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Structural and Multidisciplinary Optimization
Erschienen in: Structural and Multidisciplinary Optimization 4/2012

01.04.2012 | Research Paper

Mid-range metamodel assembly building based on linear regression for large scale optimization problems

verfasst von: Andrey Polynkin, Vassili V. Toropov

Erschienen in: Structural and Multidisciplinary Optimization | Ausgabe 4/2012

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Abstract

In this work an approach to building a high accuracy approximation valid in a larger range of design variables is investigated. The approach is based on an assembly of multiple surrogates into a single surrogate using linear regression. The coefficients of the model assembly are not weights of the individual models but tuning parameters determined by the least squares method. The approach was utilized in the Multipoint Approximation Method (MAM) method within the mid-range approximation framework. The developed technique has been tested on several benchmark problems with up to 1000 design variables and constraints. The obtained results show a high degree of accuracy of the built approximations and the efficiency of the technique when applied to large-scale optimization problems.
Vorheriger Artikel Fast orthogonal search (FOS) versus fast Fourier transform (FFT) as spectral model estimations techniques applied for structural health monitoring (SHM)
Nächster Artikel Classification approach for reliability-based topology optimization using probabilistic neural networks

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Metadaten
Titel
Mid-range metamodel assembly building based on linear regression for large scale optimization problems
verfasst von
Andrey Polynkin
Vassili V. Toropov
Publikationsdatum
01.04.2012
Verlag
Springer-Verlag
Erschienen in
Structural and Multidisciplinary Optimization / Ausgabe 4/2012
Print ISSN: 1615-147X
Elektronische ISSN: 1615-1488
DOI
https://doi.org/10.1007/s00158-011-0692-1

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