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dc.contributor.author이태희-
dc.date.accessioned2016-10-31T05:10:46Z-
dc.date.available2016-10-31T05:10:46Z-
dc.date.issued2015-04-
dc.identifier.citationJOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY, v. 29, NO 4, Page. 1421-1427en_US
dc.identifier.issn1738-494X-
dc.identifier.issn1976-3824-
dc.identifier.urihttp://link.springer.com/article/10.1007%2Fs12206-015-0313-9-
dc.identifier.urihttp://hdl.handle.net/20.500.11754/24020-
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.en_US
dc.description.sponsorshipThis study was conducted as a part of a National Project, Development of Deep-seabed Mining Technology, sponsored by the Ministry of Oceans and Fisheries, Korea. The authors appreciate support for this research.en_US
dc.language.isoenen_US
dc.publisherKOREAN SOC MECHANICAL ENGINEERSen_US
dc.subjectConstrained global optimizationen_US
dc.subjectMetamodel-based design optimizationen_US
dc.subjectKriging surrogate modelen_US
dc.subjectStochastic global optimizationen_US
dc.titleStatistical surrogate model based sampling criterion for stochastic global optimization of problems with constraintsen_US
dc.typeArticleen_US
dc.relation.no4-
dc.relation.volume29-
dc.identifier.doi10.1007/s12206-015-0313-9-
dc.relation.page1421-1427-
dc.relation.journalJOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY-
dc.contributor.googleauthorCho, Su-gil-
dc.contributor.googleauthorJang, Junyong-
dc.contributor.googleauthorKim, Jihoon-
dc.contributor.googleauthorLee, Minuk-
dc.contributor.googleauthorChoi, Jong-Su-
dc.contributor.googleauthorHong, Sup-
dc.contributor.googleauthorLee, Tae Hee-
dc.relation.code2015005986-
dc.sector.campusS-
dc.sector.daehakCOLLEGE OF ENGINEERING[S]-
dc.sector.departmentDEPARTMENT OF AUTOMOTIVE ENGINEERING-
dc.identifier.pidthlee-
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COLLEGE OF ENGINEERING[S](공과대학) > AUTOMOTIVE ENGINEERING(미래자동차공학과) > Articles
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