PMT: opposition-based learning technique for enhancing meta-heuristic performance

Meta-heuristic algorithms have shown promising performance in solving sophisticated real-world optimization problems. Nevertheless, many meta-heuristic algorithms are still suffering from a low convergence rate because of the poor balance between exploration (i.e., roaming new potential search areas...

Full description

Bibliographic Details
Main Authors:	Alamri, Hammoudeh S., Kamal Z., Zamli
Format:	Article
Language:	English
Published:	IEEE 2019
Subjects:	QA Mathematics TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/25717/ http://umpir.ump.edu.my/id/eprint/25717/ http://umpir.ump.edu.my/id/eprint/25717/ http://umpir.ump.edu.my/id/eprint/25717/1/PMT_%20opposition-based%20learning%20technique%20for%20enhancing.pdf

id	ump-25717
recordtype	eprints
spelling	ump-257172019-08-29T07:47:26Z http://umpir.ump.edu.my/id/eprint/25717/ PMT: opposition-based learning technique for enhancing meta-heuristic performance Alamri, Hammoudeh S. Kamal Z., Zamli QA Mathematics TK Electrical engineering. Electronics Nuclear engineering Meta-heuristic algorithms have shown promising performance in solving sophisticated real-world optimization problems. Nevertheless, many meta-heuristic algorithms are still suffering from a low convergence rate because of the poor balance between exploration (i.e., roaming new potential search areas) and exploitation (i.e., exploiting the existing neighbors). In some complex problems, the convergence rate can still be poor owing to becoming trapped in local optima. Addressing these issues, this research proposes a new general opposition-based learning (OBL) technique inspired by a natural phenomenon of parallel mirrors systems called the parallel mirrors technique (PMT). Like existing OBL-based approaches, the PMT generates new potential solutions based on the currently selected candidate. Unlike existing OBL-based techniques, the PMT generates more than one candidate in multiple solution-space directions. To evaluate the PMT's performance and adaptability, the PMT has been applied to four contemporary meta-heuristic algorithms, differential evolution (DE), particle swarm optimization (PSO), simulated annealing (SA), and whale optimization algorithm (WOA), to solve 15 well-known benchmark functions. The experimentally, the PMT shows promising results by accelerating the convergence rate against the original algorithms with the same number of fitness evaluations. IEEE 2019-06-26 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/25717/1/PMT_%20opposition-based%20learning%20technique%20for%20enhancing.pdf Alamri, Hammoudeh S. and Kamal Z., Zamli (2019) PMT: opposition-based learning technique for enhancing meta-heuristic performance. IEEE Access, 7 (8746627). pp. 97653-97672. ISSN 2169-3536 https://doi.org/10.1109/ACCESS.2019.2925088 https://doi.org/10.1109/ACCESS.2019.2925088
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	QA Mathematics TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	QA Mathematics TK Electrical engineering. Electronics Nuclear engineering Alamri, Hammoudeh S. Kamal Z., Zamli PMT: opposition-based learning technique for enhancing meta-heuristic performance
description	Meta-heuristic algorithms have shown promising performance in solving sophisticated real-world optimization problems. Nevertheless, many meta-heuristic algorithms are still suffering from a low convergence rate because of the poor balance between exploration (i.e., roaming new potential search areas) and exploitation (i.e., exploiting the existing neighbors). In some complex problems, the convergence rate can still be poor owing to becoming trapped in local optima. Addressing these issues, this research proposes a new general opposition-based learning (OBL) technique inspired by a natural phenomenon of parallel mirrors systems called the parallel mirrors technique (PMT). Like existing OBL-based approaches, the PMT generates new potential solutions based on the currently selected candidate. Unlike existing OBL-based techniques, the PMT generates more than one candidate in multiple solution-space directions. To evaluate the PMT's performance and adaptability, the PMT has been applied to four contemporary meta-heuristic algorithms, differential evolution (DE), particle swarm optimization (PSO), simulated annealing (SA), and whale optimization algorithm (WOA), to solve 15 well-known benchmark functions. The experimentally, the PMT shows promising results by accelerating the convergence rate against the original algorithms with the same number of fitness evaluations.
format	Article
author	Alamri, Hammoudeh S. Kamal Z., Zamli
author_facet	Alamri, Hammoudeh S. Kamal Z., Zamli
author_sort	Alamri, Hammoudeh S.
title	PMT: opposition-based learning technique for enhancing meta-heuristic performance
title_short	PMT: opposition-based learning technique for enhancing meta-heuristic performance
title_full	PMT: opposition-based learning technique for enhancing meta-heuristic performance
title_fullStr	PMT: opposition-based learning technique for enhancing meta-heuristic performance
title_full_unstemmed	PMT: opposition-based learning technique for enhancing meta-heuristic performance
title_sort	pmt: opposition-based learning technique for enhancing meta-heuristic performance
publisher	IEEE
publishDate	2019
url	http://umpir.ump.edu.my/id/eprint/25717/ http://umpir.ump.edu.my/id/eprint/25717/ http://umpir.ump.edu.my/id/eprint/25717/ http://umpir.ump.edu.my/id/eprint/25717/1/PMT_%20opposition-based%20learning%20technique%20for%20enhancing.pdf
first_indexed	2023-09-18T22:39:39Z
last_indexed	2023-09-18T22:39:39Z
_version_	1777416829372727296

PMT: opposition-based learning technique for enhancing meta-heuristic performance

Similar Items