Probability of improvement methods for constrained multi-objective optimization
Probability of improvement methods for constrained multi-objective optimization
This paper shows how the simultaneous consideration of multiple Kriging models can lead to useful metrics for the selection of design vectors in constrained multiobjective optimization. The savings in computational cost with such methods make them particularly useful for optimal electromagnetic design.
Constrained multi-objective optimization, kriging
978-0-86341-891-4
50-51
Hawe, G. I.
0979384f-3c4d-4ba0-aba7-c4203c26e591
Sykulski, J. K.
d6885caf-aaed-4d12-9ef3-46c4c3bbd7fb
7 April 2008
Hawe, G. I.
0979384f-3c4d-4ba0-aba7-c4203c26e591
Sykulski, J. K.
d6885caf-aaed-4d12-9ef3-46c4c3bbd7fb
Hawe, G. I. and Sykulski, J. K.
(2008)
Probability of improvement methods for constrained multi-objective optimization.
The IET 7th International Conference on Computation in Electromagnetics CEM 2008, Old Ship Hotel Brighton, United Kingdom.
07 - 10 Apr 2008.
.
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Conference or Workshop Item
(Paper)
Abstract
This paper shows how the simultaneous consideration of multiple Kriging models can lead to useful metrics for the selection of design vectors in constrained multiobjective optimization. The savings in computational cost with such methods make them particularly useful for optimal electromagnetic design.
Text
CEM2008_GH_JKS_p50.pdf
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More information
Published date: 7 April 2008
Additional Information:
Event Dates: 7 – 10 April 2008
Venue - Dates:
The IET 7th International Conference on Computation in Electromagnetics CEM 2008, Old Ship Hotel Brighton, United Kingdom, 2008-04-07 - 2008-04-10
Keywords:
Constrained multi-objective optimization, kriging
Organisations:
EEE
Identifiers
Local EPrints ID: 265420
URI: http://eprints.soton.ac.uk/id/eprint/265420
ISBN: 978-0-86341-891-4
PURE UUID: bb077c57-314c-455f-82ea-d1b31472ed31
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Date deposited: 11 Apr 2008 09:11
Last modified: 15 Mar 2024 02:34
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Contributors
Author:
G. I. Hawe
Author:
J. K. Sykulski
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