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Erschienen in: Structural and Multidisciplinary Optimization 1/2019

16.07.2018 | RESEARCH PAPER

Comparative study of HDMRs and other popular metamodeling techniques for high dimensional problems

verfasst von: Liming Chen, Hu Wang, Fan Ye, Wei Hu

Erschienen in: Structural and Multidisciplinary Optimization | Ausgabe 1/2019

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Abstract

The efficiency of optimization for the high dimensional problem has been improved by the metamodeling techniques in multidisciplinary in the past decades. In this study, comparative studies are implemented for high dimensional problems on the accuracy of four popular metamodeling methods, Kriging (KRG), radial basis function (RBF), least square support vector regression (LSSVR) and cut-high dimensional model representation (cut-HDMR) methods. Besides, HDMR methods with different basis functions are considered, including KRG-HDMR, RBF-HDMR and SVR-HDMR. Four factors that might influence the quality of metamodeling methods involving parameter interaction of problems, sample sizes, noise level and sampling strategies are considered. The results show that the LSSVR with Gaussian kernel, using Latin hypercube sampling (LHS) strategy, constructs more accurate metamodels than the KRG. The RBF with Gaussian basis function performs poor in the group. Generally, cut-HDMR methods perform much better than the other metamodeling methods when handling the function with weak parameter interaction, but not better when handling the function with strong parameter interaction.

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Metadaten
Titel
Comparative study of HDMRs and other popular metamodeling techniques for high dimensional problems
verfasst von
Liming Chen
Hu Wang
Fan Ye
Wei Hu
Publikationsdatum
16.07.2018
Verlag
Springer Berlin Heidelberg
Erschienen in
Structural and Multidisciplinary Optimization / Ausgabe 1/2019
Print ISSN: 1615-147X
Elektronische ISSN: 1615-1488
DOI
https://doi.org/10.1007/s00158-018-2046-8

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