Modified Opposition Based Learning to Improve Harmony Search Variants Exploration

Harmony Search Algorithm (HS) is a well-known optimization algorithm with strong and robust exploitation process. HS such as many optimization algorithms suffers from a weak exploration and susceptible to fall in local optima. Owing to its weaknesses, many variants of HS were introduced in the last...

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Main Authors:	Al-Omoush, Alaa A., Alsewari, Abdulrahman A., Alamri, Hammoudeh S., Kamal Z., Zamli
Format:	Conference or Workshop Item
Language:	English
Published:	Springer 2020
Subjects:	QA76 Computer software
Online Access:	http://umpir.ump.edu.my/id/eprint/26391/ http://umpir.ump.edu.my/id/eprint/26391/ http://umpir.ump.edu.my/id/eprint/26391/1/Camera%20Ready%20Paper..pdf

id	ump-26391
recordtype	eprints
spelling	ump-263912020-02-24T02:37:28Z http://umpir.ump.edu.my/id/eprint/26391/ Modified Opposition Based Learning to Improve Harmony Search Variants Exploration Al-Omoush, Alaa A. Alsewari, Abdulrahman A. Alamri, Hammoudeh S. Kamal Z., Zamli QA76 Computer software Harmony Search Algorithm (HS) is a well-known optimization algorithm with strong and robust exploitation process. HS such as many optimization algorithms suffers from a weak exploration and susceptible to fall in local optima. Owing to its weaknesses, many variants of HS were introduced in the last decade to improve its performance. The Opposition-based learning and its variants have been successfully employed to improve many optimization algorithms, including HS. Opposition-based learning variants enhanced the explorations and help optimization algorithms to avoid local optima falling. Thus, inspired by a new opposition-based learning variant named modified opposition-based learning (MOBL), this research employed the MOBL to improve five well-known variants of HS. The new improved variants are evaluated using nine classical benchmark function and compared with the original variants to evaluate the effectiveness of the proposed technique. The results show that MOBL improved the HS variants in term of exploration and convergence rate. Springer 2020-11-02 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/26391/1/Camera%20Ready%20Paper..pdf Al-Omoush, Alaa A. and Alsewari, Abdulrahman A. and Alamri, Hammoudeh S. and Kamal Z., Zamli (2020) Modified Opposition Based Learning to Improve Harmony Search Variants Exploration. In: 4th International Conference of Reliable Information and Communication Technology. IRICT 2019, 22-23 September 2019 , Johor Bahru, Johor, Malaysia. pp. 279-287., 1073. ISBN 978-3-030-33582-3 https://doi.org/10.1007/978-3-030-33582-3_27
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
spellingShingle	QA76 Computer software Al-Omoush, Alaa A. Alsewari, Abdulrahman A. Alamri, Hammoudeh S. Kamal Z., Zamli Modified Opposition Based Learning to Improve Harmony Search Variants Exploration
description	Harmony Search Algorithm (HS) is a well-known optimization algorithm with strong and robust exploitation process. HS such as many optimization algorithms suffers from a weak exploration and susceptible to fall in local optima. Owing to its weaknesses, many variants of HS were introduced in the last decade to improve its performance. The Opposition-based learning and its variants have been successfully employed to improve many optimization algorithms, including HS. Opposition-based learning variants enhanced the explorations and help optimization algorithms to avoid local optima falling. Thus, inspired by a new opposition-based learning variant named modified opposition-based learning (MOBL), this research employed the MOBL to improve five well-known variants of HS. The new improved variants are evaluated using nine classical benchmark function and compared with the original variants to evaluate the effectiveness of the proposed technique. The results show that MOBL improved the HS variants in term of exploration and convergence rate.
format	Conference or Workshop Item
author	Al-Omoush, Alaa A. Alsewari, Abdulrahman A. Alamri, Hammoudeh S. Kamal Z., Zamli
author_facet	Al-Omoush, Alaa A. Alsewari, Abdulrahman A. Alamri, Hammoudeh S. Kamal Z., Zamli
author_sort	Al-Omoush, Alaa A.
title	Modified Opposition Based Learning to Improve Harmony Search Variants Exploration
title_short	Modified Opposition Based Learning to Improve Harmony Search Variants Exploration
title_full	Modified Opposition Based Learning to Improve Harmony Search Variants Exploration
title_fullStr	Modified Opposition Based Learning to Improve Harmony Search Variants Exploration
title_full_unstemmed	Modified Opposition Based Learning to Improve Harmony Search Variants Exploration
title_sort	modified opposition based learning to improve harmony search variants exploration
publisher	Springer
publishDate	2020
url	http://umpir.ump.edu.my/id/eprint/26391/ http://umpir.ump.edu.my/id/eprint/26391/ http://umpir.ump.edu.my/id/eprint/26391/1/Camera%20Ready%20Paper..pdf
first_indexed	2023-09-18T22:41:04Z
last_indexed	2023-09-18T22:41:04Z
_version_	1777416918274146304

Modified Opposition Based Learning to Improve Harmony Search Variants Exploration

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