Adaptive Metamodel Based Analytical Target Cascading for Multidisciplinary Design Optimization

摘要：

　　Analytical target cascading(ATC)provides a systematic approach in solving decomposed large scale systems that has solvable subsystems.However,complex engineering system usually has a high computational cost,which result in limiting real-life applications of ATC based on high-fidelity simulation models.To address these problems,this paper aims to develop an adaptive metamodel based ATC,to reduce computational cost associated with multidisciplinary design optimization problems,in which anmetamodelis created to replace the analysis module of the children element.Furthermore a parameterized lower parameterized lower confidence bounding scheme is applied in the optimization module to adaptively update the metamodel in analysis module.The effectiveness of the method for modeling and adaptively updating metamodels under the ATC framework is studied.Overall results show the methods introduced in this paper provide an effective way of improving computational efficiency of the ATC method based on adaptive metamodels.

关键词： Adaptive metamodel Analytical Target Cascading Multidisciplinary Design Optimization

作者: Zheng Jun Lu Chao Li Zhuowei

作者单位: Engineering Faculty,China University of Geosciences,Wuhan,Hubei,P.R.China school of computing,China University of Geosciences,Wuhan,Hubei,P.R.China

会议类型: 国际会议

会议名称: 第十七届国际制造会议(The 17th International Manufacturing Conference in China)(IMCC 2017))

会议地点: 深圳

会议语种:英文

页码: 1-7

在线出版日期: 2017-11-23（万方平台首次上网日期，不代表论文的发表时间）