A DESIGN OPTIMIZATION METHOD USING EVIDENCE THEORY

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外文摘要：Early in the engineering design cycle， it is difficult to quantify product reliability or compliance to performance targets due to insufficient data or information to model uncertainties。 Probability theory can not be therefore， used。 Design decisions are usually， based on fuzzy information that is vague， imprecise qualitative， linguistic or incomplete。 Recently， evidence theory has been proposed to handle uncertainty with limited information as an alternative to probability theory。 In this paper， a computationally efficient design optimization method is proposed based on evidence theory， which can handle a mixture of epistemic and random uncertainties。 It quickly identifies the vicinity of the optimal point and the active constraints by moving a hyper-ellipse in the original design space， using a reliability-based design optimization (RBDO) algorithm。 Subsequently， a derivative-free optimizer calculates the evidence-based optimum， starting from the close-by RBDO optimum， considering only the identified active constraints。 The computational cost is kept low by first moving to the vicinity of the optimum quickly and subsequently using local surrogate models of the active constraints only。 Two examples demonstrate the proposed evidence-based design optimization method。

外文关键词：

evidence-based design optimizationevidence theoryepistemic uncertaintybelief and plausibility measures

作者：

Zissimos P. Mourelatos、Jun Zhou

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作者单位：

Department of Mechanical Engineering, Oakland University, Rochester, MI 48309, USA

会议名称：

31st Design Automation Conference: (DETC2005) vol.2 pt.B

会议地点：

Long Beach,CA(US)

会议母体文献：

American Society of Mechanical Engineers(ASME) International Design Engineering Technical Conference and Computers and Information in Engineering Conference(DETC2005) vol.2 pt.B: 31st Design Automation Conference; 20050924-28; Long Beach,CA(US)

页码：

P.1153-1161

出版时间：

2005