Hybrid Harmony Search Algorithm with Grey Wolf Optimizer and Modified Opposition-based Learning

Most metaheuristic algorithms, including harmony search (HS), suffer from parameter selection. Many variants have been developed to cope with this problem and improve algorithm performance. In this paper, a hybrid algorithm of HS with grey wolf optimizer (GWO) has been developed to solve the problem...

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Main Authors:	Alomoush, Alaa A., Alsewari, Abdulrahman A., Alamri, Hammoudeh S., Aloufi, Khalid, Kamal Z., Zamli
Format:	Article
Language:	English
Published:	IEEE 2019
Subjects:	QA75 Electronic computers. Computer science QA76 Computer software
Online Access:	http://umpir.ump.edu.my/id/eprint/25051/ http://umpir.ump.edu.my/id/eprint/25051/ http://umpir.ump.edu.my/id/eprint/25051/ http://umpir.ump.edu.my/id/eprint/25051/8/Hybrid%20Harmony%20Search%20Algorithm%20with%20Grey%20Wolf1.pdf

id	ump-25051
recordtype	eprints
spelling	ump-250512019-07-03T06:19:45Z http://umpir.ump.edu.my/id/eprint/25051/ Hybrid Harmony Search Algorithm with Grey Wolf Optimizer and Modified Opposition-based Learning Alomoush, Alaa A. Alsewari, Abdulrahman A. Alamri, Hammoudeh S. Aloufi, Khalid Kamal Z., Zamli QA75 Electronic computers. Computer science QA76 Computer software Most metaheuristic algorithms, including harmony search (HS), suffer from parameter selection. Many variants have been developed to cope with this problem and improve algorithm performance. In this paper, a hybrid algorithm of HS with grey wolf optimizer (GWO) has been developed to solve the problem of HS parameter selection. Then, a modified version of opposition-based learning technique has been applied on the hybrid algorithm to improve the HS exploration because HS easily gets trapped into local optima. Two HS parameters were automatically updated using GWO, namely, pitch adjustment rate and bandwidth. The proposed hybrid algorithm for global optimization problems is called GWO-HS. GWO-HS was evaluated using 24 classical benchmark functions with 30 state-of-the-art benchmark functions from CEC2014. Then, GWO-HS has been compared with recent HS variants and other well-known metaheuristic algorithms. Results show that the GWO-HS is superior over the old HS variants and other well-known metaheuristics in terms of accuracy and speed process. IEEE 2019-05-20 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/25051/8/Hybrid%20Harmony%20Search%20Algorithm%20with%20Grey%20Wolf1.pdf Alomoush, Alaa A. and Alsewari, Abdulrahman A. and Alamri, Hammoudeh S. and Aloufi, Khalid and Kamal Z., Zamli (2019) Hybrid Harmony Search Algorithm with Grey Wolf Optimizer and Modified Opposition-based Learning. IEEE Access, 7. 68764- 68785. ISSN 2169-3536 https://doi.org/10.1109/ACCESS.2019.2917803 https://doi.org/10.1109/ACCESS.2019.2917803
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	QA75 Electronic computers. Computer science QA76 Computer software
spellingShingle	QA75 Electronic computers. Computer science QA76 Computer software Alomoush, Alaa A. Alsewari, Abdulrahman A. Alamri, Hammoudeh S. Aloufi, Khalid Kamal Z., Zamli Hybrid Harmony Search Algorithm with Grey Wolf Optimizer and Modified Opposition-based Learning
description	Most metaheuristic algorithms, including harmony search (HS), suffer from parameter selection. Many variants have been developed to cope with this problem and improve algorithm performance. In this paper, a hybrid algorithm of HS with grey wolf optimizer (GWO) has been developed to solve the problem of HS parameter selection. Then, a modified version of opposition-based learning technique has been applied on the hybrid algorithm to improve the HS exploration because HS easily gets trapped into local optima. Two HS parameters were automatically updated using GWO, namely, pitch adjustment rate and bandwidth. The proposed hybrid algorithm for global optimization problems is called GWO-HS. GWO-HS was evaluated using 24 classical benchmark functions with 30 state-of-the-art benchmark functions from CEC2014. Then, GWO-HS has been compared with recent HS variants and other well-known metaheuristic algorithms. Results show that the GWO-HS is superior over the old HS variants and other well-known metaheuristics in terms of accuracy and speed process.
format	Article
author	Alomoush, Alaa A. Alsewari, Abdulrahman A. Alamri, Hammoudeh S. Aloufi, Khalid Kamal Z., Zamli
author_facet	Alomoush, Alaa A. Alsewari, Abdulrahman A. Alamri, Hammoudeh S. Aloufi, Khalid Kamal Z., Zamli
author_sort	Alomoush, Alaa A.
title	Hybrid Harmony Search Algorithm with Grey Wolf Optimizer and Modified Opposition-based Learning
title_short	Hybrid Harmony Search Algorithm with Grey Wolf Optimizer and Modified Opposition-based Learning
title_full	Hybrid Harmony Search Algorithm with Grey Wolf Optimizer and Modified Opposition-based Learning
title_fullStr	Hybrid Harmony Search Algorithm with Grey Wolf Optimizer and Modified Opposition-based Learning
title_full_unstemmed	Hybrid Harmony Search Algorithm with Grey Wolf Optimizer and Modified Opposition-based Learning
title_sort	hybrid harmony search algorithm with grey wolf optimizer and modified opposition-based learning
publisher	IEEE
publishDate	2019
url	http://umpir.ump.edu.my/id/eprint/25051/ http://umpir.ump.edu.my/id/eprint/25051/ http://umpir.ump.edu.my/id/eprint/25051/ http://umpir.ump.edu.my/id/eprint/25051/8/Hybrid%20Harmony%20Search%20Algorithm%20with%20Grey%20Wolf1.pdf
first_indexed	2023-09-18T22:38:16Z
last_indexed	2023-09-18T22:38:16Z
_version_	1777416741821874176

Hybrid Harmony Search Algorithm with Grey Wolf Optimizer and Modified Opposition-based Learning

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