Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

효율적인 제한조건경계 샘플링을 이용한 신뢰성 기반 순차적 근사 최적화

Full metadata record
DC Field Value Language
dc.contributor.author최상인-
dc.contributor.author김지훈-
dc.contributor.author이태희-
dc.contributor.author박정수-
dc.contributor.author정상현-
dc.date.accessioned2021-07-30T05:22:52Z-
dc.date.available2021-07-30T05:22:52Z-
dc.date.created2021-05-14-
dc.date.issued2019-11-
dc.identifier.urihttps://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/4497-
dc.description.abstractThere are two types of sequential approximate method: Sequential approximate optimization (SAO) is a global optimization that finds a global optimum using sequentially constructed surrogate model; and sequential approximate reliability analysis (SARA) is a method that sequentially generates sample points on constraint boundaries and performs reliability analysis using surrogate model. However, the optimums in SAO are likely to fall in failure region because the optimums are found by deterministic design optimization (DDO); and SARA does not guarantee that optimum is the global optimum. Because each method has the drawbacks individually, the method is necessary that complements the drawbacks while having the advantages of each method. In this paper, reliability-based SAO that apply efficient constraint boundary sampling (ECBS) to SAO is proposed to obtain the global optimum in RBDO problem. Reliabilitybased SAO generates sample points sequentially on the constraint boundaries that object function value is lower than the optimum and the region that is high probability of feasibility and far from existing sample points. Therefore, reliability-based SAO enhance not only the probability of finding the global optimum, but also reliability accuracy of the optimums. The accuracy of reliability-based SAO is verified by mathematical examples.-
dc.language한국어-
dc.language.isoko-
dc.publisher대한기계학회-
dc.title효율적인 제한조건경계 샘플링을 이용한 신뢰성 기반 순차적 근사 최적화-
dc.title.alternativeReliability-based Sequential Approximate Optimization using Efficient Constraint Boundary Sampling-
dc.typeArticle-
dc.contributor.affiliatedAuthor이태희-
dc.identifier.bibliographicCitation대한기계학회 2019년 학술대회, pp.1203 - 1204-
dc.relation.isPartOf대한기계학회 2019년 학술대회-
dc.citation.title대한기계학회 2019년 학술대회-
dc.citation.startPage1203-
dc.citation.endPage1204-
dc.type.rimsART-
dc.type.docTypeProceeding-
dc.description.journalClass3-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassother-
dc.subject.keywordAuthor순차적 근사 최적화(Sequential approximate optimization)-
dc.subject.keywordAuthor순차적 근사 신뢰성 해석(Sequential approximate reliability analysis)-
dc.subject.keywordAuthor신뢰성 기반 최적설계(Reliability-based design optimization)-
dc.subject.keywordAuthor전역 최적해(Global optimum)-
dc.identifier.urlhttps://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE09345230-
Files in This Item
Go to Link
Appears in
Collections
서울 공과대학 > 서울 미래자동차공학과 > 1. Journal Articles

qrcode

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

Related Researcher

Researcher Lee, Tae Hee photo

Lee, Tae Hee
COLLEGE OF ENGINEERING (DEPARTMENT OF AUTOMOTIVE ENGINEERING)
Read more

Altmetrics

Total Views & Downloads

BROWSE