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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
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
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
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.

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OA3-1 Li A Dual Kriging Approach with Improved Points Selection Algorithm - Other
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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
ORCID for J.K. Sykulski: ORCID iD orcid.org/0000-0001-6392-126X

Catalogue record

Date deposited: 14 Sep 2015 14:33
Last modified: 01 Oct 2019 01:05

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