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A dual kriging approach with improved points selection algorithm for memory efficient surrogate optimization in electromagnetics

A dual kriging approach with improved points selection algorithm for memory efficient surrogate optimization in electromagnetics
A dual kriging approach with improved points selection algorithm for memory efficient surrogate optimization in electromagnetics
This paper introduces a new approach to kriging surrogate model sampling points allocation. By introducing the second (dual) kriging during the model construction, the existing sampling points are reallocated to reduce overall memory requirements. Moreover, a new algorithm is proposed for selecting the position of the next sampling point by utilizing a modified expected improvement criterion.
global optimization, kriging, large data sets, surrogate modeling
0018-9464
1-4
Li, Yinjiang
035e8693-c6e6-4a91-8e10-9232fd0c3112
Xiao, Song
6ffa9657-513e-4b86-86a2-e560d3c09c72
Rotaru, Mihai
c53c5038-2fed-4ace-8fad-9f95d4c95b7e
Sykulski, Jan
d6885caf-aaed-4d12-9ef3-46c4c3bbd7fb
Li, Yinjiang
035e8693-c6e6-4a91-8e10-9232fd0c3112
Xiao, Song
6ffa9657-513e-4b86-86a2-e560d3c09c72
Rotaru, Mihai
c53c5038-2fed-4ace-8fad-9f95d4c95b7e
Sykulski, Jan
d6885caf-aaed-4d12-9ef3-46c4c3bbd7fb

Li, Yinjiang, Xiao, Song, Rotaru, Mihai and Sykulski, Jan (2016) A dual kriging approach with improved points selection algorithm for memory efficient surrogate optimization in electromagnetics. IEEE Transactions on Magnetics, 52 (3), 1-4. (doi:10.1109/TMAG.2015.2486522).

Record type: Article

Abstract

This paper introduces a new approach to kriging surrogate model sampling points allocation. By introducing the second (dual) kriging during the model construction, the existing sampling points are reallocated to reduce overall memory requirements. Moreover, a new algorithm is proposed for selecting the position of the next sampling point by utilizing a modified expected improvement criterion.

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IEEEvol52no3Mar2016page7000504.pdf - Accepted Manuscript
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Accepted/In Press date: 30 September 2015
e-pub ahead of print date: 5 October 2015
Published date: March 2016
Keywords: global optimization, kriging, large data sets, surrogate modeling
Organisations: EEE

Identifiers

Local EPrints ID: 393149
URI: http://eprints.soton.ac.uk/id/eprint/393149
DOI: doi:10.1109/TMAG.2015.2486522
ISSN: 0018-9464
PURE UUID: 9ff5a900-4b29-42b0-940a-73e852b868b2
ORCID for Jan Sykulski: ORCID iD orcid.org/0000-0001-6392-126X

Catalogue record

Date deposited: 21 Apr 2016 06:30
Last modified: 15 Mar 2024 02:34

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Contributors

Author: Yinjiang Li
Author: Song Xiao
Author: Mihai Rotaru
Author: Jan Sykulski ORCID iD

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