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RBMDO Using Gaussian Mixture Model-Based Second-Order Mean-Value Saddlepoint Approximation 被引量:9
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作者 Debiao Meng Shiyuan Yang +3 位作者 Tao Lin Jiapeng Wang Hengfei Yang Zhiyuan Lv 《Computer Modeling in Engineering & Sciences》 SCIE EI 2022年第8期553-568,共16页
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 en... 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. 展开更多
关键词 Uncertain factors reliability-based multidisciplinary design optimization saddlepoint approximate gaussian mixture model collaborative optimization
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