TY  - EJOU
AU  - Meng, Debiao 
AU  - Yang, Shiyuan 
AU  - Lin, Tao 
AU  - Wang, Jiapeng 
AU  - Yang, Hengfei 
AU  - Lv, Zhiyuan 

TI  - RBMDO Using Gaussian Mixture Model-Based Second-Order Mean-Value Saddlepoint Approximation
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2022
VL  - 132
IS  - 2
SN  - 1526-1506

AB  - Actual engineering systems will be inevitably affected by uncertain factors. Thus, the Reliability-Based Multidisciplinary Design Optimization (RBMDO) has become a hotspot for recent research and application in complex engineering system design. The Second-Order/First-Order Mean-Value Saddlepoint Approximate (SOMVSA/FOMVSA) are two popular reliability analysis strategies that are widely used in RBMDO. However, the SOMVSA method can only be used efficiently when the distribution of input variables is Gaussian distribution, which significantly limits its application. In this study, the Gaussian Mixture Model-based Second-Order Mean-Value Saddlepoint Approximation (GMM-SOMVSA) is introduced to tackle above problem. It is integrated with the Collaborative Optimization (CO) method to solve RBMDO problems. Furthermore, the formula and procedure of RBMDO using GMM-SOMVSA-Based CO(GMM-SOMVSA-CO) are proposed. Finally, an engineering example is given to show the application of the GMM-SOMVSA-CO method.
KW  - Uncertain factors; reliability-based multidisciplinary design optimization; saddlepoint approximate; gaussian mixture model; collaborative optimization

DO  - 10.32604/cmes.2022.020756