Engineering design applications of surrogate-assisted
optimization techniques
Engineering design applications of surrogate-assisted
optimization techniques
The construction of models aimed at learning the behaviour of a system whose responses to inputs are expensive to measure is a branch of statistical science that has been around for a very long time. Geostatistics has pioneered a drive over the last half century towards a better understanding of the accuracy of such ‘surrogate’ models of the expensive function. Of particular interest to us here are some of the even more recent advances related to exploiting such formulations in an optimization context. While the classic goal of the modelling process has been to achieve a uniform prediction accuracy across the domain, an economical optimization process may aim to bias the distribution of the learning budget towards promising basins of attraction. This can only happen, of course, at the expense of the global exploration of the space and thus finding the best balance may be viewed as an optimization problem in itself. We examine here a selection of the state of-the-art solutions to this type of balancing exercise through the prism of several simple, illustrative problems, followed by two ‘real world’ applications: the design of a regional airliner wing and the multi-objective search for a low environmental impact house
243-265
Sobester, Andras
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Forrester, Alexander I.J.
176bf191-3fc2-46b4-80e0-9d9a0cd7a572
Toal, David J.J.
dc67543d-69d2-4f27-a469-42195fa31a68
Tresidder, Es
585d665e-78a3-453e-a8b6-cd9a3b5196a8
Tucker, Simon
678b892a-c71f-4201-be2a-bd30174b7d07
March 2014
Sobester, Andras
096857b0-cad6-45ae-9ae6-e66b8cc5d81b
Forrester, Alexander I.J.
176bf191-3fc2-46b4-80e0-9d9a0cd7a572
Toal, David J.J.
dc67543d-69d2-4f27-a469-42195fa31a68
Tresidder, Es
585d665e-78a3-453e-a8b6-cd9a3b5196a8
Tucker, Simon
678b892a-c71f-4201-be2a-bd30174b7d07
Sobester, Andras, Forrester, Alexander I.J., Toal, David J.J., Tresidder, Es and Tucker, Simon
(2014)
Engineering design applications of surrogate-assisted
optimization techniques.
Optimization and Engineering, 15 (1), .
(doi:10.1007/s11081-012-9199-x).
Abstract
The construction of models aimed at learning the behaviour of a system whose responses to inputs are expensive to measure is a branch of statistical science that has been around for a very long time. Geostatistics has pioneered a drive over the last half century towards a better understanding of the accuracy of such ‘surrogate’ models of the expensive function. Of particular interest to us here are some of the even more recent advances related to exploiting such formulations in an optimization context. While the classic goal of the modelling process has been to achieve a uniform prediction accuracy across the domain, an economical optimization process may aim to bias the distribution of the learning budget towards promising basins of attraction. This can only happen, of course, at the expense of the global exploration of the space and thus finding the best balance may be viewed as an optimization problem in itself. We examine here a selection of the state of-the-art solutions to this type of balancing exercise through the prism of several simple, illustrative problems, followed by two ‘real world’ applications: the design of a regional airliner wing and the multi-objective search for a low environmental impact house
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More information
Accepted/In Press date: 8 August 2012
e-pub ahead of print date: 8 September 2012
Published date: March 2014
Organisations:
Computational Engineering & Design Group
Identifiers
Local EPrints ID: 342651
URI: http://eprints.soton.ac.uk/id/eprint/342651
ISSN: 1389-4420
PURE UUID: cfc6b8c4-99fd-4796-a057-efeefbe9b9e4
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Date deposited: 12 Sep 2012 10:34
Last modified: 15 Mar 2024 03:29
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Author:
Es Tresidder
Author:
Simon Tucker
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