Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 이태희 | - |
dc.date.accessioned | 2018-03-14T06:00:25Z | - |
dc.date.available | 2018-03-14T06:00:25Z | - |
dc.date.issued | 2014-05 | - |
dc.identifier.citation | JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY, 권: 29, 호: 4, 페이지: 1421-1427 | en_US |
dc.identifier.issn | 1738-494X | - |
dc.identifier.issn | 1976-3824 | - |
dc.identifier.uri | https://link.springer.com/article/10.1007%2Fs12206-015-0313-9 | - |
dc.identifier.uri | http://hdl.handle.net/20.500.11754/46652 | - |
dc.description.abstract | Sequential 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.sponsorship | This 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.iso | en | en_US |
dc.publisher | KOREAN SOC MECHANICAL ENGINEERS, KSTC NEW BLD. 7TH FLOOR, 635-4 YEOKSAM-DONG KANGNAM-KU, SEOUL 135-703, SOUTH KOREA | en_US |
dc.subject | Constrained global optimization | en_US |
dc.subject | Metamodel-based design optimization | en_US |
dc.subject | Kriging surrogate model | en_US |
dc.subject | Stochastic global optimization | en_US |
dc.title | Statistical Surrogate Model based Sampling Criterion for Stochastic Global Optimization of Problems with Constraints | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1007/s12206-015-0313-9 | - |
dc.relation.page | 1-6 | - |
dc.contributor.googleauthor | Cho, Su-gil | - |
dc.contributor.googleauthor | Jang, Junyong | - |
dc.contributor.googleauthor | Kim, Jihoon | - |
dc.contributor.googleauthor | Lee, Minuk | - |
dc.contributor.googleauthor | Choi, Jong-Su | - |
dc.contributor.googleauthor | Hong, Sup | - |
dc.contributor.googleauthor | Lee, Tae Hee | - |
dc.sector.campus | S | - |
dc.sector.daehak | COLLEGE OF ENGINEERING[S] | - |
dc.sector.department | DEPARTMENT OF AUTOMOTIVE ENGINEERING | - |
dc.identifier.pid | thlee | - |
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