Development of surrogate models in reliability-based design optimization: A review

Reliability-based design optimization (RBDO) is applied to handle the unavoidable uncertainties in engineering applications. To alleviate the huge computational burden in reliability analysis and design optimization, surrogate models are introduced to replace the implicit objective and performance f...

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Autores principales:	Xiaoke Li, Qingyu Yang, Yang Wang, Xinyu Han, Yang Cao, Lei Fan, Jun Ma
Formato:	article
Lenguaje:	EN
Publicado:	AIMS Press 2021
Materias:	reliability-based design optimization surrogate modeling sequential sampling reliability analysis Biotechnology TP248.13-248.65 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/d36ddfc7038341b5b00f8ba6266da01f
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spelling	oai:doaj.org-article:d36ddfc7038341b5b00f8ba6266da01f2021-11-11T01:25:36ZDevelopment of surrogate models in reliability-based design optimization: A review10.3934/mbe.20213171551-0018https://doaj.org/article/d36ddfc7038341b5b00f8ba6266da01f2021-07-01T00:00:00Zhttps://www.aimspress.com/article/doi/10.3934/mbe.2021317?viewType=HTMLhttps://doaj.org/toc/1551-0018Reliability-based design optimization (RBDO) is applied to handle the unavoidable uncertainties in engineering applications. To alleviate the huge computational burden in reliability analysis and design optimization, surrogate models are introduced to replace the implicit objective and performance functions. In this paper, the commonly used surrogate modeling methods and surrogate-assisted RBDO methods are reviewed and discussed. First, the existing reliability analysis methods, RBDO methods, commonly used surrogate models in RBDO, sample selection methods and accuracy evaluation methods of surrogate models are summarized and compared. Then the surrogate-assisted RBDO methods are classified into global modeling methods and local modeling methods. A classic two-dimensional RBDO numerical example are used to demonstrate the performance of representative global modeling method (Constraint Boundary Sampling, CBS) and local modeling method (Local Adaptive Sampling, LAS). The advantages and disadvantages of these two kinds of modeling methods are summarized and compared. Finally, summary and prospect of the surrogate–assisted RBDO methods are drown.Xiaoke Li Qingyu YangYang WangXinyu HanYang Cao Lei FanJun Ma AIMS Pressarticlereliability-based design optimizationsurrogate modelingsequential samplingreliability analysisBiotechnologyTP248.13-248.65MathematicsQA1-939ENMathematical Biosciences and Engineering, Vol 18, Iss 5, Pp 6386-6409 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	reliability-based design optimization surrogate modeling sequential sampling reliability analysis Biotechnology TP248.13-248.65 Mathematics QA1-939
spellingShingle	reliability-based design optimization surrogate modeling sequential sampling reliability analysis Biotechnology TP248.13-248.65 Mathematics QA1-939 Xiaoke Li Qingyu Yang Yang Wang Xinyu Han Yang Cao Lei Fan Jun Ma Development of surrogate models in reliability-based design optimization: A review
description	Reliability-based design optimization (RBDO) is applied to handle the unavoidable uncertainties in engineering applications. To alleviate the huge computational burden in reliability analysis and design optimization, surrogate models are introduced to replace the implicit objective and performance functions. In this paper, the commonly used surrogate modeling methods and surrogate-assisted RBDO methods are reviewed and discussed. First, the existing reliability analysis methods, RBDO methods, commonly used surrogate models in RBDO, sample selection methods and accuracy evaluation methods of surrogate models are summarized and compared. Then the surrogate-assisted RBDO methods are classified into global modeling methods and local modeling methods. A classic two-dimensional RBDO numerical example are used to demonstrate the performance of representative global modeling method (Constraint Boundary Sampling, CBS) and local modeling method (Local Adaptive Sampling, LAS). The advantages and disadvantages of these two kinds of modeling methods are summarized and compared. Finally, summary and prospect of the surrogate–assisted RBDO methods are drown.
format	article
author	Xiaoke Li Qingyu Yang Yang Wang Xinyu Han Yang Cao Lei Fan Jun Ma
author_facet	Xiaoke Li Qingyu Yang Yang Wang Xinyu Han Yang Cao Lei Fan Jun Ma
author_sort	Xiaoke Li
title	Development of surrogate models in reliability-based design optimization: A review
title_short	Development of surrogate models in reliability-based design optimization: A review
title_full	Development of surrogate models in reliability-based design optimization: A review
title_fullStr	Development of surrogate models in reliability-based design optimization: A review
title_full_unstemmed	Development of surrogate models in reliability-based design optimization: A review
title_sort	development of surrogate models in reliability-based design optimization: a review
publisher	AIMS Press
publishDate	2021
url	https://doaj.org/article/d36ddfc7038341b5b00f8ba6266da01f
work_keys_str_mv	AT xiaokeli developmentofsurrogatemodelsinreliabilitybaseddesignoptimizationareview AT qingyuyang developmentofsurrogatemodelsinreliabilitybaseddesignoptimizationareview AT yangwang developmentofsurrogatemodelsinreliabilitybaseddesignoptimizationareview AT xinyuhan developmentofsurrogatemodelsinreliabilitybaseddesignoptimizationareview AT yangcao developmentofsurrogatemodelsinreliabilitybaseddesignoptimizationareview AT leifan developmentofsurrogatemodelsinreliabilitybaseddesignoptimizationareview AT junma developmentofsurrogatemodelsinreliabilitybaseddesignoptimizationareview
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Development of surrogate models in reliability-based design optimization: A review

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