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

Mathematical Biosciences and Engineering

Qingyu Yang¹  Yang Cao¹  Xiaoke Li¹  Jun Ma¹  Yang Wang²  Xinyu Han³  Lei Fan³ 

[1] 1. Henan Key Laboratory of Mechanical Equipment Intelligent Manufacturing, School of Mechanical and Electrical Engineering, Zhengzhou University of Light Industry, Zhengzhou, MO 450002, China;2. Zhengzhou Railway Vocational and Technical College, Zhengzhou, MO 451460, China;3. China Railway Engineering Equipment Group Co., Ltd, Zhengzhou, MO 450002, China;

关键词: reliability-based design optimization;    surrogate modeling;    sequential sampling;    reliability analysis;   

DOI  :  10.3934/mbe.2021317

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown