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Multi-objective Pareto optimization of electromagnetic devices exploiting kriging with lipschitzian optimized expected improvement

Multi-objective Pareto optimization of electromagnetic devices exploiting kriging with lipschitzian optimized expected improvement
Multi-objective Pareto optimization of electromagnetic devices exploiting kriging with lipschitzian optimized expected improvement
This paper focuses on resolving the storage issue of correlation matrices generated by kriging surrogate models in the context of electromagnetic optimization problems with many design variables and multiple objectives. The suggested-improved kriging approach incorporating a direct algorithm is able to maintain memory requirements at a nearly constant level while offering high efficiency of searching for a global optimum. The feasibility and efficiency of this proposed methodology are demonstrated using an example of a classic two-variable analytic function and a new proposed benchmark TEAM multi-objective Pareto optimization problem.
Correlation direct algorithm, hybrid kriging, Kriging surrogate model, multi-objective Pareto optimization
0018-9464
Xiao, Song
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Liu, G. Q.
d7cf4e31-f695-420a-a875-16c1284e4eb8
Zhang, Y. Z.
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Jing, Y. Z.
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Duan, J. H.
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Di Barba, Paolo
e618834b-ff8e-49e0-92b3-07a2cfe363dd
Sykulski, Jan
d6885caf-aaed-4d12-9ef3-46c4c3bbd7fb
Xiao, Song
6ffa9657-513e-4b86-86a2-e560d3c09c72
Liu, G. Q.
d7cf4e31-f695-420a-a875-16c1284e4eb8
Zhang, Y. Z.
f44d5c7b-94ae-42ba-a385-3cc7273d505d
Jing, Y. Z.
767852e8-4a7a-4ac5-9594-941977e5a7fd
Duan, J. H.
9d589bee-9800-4527-8302-ab85c62f016a
Di Barba, Paolo
e618834b-ff8e-49e0-92b3-07a2cfe363dd
Sykulski, Jan
d6885caf-aaed-4d12-9ef3-46c4c3bbd7fb

Xiao, Song, Liu, G. Q., Zhang, Y. Z., Jing, Y. Z., Duan, J. H., Di Barba, Paolo and Sykulski, Jan (2018) Multi-objective Pareto optimization of electromagnetic devices exploiting kriging with lipschitzian optimized expected improvement. IEEE Transactions on Magnetics. (doi:10.1109/TMAG.2017.2771561).

Record type: Article

Abstract

This paper focuses on resolving the storage issue of correlation matrices generated by kriging surrogate models in the context of electromagnetic optimization problems with many design variables and multiple objectives. The suggested-improved kriging approach incorporating a direct algorithm is able to maintain memory requirements at a nearly constant level while offering high efficiency of searching for a global optimum. The feasibility and efficiency of this proposed methodology are demonstrated using an example of a classic two-variable analytic function and a new proposed benchmark TEAM multi-objective Pareto optimization problem.

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IEEE-TMag-Roger-2018 - Accepted Manuscript
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More information

Accepted/In Press date: 29 October 2017
e-pub ahead of print date: 4 January 2018
Keywords: Correlation direct algorithm, hybrid kriging, Kriging surrogate model, multi-objective Pareto optimization

Identifiers

Local EPrints ID: 416848
URI: http://eprints.soton.ac.uk/id/eprint/416848
ISSN: 0018-9464
PURE UUID: 78711721-45ea-40ce-8bcd-28d7ba8da7ce
ORCID for Jan Sykulski: ORCID iD orcid.org/0000-0001-6392-126X

Catalogue record

Date deposited: 11 Jan 2018 17:30
Last modified: 17 Dec 2019 02:04

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Contributors

Author: Song Xiao
Author: G. Q. Liu
Author: Y. Z. Zhang
Author: Y. Z. Jing
Author: J. H. Duan
Author: Paolo Di Barba
Author: Jan Sykulski ORCID iD

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