A Dual Kriging Approach with Improved Points Selection Algorithm for Memory Efficient Surrogate Optimisation in Electromagnetics
A Dual Kriging Approach with Improved Points Selection Algorithm for Memory Efficient Surrogate Optimisation in Electromagnetics
The paper introduces a new approach to kriging surrogate model sampling points allocation. By introducing a second (dual) kriging during the model construction process the existing sampling points are reallocated to reduce overall memory requirements. Moreover, a new algorithm is suggested for selecting the position of the next sampling point by utilising a modified Expected Improvement criterion.
Kriging, global optimisation, surrogate modelling, large datasets.
Li, Yinjiang
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Xiao, Song
7836d26c-8286-4fb3-ae82-c9522e63630e
Rotaru, M.
c53c5038-2fed-4ace-8fad-9f95d4c95b7e
Sykulski, J.K.
d6885caf-aaed-4d12-9ef3-46c4c3bbd7fb
28 June 2015
Li, Yinjiang
035e8693-c6e6-4a91-8e10-9232fd0c3112
Xiao, Song
7836d26c-8286-4fb3-ae82-c9522e63630e
Rotaru, M.
c53c5038-2fed-4ace-8fad-9f95d4c95b7e
Sykulski, J.K.
d6885caf-aaed-4d12-9ef3-46c4c3bbd7fb
Li, Yinjiang, Xiao, Song, Rotaru, M. and Sykulski, J.K.
(2015)
A Dual Kriging Approach with Improved Points Selection Algorithm for Memory Efficient Surrogate Optimisation in Electromagnetics.
Compumag 2015.
28 Jun - 02 Jul 2015.
2 pp
.
Record type:
Conference or Workshop Item
(Paper)
Abstract
The paper introduces a new approach to kriging surrogate model sampling points allocation. By introducing a second (dual) kriging during the model construction process the existing sampling points are reallocated to reduce overall memory requirements. Moreover, a new algorithm is suggested for selecting the position of the next sampling point by utilising a modified Expected Improvement criterion.
Text
OA3-1 Li A Dual Kriging Approach with Improved Points Selection Algorithm
- Other
More information
Published date: 28 June 2015
Venue - Dates:
Compumag 2015, 2015-06-28 - 2015-07-02
Keywords:
Kriging, global optimisation, surrogate modelling, large datasets.
Organisations:
EEE
Identifiers
Local EPrints ID: 381561
URI: http://eprints.soton.ac.uk/id/eprint/381561
PURE UUID: 912f23c6-f9e0-4490-a658-62326a48e93a
Catalogue record
Date deposited: 14 Sep 2015 14:33
Last modified: 15 Mar 2024 02:34
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Contributors
Author:
Yinjiang Li
Author:
Song Xiao
Author:
M. Rotaru
Author:
J.K. Sykulski
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