Detailed Information

Cited 6 time in webofscience Cited 7 time in scopus
Metadata Downloads

Statistical surrogate model based sampling criterion for stochastic global optimization of problems with constraints

Full metadata record
DC Field Value Language
dc.contributor.authorCho, Su-gil-
dc.contributor.authorJang, Junyong-
dc.contributor.authorKim, Jihoon-
dc.contributor.authorLee, Minuk-
dc.contributor.authorChoi, Jong-Su-
dc.contributor.authorHong, Sup-
dc.contributor.authorLee, Tae Hee-
dc.date.accessioned2021-08-03T04:43:56Z-
dc.date.available2021-08-03T04:43:56Z-
dc.date.issued2015-04-
dc.identifier.issn1738-494X-
dc.identifier.issn1976-3824-
dc.identifier.urihttps://www.kriso.re.kr/sciwatch/handle/2021.sw.kriso/776-
dc.description.abstractSequential surrogate model-based global optimization algorithms, such as super-EGO, have been developed to increase the efficiency of commonly used global optimization technique as well as to ensure the accuracy of optimization. However, earlier studies have drawbacks because there are three phases in the optimization loop and empirical parameters. We propose a united sampling criterion to simplify the algorithm and to achieve the global optimum of problems with constraints without any empirical parameters. It is able to select the points located in a feasible region with high model uncertainty as well as the points along the boundary of constraint at the lowest objective value. The mean squared error determines which criterion is more dominant among the infill sampling criterion and boundary sampling criterion. Also, the method guarantees the accuracy of the surrogate model because the sample points are not located within extremely small regions like super-EGO. The performance of the proposed method, such as the solvability of a problem, convergence properties, and efficiency, are validated through nonlinear numerical examples with disconnected feasible regions.-
dc.format.extent7-
dc.language영어-
dc.language.isoENG-
dc.publisherKOREAN SOC MECHANICAL ENGINEERS-
dc.titleStatistical surrogate model based sampling criterion for stochastic global optimization of problems with constraints-
dc.typeArticle-
dc.publisher.location대한민국-
dc.identifier.doi10.1007/s12206-015-0313-9-
dc.identifier.scopusid2-s2.0-84928327511-
dc.identifier.wosid000352685600013-
dc.identifier.bibliographicCitationJOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY, v.29, no.4, pp 1421 - 1427-
dc.citation.titleJOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY-
dc.citation.volume29-
dc.citation.number4-
dc.citation.startPage1421-
dc.citation.endPage1427-
dc.type.docTypeArticle-
dc.identifier.kciidART001977719-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.description.journalRegisteredClasskci-
dc.relation.journalResearchAreaEngineering-
dc.relation.journalWebOfScienceCategoryEngineering, Mechanical-
dc.subject.keywordAuthorConstrained global optimization-
dc.subject.keywordAuthorMetamodel-based design optimization-
dc.subject.keywordAuthorKriging surrogate model-
dc.subject.keywordAuthorStochastic global optimization-
Files in This Item
There are no files associated with this item.
Appears in
Collections
ETC > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher Hong, Sup photo

Hong, Sup
연구전략본부 (KRISO 유럽센터)
Read more

Altmetrics

Total Views & Downloads

BROWSE