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International Journal for Uncertainty Quantification
Главный редактор: Habib N. Najm (open in a new tab)
Ассоциированный редакторs: Dongbin Xiu (open in a new tab) Tao Zhou (open in a new tab)
Редактор-основатель: Nicholas Zabaras (open in a new tab)

Выходит 6 номеров в год

ISSN Печать: 2152-5080

ISSN Онлайн: 2152-5099

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 1.7 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 1.9 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 0.5 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.0007 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.5 SJR: 0.584 SNIP: 0.676 CiteScore™:: 3 H-Index: 25

Indexed in

A CROSS-ENTROPY METHOD ACCELERATED DERIVATIVE-FREE RBDO ALGORITHM

Том 6, Выпуск 6, 2016, pp. 487-500
DOI: 10.1615/Int.J.UncertaintyQuantification.2016017305
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Краткое описание

Many engineering applications require optimization of the system performance subject to reliability constraints, which are commonly referred to as the reliability based design and optimization (RBDO) problems. In this work we propose a derivative-free algorithm to solve the RBDO problems. In particular, we focus on the type of RBDO problems where the objective function is deterministic and easy to evaluate, whereas the reliability constraints involve very small failure probabilities. The algorithm consists of solving a set of subproblems, in which simple surrogate models of the reliability constraints are constructed and used in solving the subproblems. Moreover, we employ a cross-entropy (CE) method with sample reweighting to evaluate the rare failure probabilities, which constructs the surrogate for the reliability constraints by performing only a single full CE simulation in each iteration. Finally we demonstrate the performance of the proposed method with both academic and practical examples.

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