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A kriging based optimization approach for large datasets exploiting points aggregation techniques

A kriging based optimization approach for large datasets exploiting points aggregation techniques
A kriging based optimization approach for large datasets exploiting points aggregation techniques
A kriging based optimization approach is proposed for problems with large datasets and high dimensionality. Memory usage is maintained via model centering aided by minimizing the impact of information loss on accuracy of new point prediction using points aggregation techniques. The 8-parameter TEAM problem 22 is revisited in the context of computational efficiency and accuracy.
Kriging, Surrogate optimization, Clustering, Large datasets
0018-9464
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 (2017) A kriging based optimization approach for large datasets exploiting points aggregation techniques. IEEE Transactions on Magnetics. (doi:10.1109/TMAG.2017.2665703).

Record type: Article

Abstract

A kriging based optimization approach is proposed for problems with large datasets and high dimensionality. Memory usage is maintained via model centering aided by minimizing the impact of information loss on accuracy of new point prediction using points aggregation techniques. The 8-parameter TEAM problem 22 is revisited in the context of computational efficiency and accuracy.

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IEEEvol53no2017 - Accepted Manuscript
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More information

Accepted/In Press date: 8 February 2017
e-pub ahead of print date: 8 February 2017
Keywords: Kriging, Surrogate optimization, Clustering, Large datasets
Organisations: Electronics & Computer Science, EEE

Identifiers

Local EPrints ID: 406216
URI: http://eprints.soton.ac.uk/id/eprint/406216
ISSN: 0018-9464
PURE UUID: 8df67ebc-75ff-41e9-9b13-bffb59028746
ORCID for Jan Sykulski: ORCID iD orcid.org/0000-0001-6392-126X

Catalogue record

Date deposited: 10 Mar 2017 10:42
Last modified: 17 Dec 2019 02:04

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