Material Generation Algorithm Combined with Epsilon Constraint Handling Scheme for Engineering Optimization

Azizi, M; Talatahari, S

Material Generation Algorithm Combined with Epsilon Constraint Handling Scheme for Engineering Optimization

Azizi, M Talatahari, S

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Publisher:: Springer Nature
Publication Type:: Chapter
Citation:: Handbook of Nature-Inspired Optimization Algorithms: The State of the Art, 2022, 213, pp. 165-187
Issue Date:: 2022-09-04

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Field	Value	Language
dc.contributor.author	Azizi, M
dc.contributor.author	Talatahari, S
dc.date.accessioned	2023-06-23T05:22:57Z
dc.date.available	2023-06-23T05:22:57Z
dc.date.issued	2022-09-04
dc.identifier.citation	Handbook of Nature-Inspired Optimization Algorithms: The State of the Art, 2022, 213, pp. 165-187
dc.identifier.isbn	978-3-031-07515-5
dc.identifier.uri	http://hdl.handle.net/10453/170841
dc.description.abstract	Metaheuristic optimization algorithms have been progressively introduced for optimization objectives in recent years, with each displaying varying degrees of aptitude for resolving various sorts of optimization problems. Hence, providing a unified judgment call for these sorts of novel techniques has been a chief challenge over the past few years. Regarding this, the recent Competitions on Evolutionary Computation (CEC) have provided some great sets of design examples of different kinds to provide acceptable levels of unbiased judgment calls regarding new and improved and hybridized optimization techniques. This book chapter implements a new constraint handling methodology to the Material Generation Algorithm (MGA) as a recently developed metaheuristic algorithm. The advanced principles of material chemistry regarding the configuration of chemical reactions and chemical compounds in generating novel materials are in perspective. The epsilon constraint handling approach as a well-known handling scheme is applied to MGA, which has been investigated for the first time as a handling approach for this algorithm. For numerical investigations, 8 of the well-known engineering design problems which have been benchmarked by the CEC are utilized. The results of different metaheuristic algorithms are given based on the literature, while the statistical results, such as the mean, worst, and standard deviation, are also calculated. The results proved that the MGA combined with epsilon constraint handling approach (εMGA) could provide better results than the MGA with penalty scheme and the other metaheuristics from the literature.
dc.format.extent	10
dc.language	en
dc.publisher	Springer Nature
dc.relation.ispartof	Handbook of Nature-Inspired Optimization Algorithms: The State of the Art
dc.relation.ispartofseries	Studies in Systems, Decision and Control
dc.relation.isbasedon	10.1007/978-3-031-07516-2_9
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Material Generation Algorithm Combined with Epsilon Constraint Handling Scheme for Engineering Optimization
dc.type	Chapter
utslib.citation.volume	213
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2023-06-23T05:22:56Z
pubs.place-of-publication	Switzerland
pubs.publication-status	Published
pubs.volume	213
dc.location	Switzerland

Abstract:

Metaheuristic optimization algorithms have been progressively introduced for optimization objectives in recent years, with each displaying varying degrees of aptitude for resolving various sorts of optimization problems. Hence, providing a unified judgment call for these sorts of novel techniques has been a chief challenge over the past few years. Regarding this, the recent Competitions on Evolutionary Computation (CEC) have provided some great sets of design examples of different kinds to provide acceptable levels of unbiased judgment calls regarding new and improved and hybridized optimization techniques. This book chapter implements a new constraint handling methodology to the Material Generation Algorithm (MGA) as a recently developed metaheuristic algorithm. The advanced principles of material chemistry regarding the configuration of chemical reactions and chemical compounds in generating novel materials are in perspective. The epsilon constraint handling approach as a well-known handling scheme is applied to MGA, which has been investigated for the first time as a handling approach for this algorithm. For numerical investigations, 8 of the well-known engineering design problems which have been benchmarked by the CEC are utilized. The results of different metaheuristic algorithms are given based on the literature, while the statistical results, such as the mean, worst, and standard deviation, are also calculated. The results proved that the MGA combined with epsilon constraint handling approach (εMGA) could provide better results than the MGA with penalty scheme and the other metaheuristics from the literature.

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http://hdl.handle.net/10453/170841