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Structural and Multidisciplinary Optimization

, Volume 59, Issue 1, pp 21–42 | Cite as

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

  • Liming Chen
  • Hu WangEmail author
  • Fan Ye
  • Wei Hu
RESEARCH PAPER
  • 294 Downloads

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.

Keywords

Metamodel High dimensional problem HDMR Parameter interaction 

Nomenclature

R

Correlation function

x

Sample point

θ

Correlation coefficient

x

Vector of sample point

\( \widehat{y} \)

Prediction of sample point

β

Weigh coefficient

ϕ

Predefined polynomial basis function

z

Realization of stochastic process

Var

Variance

ψ

Basis functions

J

Cost function

β

A vector of weights

γ

Penalty parameter

e

Error variable

y

Evaluation of sample point

D

A set of sample point

φ

Nonlinear mapping

b

Model offset

α

Lagrange multiplier

K

Kernel function

σ

Tuning parameter

R

Design domain

p

Vector consisting linear variable terms

E, F

Coefficients

N

Standard Gaussian distribution

η

Random number sampled

c

Center points

δ

Side length

g

Unit vector

w

Best function values

Subscripts

j,k

Point index

n

Dimension

t

Point index

m

Number of sample point

Notes

Acknowledgments

This work has been supported by National Key Research and Development Program of China 2017YFB0203701 and Project of the Program of National Natural Science Foundation of China under the Grant Numbers 11572120.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018
corrected publication September/2018

Authors and Affiliations

  1. 1.State Key Laboratory of Advanced Design and Manufacturing for Vehicle BodyHunan UniversityChangshaChina
  2. 2.Joint Center for Intelligent New Energy VehicleShanghaiChina

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