/id/eprint/22298/ http://umpir.ump.edu.my/id/eprint/22298/1/Improving%20particle%20swarm%20optimization%20via%20adaptive%20switching%20asynchronous.pdf http://umpir.ump.edu.my/id/eprint/22298/7/Improving%20particle%20swarm%20optimization%20via%20adaptive%20switching%20asynchronous%20%E2%80%93%20synchronous%20update.pdf

id	ump-22298
recordtype	eprints
spelling	ump-222982018-11-15T03:13:07Z http://umpir.ump.edu.my/id/eprint/22298/ Improving particle swarm optimization via adaptive switching asynchronous – synchronous update Nor Azlina, Ab. Aziz Zuwairie, Ibrahim Marizan, Mubin Sophan Wahyudi, Nawawi Mohd Saberi, Mohamad TS Manufactures Particle swarm optimization (PSO) is a population-based metaheuristic optimization algorithm that solves a problem through iterative operations. Traditional PSO iteration strategies can be categorized into two groups: synchronous (S-PSO) or asynchronous (A-PSO) update. In S-PSO, the performance of the entire swarm is evaluated before the particles’ velocities and positions are updated, whereas in A-PSO, each particle's velocity and position are updated immediately after an individual's performance is evaluated. Previous research claimed that S-PSO is better in exploitation and has fast convergence, whereas A-PSO converges at a slower rate and is stronger at exploration. Exploration and exploitation are important in ensuring good performance for any population-based metaheuristic. In this paper, an adaptive switching PSO (Switch-PSO) algorithm that uses a hybrid update sequence is proposed. The iteration strategy in Switch-PSO is adaptively switched between the two traditional iteration strategies according to the performance of the swarm's best member. The performance of Switch-PSO is compared with existing S-PSO, A-PSO and three state-of-the-art PSO algorithms using CEC2014's benchmark functions. The results show that Switch-PSO achieves superior performance in comparison to the other algorithms. Switch-PSO is then applied for infinite impulse response model identification, where Switch-PSO is found to rank the best among all the algorithms applied. Elsevier Ltd 2018 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/22298/1/Improving%20particle%20swarm%20optimization%20via%20adaptive%20switching%20asynchronous.pdf pdf en http://umpir.ump.edu.my/id/eprint/22298/7/Improving%20particle%20swarm%20optimization%20via%20adaptive%20switching%20asynchronous%20%E2%80%93%20synchronous%20update.pdf Nor Azlina, Ab. Aziz and Zuwairie, Ibrahim and Marizan, Mubin and Sophan Wahyudi, Nawawi and Mohd Saberi, Mohamad (2018) Improving particle swarm optimization via adaptive switching asynchronous – synchronous update. Applied Soft Computing, 72. pp. 298-311. ISSN 1568-4946 https://doi.org/10.1016/j.asoc.2018.07.047 10.1016/j.asoc.2018.07.047
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English English
topic	TS Manufactures
spellingShingle	TS Manufactures Nor Azlina, Ab. Aziz Zuwairie, Ibrahim Marizan, Mubin Sophan Wahyudi, Nawawi Mohd Saberi, Mohamad Improving particle swarm optimization via adaptive switching asynchronous – synchronous update
description	Particle swarm optimization (PSO) is a population-based metaheuristic optimization algorithm that solves a problem through iterative operations. Traditional PSO iteration strategies can be categorized into two groups: synchronous (S-PSO) or asynchronous (A-PSO) update. In S-PSO, the performance of the entire swarm is evaluated before the particles’ velocities and positions are updated, whereas in A-PSO, each particle's velocity and position are updated immediately after an individual's performance is evaluated. Previous research claimed that S-PSO is better in exploitation and has fast convergence, whereas A-PSO converges at a slower rate and is stronger at exploration. Exploration and exploitation are important in ensuring good performance for any population-based metaheuristic. In this paper, an adaptive switching PSO (Switch-PSO) algorithm that uses a hybrid update sequence is proposed. The iteration strategy in Switch-PSO is adaptively switched between the two traditional iteration strategies according to the performance of the swarm's best member. The performance of Switch-PSO is compared with existing S-PSO, A-PSO and three state-of-the-art PSO algorithms using CEC2014's benchmark functions. The results show that Switch-PSO achieves superior performance in comparison to the other algorithms. Switch-PSO is then applied for infinite impulse response model identification, where Switch-PSO is found to rank the best among all the algorithms applied.
format	Article
author	Nor Azlina, Ab. Aziz Zuwairie, Ibrahim Marizan, Mubin Sophan Wahyudi, Nawawi Mohd Saberi, Mohamad
author_facet	Nor Azlina, Ab. Aziz Zuwairie, Ibrahim Marizan, Mubin Sophan Wahyudi, Nawawi Mohd Saberi, Mohamad
author_sort	Nor Azlina, Ab. Aziz
title	Improving particle swarm optimization via adaptive switching asynchronous – synchronous update
title_short	Improving particle swarm optimization via adaptive switching asynchronous – synchronous update
title_full	Improving particle swarm optimization via adaptive switching asynchronous – synchronous update
title_fullStr	Improving particle swarm optimization via adaptive switching asynchronous – synchronous update
title_full_unstemmed	Improving particle swarm optimization via adaptive switching asynchronous – synchronous update
title_sort	improving particle swarm optimization via adaptive switching asynchronous – synchronous update
publisher	Elsevier Ltd
publishDate	2018
url	http://umpir.ump.edu.my/id/eprint/22298/ http://umpir.ump.edu.my/id/eprint/22298/ http://umpir.ump.edu.my/id/eprint/22298/ http://umpir.ump.edu.my/id/eprint/22298/1/Improving%20particle%20swarm%20optimization%20via%20adaptive%20switching%20asynchronous.pdf http://umpir.ump.edu.my/id/eprint/22298/7/Improving%20particle%20swarm%20optimization%20via%20adaptive%20switching%20asynchronous%20%E2%80%93%20synchronous%20update.pdf
first_indexed	2023-09-18T22:33:07Z
last_indexed	2023-09-18T22:33:07Z
_version_	1777416417970225152

Improving particle swarm optimization via adaptive switching asynchronous – synchronous update

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