Quality assessment of coarse models and surrogates for space mapping optimization

Publication: Research - peer-reviewJournal article – Annual report year: 2008

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Quality assessment of coarse models and surrogates for space mapping optimization. / Koziel, Slawomir; Bandler, John W.; Madsen, Kaj.

In: Optimization and Engineering, Vol. 9, No. 4, 2008, p. 375-391.

Publication: Research - peer-reviewJournal article – Annual report year: 2008

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Author

Koziel, Slawomir; Bandler, John W.; Madsen, Kaj / Quality assessment of coarse models and surrogates for space mapping optimization.

In: Optimization and Engineering, Vol. 9, No. 4, 2008, p. 375-391.

Publication: Research - peer-reviewJournal article – Annual report year: 2008

Bibtex

@article{e1ab0559910f46c8996a7d11fac2644e,
title = "Quality assessment of coarse models and surrogates for space mapping optimization",
publisher = "Springer New York LLC",
author = "Slawomir Koziel and Bandler, {John W.} and Kaj Madsen",
year = "2008",
doi = "10.1007/s11081-007-9032-0",
volume = "9",
number = "4",
pages = "375--391",
journal = "Optimization and Engineering",
issn = "1389-4420",

}

RIS

TY - JOUR

T1 - Quality assessment of coarse models and surrogates for space mapping optimization

A1 - Koziel,Slawomir

A1 - Bandler,John W.

A1 - Madsen,Kaj

AU - Koziel,Slawomir

AU - Bandler,John W.

AU - Madsen,Kaj

PB - Springer New York LLC

PY - 2008

Y1 - 2008

N2 - One of the central issues in space mapping optimization is the quality of the underlying coarse models and surrogates. Whether a coarse model is sufficiently similar to the fine model may be critical to the performance of the space mapping optimization algorithm and a poor coarse model may result in lack of convergence. Although similarity requirements can be expressed with proper analytical conditions, it is difficult to verify such conditions beforehand for real-world engineering optimization problems. In this paper, we provide methods of assessing the quality of coarse/surrogate models. These methods can be used to predict whether a given model might be successfully used in space mapping optimization, to compare the quality of different coarse models, or to choose the proper type of space mapping which would be suitable to a given engineering design problem. Our quality estimation methods are derived from convergence results for space mapping algorithms. We provide illustrations and several practical application examples.

AB - One of the central issues in space mapping optimization is the quality of the underlying coarse models and surrogates. Whether a coarse model is sufficiently similar to the fine model may be critical to the performance of the space mapping optimization algorithm and a poor coarse model may result in lack of convergence. Although similarity requirements can be expressed with proper analytical conditions, it is difficult to verify such conditions beforehand for real-world engineering optimization problems. In this paper, we provide methods of assessing the quality of coarse/surrogate models. These methods can be used to predict whether a given model might be successfully used in space mapping optimization, to compare the quality of different coarse models, or to choose the proper type of space mapping which would be suitable to a given engineering design problem. Our quality estimation methods are derived from convergence results for space mapping algorithms. We provide illustrations and several practical application examples.

U2 - 10.1007/s11081-007-9032-0

DO - 10.1007/s11081-007-9032-0

JO - Optimization and Engineering

JF - Optimization and Engineering

SN - 1389-4420

IS - 4

VL - 9

SP - 375

EP - 391

ER -