Enhancing generality of meta-heuristic algorithms through adaptive selection and hybridization

Solving complex optimization problems can be painstakingly difficult endeavor considering multiple and conflicting design goals. A growing trend in utilizing meta-heuristic algorithms to solve these problems has been observed as they have shown considerable success in dealing with tradeoffs between...

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Main Author:	Kamal Z., Zamli
Format:	Conference or Workshop Item
Language:	English
Published:	IEEE 2018
Subjects:	QA76 Computer software
Online Access:	http://umpir.ump.edu.my/id/eprint/23080/ http://umpir.ump.edu.my/id/eprint/23080/ http://umpir.ump.edu.my/id/eprint/23080/1/Enhancing%20generality%20of%20meta-heuristic%20algorithms%20through%20adaptive%20selection%20and%20hybridization.pdf

id	ump-23080
recordtype	eprints
spelling	ump-230802019-04-22T03:14:58Z http://umpir.ump.edu.my/id/eprint/23080/ Enhancing generality of meta-heuristic algorithms through adaptive selection and hybridization Kamal Z., Zamli QA76 Computer software Solving complex optimization problems can be painstakingly difficult endeavor considering multiple and conflicting design goals. A growing trend in utilizing meta-heuristic algorithms to solve these problems has been observed as they have shown considerable success in dealing with tradeoffs between conflicting design goals. Many meta-heuristic algorithms have been developed to date (e.g. Simulated Annealing (SA), Particle Swarm Optimization (PSO), Teaching Learning based Optimization (TLBO), Grey Wolf Optimizer(GWO) to name a few). Much of these algorithms have adopted elegant metaphors (e.g. heating and cooling of metals in the case of SA and swarming of flocking birds in the case of PSO) from nature in order to derive the mathematical models for generating the solution as well as provides control over their exploration (i.e. sufficient roaming of the search space) and exploitation (i.e. using known knowledge of the surroundings). In line with the no free lunch theorem (), this paper argues that rather than focusing on designing new algorithm, new research should focus on adaptive hybridization of meta-heuristics algorithms in order to compensate the limitation of one with the strengths of another. In this paper, we review the meta-heuristic and hyper-heuristic algorithms in order to highlight the current-state-of-the-arts and suggest areas for future research. IEEE 2018 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/23080/1/Enhancing%20generality%20of%20meta-heuristic%20algorithms%20through%20adaptive%20selection%20and%20hybridization.pdf Kamal Z., Zamli (2018) Enhancing generality of meta-heuristic algorithms through adaptive selection and hybridization. In: 1st International Conference on Information and Communications Technology (ICOIACT 2018), 6-7 March 2018 , Grand ZuriYogyakarta, Indonesia. pp. 67-71., 2018. ISBN 978-153860954-5 https://ieeexplore.ieee.org/abstract/document/8350825
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 Kamal Z., Zamli Enhancing generality of meta-heuristic algorithms through adaptive selection and hybridization
description	Solving complex optimization problems can be painstakingly difficult endeavor considering multiple and conflicting design goals. A growing trend in utilizing meta-heuristic algorithms to solve these problems has been observed as they have shown considerable success in dealing with tradeoffs between conflicting design goals. Many meta-heuristic algorithms have been developed to date (e.g. Simulated Annealing (SA), Particle Swarm Optimization (PSO), Teaching Learning based Optimization (TLBO), Grey Wolf Optimizer(GWO) to name a few). Much of these algorithms have adopted elegant metaphors (e.g. heating and cooling of metals in the case of SA and swarming of flocking birds in the case of PSO) from nature in order to derive the mathematical models for generating the solution as well as provides control over their exploration (i.e. sufficient roaming of the search space) and exploitation (i.e. using known knowledge of the surroundings). In line with the no free lunch theorem (), this paper argues that rather than focusing on designing new algorithm, new research should focus on adaptive hybridization of meta-heuristics algorithms in order to compensate the limitation of one with the strengths of another. In this paper, we review the meta-heuristic and hyper-heuristic algorithms in order to highlight the current-state-of-the-arts and suggest areas for future research.
format	Conference or Workshop Item
author	Kamal Z., Zamli
author_facet	Kamal Z., Zamli
author_sort	Kamal Z., Zamli
title	Enhancing generality of meta-heuristic algorithms through adaptive selection and hybridization
title_short	Enhancing generality of meta-heuristic algorithms through adaptive selection and hybridization
title_full	Enhancing generality of meta-heuristic algorithms through adaptive selection and hybridization
title_fullStr	Enhancing generality of meta-heuristic algorithms through adaptive selection and hybridization
title_full_unstemmed	Enhancing generality of meta-heuristic algorithms through adaptive selection and hybridization
title_sort	enhancing generality of meta-heuristic algorithms through adaptive selection and hybridization
publisher	IEEE
publishDate	2018
url	http://umpir.ump.edu.my/id/eprint/23080/ http://umpir.ump.edu.my/id/eprint/23080/ http://umpir.ump.edu.my/id/eprint/23080/1/Enhancing%20generality%20of%20meta-heuristic%20algorithms%20through%20adaptive%20selection%20and%20hybridization.pdf
first_indexed	2023-09-18T22:34:26Z
last_indexed	2023-09-18T22:34:26Z
_version_	1777416501377105920

Enhancing generality of meta-heuristic algorithms through adaptive selection and hybridization

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