PMT : opposition based learning technique for enhancing metaheuristic algorithms performance

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

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Main Author:	Hammoudeh, S. Alamri
Format:	Thesis
Language:	English
Published:	2020
Subjects:	QA76 Computer software
Online Access:	http://umpir.ump.edu.my/id/eprint/33711/1/PMT%20%20opposition%20based%20learning%20technique%20for%20enhancing%20metaheuristic.pdf
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id	my-ump-ir.33711
record_format	uketd_dc
spelling	my-ump-ir.337112022-04-13T02:21:07Z PMT : opposition based learning technique for enhancing metaheuristic algorithms performance 2020-10 Hammoudeh, S. Alamri QA76 Computer software Metaheuristic algorithms have shown promising performance in solving sophisticated real-world optimization problems. Nevertheless, many metaheuristic 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. Opposition-based learning (OBL) has shown promising results to address the aforementioned issue. Nonetheless, OBL-based solutions often consider one particular direction of the opposition. Considering only one direction can be problematic as the best solution may come in any of a multitude of directions. Addressing these OBL limitations, this research proposes a new general 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 was applied to four contemporary metaheuristic algorithms, Differential Evolution, Particle Swarm Optimization, Simulated Annealing, and Whale Optimization Algorithm, to solve 15 well-known benchmark functions as well as 2 real world problems based on the welded beam design and pressure vessel design. Experimentally, the PMT shows promising results by accelerating the convergence rate against the original algorithms with the same number of fitness evaluations comparing to the original metaheuristic algorithms in benchmark functions and real-world optimization problems. 2020-10 Thesis http://umpir.ump.edu.my/id/eprint/33711/ http://umpir.ump.edu.my/id/eprint/33711/1/PMT%20%20opposition%20based%20learning%20technique%20for%20enhancing%20metaheuristic.pdf pdf en public phd doctoral Universiti Malaysia Pahang Faculty of Computing
institution	Universiti Malaysia Pahang Al-Sultan Abdullah
collection	UMPSA Institutional Repository
language	English
topic	QA76 Computer software
spellingShingle	QA76 Computer software Hammoudeh, S. Alamri PMT : opposition based learning technique for enhancing metaheuristic algorithms performance
description	Metaheuristic algorithms have shown promising performance in solving sophisticated real-world optimization problems. Nevertheless, many metaheuristic 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. Opposition-based learning (OBL) has shown promising results to address the aforementioned issue. Nonetheless, OBL-based solutions often consider one particular direction of the opposition. Considering only one direction can be problematic as the best solution may come in any of a multitude of directions. Addressing these OBL limitations, this research proposes a new general 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 was applied to four contemporary metaheuristic algorithms, Differential Evolution, Particle Swarm Optimization, Simulated Annealing, and Whale Optimization Algorithm, to solve 15 well-known benchmark functions as well as 2 real world problems based on the welded beam design and pressure vessel design. Experimentally, the PMT shows promising results by accelerating the convergence rate against the original algorithms with the same number of fitness evaluations comparing to the original metaheuristic algorithms in benchmark functions and real-world optimization problems.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Hammoudeh, S. Alamri
author_facet	Hammoudeh, S. Alamri
author_sort	Hammoudeh, S. Alamri
title	PMT : opposition based learning technique for enhancing metaheuristic algorithms performance
title_short	PMT : opposition based learning technique for enhancing metaheuristic algorithms performance
title_full	PMT : opposition based learning technique for enhancing metaheuristic algorithms performance
title_fullStr	PMT : opposition based learning technique for enhancing metaheuristic algorithms performance
title_full_unstemmed	PMT : opposition based learning technique for enhancing metaheuristic algorithms performance
title_sort	pmt : opposition based learning technique for enhancing metaheuristic algorithms performance
granting_institution	Universiti Malaysia Pahang
granting_department	Faculty of Computing
publishDate	2020
url	http://umpir.ump.edu.my/id/eprint/33711/1/PMT%20%20opposition%20based%20learning%20technique%20for%20enhancing%20metaheuristic.pdf
_version_	1783732174784036864

PMT : opposition based learning technique for enhancing metaheuristic algorithms performance

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