A surrogate merit function developed for structural weight optimization problems

Sadrekarimi, N; Talatahari, S; Azar, BF; Gandomi, AH

A surrogate merit function developed for structural weight optimization problems

Sadrekarimi, N Talatahari, S Azar, BF Gandomi, AH

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Publisher:: Springer Science and Business Media LLC
Publication Type:: Journal Article
Citation:: Soft Computing, 2022, pp. 1-31
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Sadrekarimi, N
dc.contributor.author	Talatahari, S
dc.contributor.author	Azar, BF
dc.contributor.author	Gandomi, AH
dc.date.accessioned	2023-01-24T01:28:43Z
dc.date.available	2023-01-24T01:28:43Z
dc.date.issued	2022-01-01
dc.identifier.citation	Soft Computing, 2022, pp. 1-31
dc.identifier.issn	1432-7643
dc.identifier.issn	1433-7479
dc.identifier.uri	http://hdl.handle.net/10453/165386
dc.description.abstract	In this paper, a surrogate merit function (SMF) is proposed to be evaluated instead of the traditional merit functions (i.e., penalized weight of the structure). The standard format of the conventional merit functions needs several expensive trial-and-error tuning processes to enhance optimization convergence quality, retuning for different structural model configurations, and final manual local search in case optimization converges to infeasible vicinity of global optimum. However, on the other hand, SMF has no tunning factor but shows statistically stable performance for different models, converges directly to outstanding feasible points, and shows other superior advantages such as reduced required iterations to achieve convergence. In other words, this new function is a no-hassle one due to its brilliant user-friendly application and robust numerical results. SMF might be a revolutionary step in commercializing design optimization in the real-world construction market.
dc.language	en
dc.publisher	Springer Science and Business Media LLC
dc.relation.ispartof	Soft Computing
dc.relation.isbasedon	10.1007/s00500-022-07453-6
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0102 Applied Mathematics, 0801 Artificial Intelligence and Image Processing, 1702 Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	A surrogate merit function developed for structural weight optimization problems
dc.type	Journal Article
utslib.for	0102 Applied Mathematics
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	1702 Cognitive Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	open_access	*
dc.date.updated	2023-01-24T01:28:42Z
pubs.publication-status	Published

Abstract:

In this paper, a surrogate merit function (SMF) is proposed to be evaluated instead of the traditional merit functions (i.e., penalized weight of the structure). The standard format of the conventional merit functions needs several expensive trial-and-error tuning processes to enhance optimization convergence quality, retuning for different structural model configurations, and final manual local search in case optimization converges to infeasible vicinity of global optimum. However, on the other hand, SMF has no tunning factor but shows statistically stable performance for different models, converges directly to outstanding feasible points, and shows other superior advantages such as reduced required iterations to achieve convergence. In other words, this new function is a no-hassle one due to its brilliant user-friendly application and robust numerical results. SMF might be a revolutionary step in commercializing design optimization in the real-world construction market.

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http://hdl.handle.net/10453/165386