Comparative evaluation of tabu search hyper-heuristic against its low-level meta-heuristic constituents

Hyper-heuristics present a superior form of hybridization of meta-heuristics. Unlike typical meta-heuristic hybridization, which requires low-level integration of two or more metaheuristics, hyper-heuristics offer meta level separation (as domain barrier) of each participating low-level meta-heurist...

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Main Authors:	Fakhrud, Din, Kamal Z., Zamli
Format:	Conference or Workshop Item
Language:	English English
Published:	2019
Subjects:	QA76 Computer software
Online Access:	http://umpir.ump.edu.my/id/eprint/25464/ http://umpir.ump.edu.my/id/eprint/25464/1/55.%20Comprative%20evaluation%20of%20tabu%20search%20hyper-heuristic.pdf http://umpir.ump.edu.my/id/eprint/25464/2/55.1%20Comprative%20evaluation%20of%20tabu%20search%20hyper-heuristic.pdf

id	ump-25464
recordtype	eprints
spelling	ump-254642020-01-10T08:06:31Z http://umpir.ump.edu.my/id/eprint/25464/ Comparative evaluation of tabu search hyper-heuristic against its low-level meta-heuristic constituents Fakhrud, Din Kamal Z., Zamli QA76 Computer software Hyper-heuristics present a superior form of hybridization of meta-heuristics. Unlike typical meta-heuristic hybridization, which requires low-level integration of two or more metaheuristics, hyper-heuristics offer meta level separation (as domain barrier) of each participating low-level meta-heuristic and permit adaptive selection between them. Owing to the prospects of improving the generality of its application to general optimization problems, this paper evaluates the performance of a Tabu search based hyper-heuristic (called HHH) against its individual low-level meta-heuristic (LLH) constituents. The results based on its application to t-way test suite generation problem indicate that HHH outperforms all its individual LLH constituents consisting of particle swarm optimization (PSO), global neighbourhood algorithm (GNA), cuckoo search (CS) algorithm and teaching learning-based opthnization algorithm (TLBO). However, there is a time performance penalty as overhead to perform the nmtime adaptive selection of each LLH. 2019 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/25464/1/55.%20Comprative%20evaluation%20of%20tabu%20search%20hyper-heuristic.pdf pdf en http://umpir.ump.edu.my/id/eprint/25464/2/55.1%20Comprative%20evaluation%20of%20tabu%20search%20hyper-heuristic.pdf Fakhrud, Din and Kamal Z., Zamli (2019) Comparative evaluation of tabu search hyper-heuristic against its low-level meta-heuristic constituents. In: 3rd International Conference On Computational Science And Information Managemant (ICOCSIM19), 20 - 23 March 2019 , Aruna Senggiri Resort And Convention Hotel,Lombok. pp. 1-6.. (Unpublished)
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English English
topic	QA76 Computer software
spellingShingle	QA76 Computer software Fakhrud, Din Kamal Z., Zamli Comparative evaluation of tabu search hyper-heuristic against its low-level meta-heuristic constituents
description	Hyper-heuristics present a superior form of hybridization of meta-heuristics. Unlike typical meta-heuristic hybridization, which requires low-level integration of two or more metaheuristics, hyper-heuristics offer meta level separation (as domain barrier) of each participating low-level meta-heuristic and permit adaptive selection between them. Owing to the prospects of improving the generality of its application to general optimization problems, this paper evaluates the performance of a Tabu search based hyper-heuristic (called HHH) against its individual low-level meta-heuristic (LLH) constituents. The results based on its application to t-way test suite generation problem indicate that HHH outperforms all its individual LLH constituents consisting of particle swarm optimization (PSO), global neighbourhood algorithm (GNA), cuckoo search (CS) algorithm and teaching learning-based opthnization algorithm (TLBO). However, there is a time performance penalty as overhead to perform the nmtime adaptive selection of each LLH.
format	Conference or Workshop Item
author	Fakhrud, Din Kamal Z., Zamli
author_facet	Fakhrud, Din Kamal Z., Zamli
author_sort	Fakhrud, Din
title	Comparative evaluation of tabu search hyper-heuristic against its low-level meta-heuristic constituents
title_short	Comparative evaluation of tabu search hyper-heuristic against its low-level meta-heuristic constituents
title_full	Comparative evaluation of tabu search hyper-heuristic against its low-level meta-heuristic constituents
title_fullStr	Comparative evaluation of tabu search hyper-heuristic against its low-level meta-heuristic constituents
title_full_unstemmed	Comparative evaluation of tabu search hyper-heuristic against its low-level meta-heuristic constituents
title_sort	comparative evaluation of tabu search hyper-heuristic against its low-level meta-heuristic constituents
publishDate	2019
url	http://umpir.ump.edu.my/id/eprint/25464/ http://umpir.ump.edu.my/id/eprint/25464/1/55.%20Comprative%20evaluation%20of%20tabu%20search%20hyper-heuristic.pdf http://umpir.ump.edu.my/id/eprint/25464/2/55.1%20Comprative%20evaluation%20of%20tabu%20search%20hyper-heuristic.pdf
first_indexed	2023-09-18T22:39:07Z
last_indexed	2023-09-18T22:39:07Z
_version_	1777416795677786112

Comparative evaluation of tabu search hyper-heuristic against its low-level meta-heuristic constituents

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