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Six sigma quality approach to robust optimization

Six sigma quality approach to robust optimization
Six sigma quality approach to robust optimization
In electromagnetic design, uncertainties in design variables are inevitable, thus in addition to pursuing the theoretical optimum of the objective function the evaluation of robustness of the optimum solution is also critical. Several methodologies exist to tackle robust optimization, such as worst case optimization and gradient index; this paper investigates the use of standard deviation and mean value of objective function under uncertainty of variables. A modified Kriging model with the ability of balancing exploration and exploitation is employed to facilitate the objective function prediction. Two TEAM benchmark problems are solved using different methodologies to compare the advantages and disadvantages of different robust optimization approaches.
Gradient index (GI), kriging, six sigma quality (SSQ) approach, worst case optimization (WCO)
0018-9464
1-4
Xiao, S.
f652a7f1-158f-4d39-b3dd-c8434c38d313
Li, Y.
035e8693-c6e6-4a91-8e10-9232fd0c3112
Rotaru, M.
c53c5038-2fed-4ace-8fad-9f95d4c95b7e
Sykulski, J.K.
d6885caf-aaed-4d12-9ef3-46c4c3bbd7fb
Xiao, S.
f652a7f1-158f-4d39-b3dd-c8434c38d313
Li, Y.
035e8693-c6e6-4a91-8e10-9232fd0c3112
Rotaru, M.
c53c5038-2fed-4ace-8fad-9f95d4c95b7e
Sykulski, J.K.
d6885caf-aaed-4d12-9ef3-46c4c3bbd7fb

Xiao, S., Li, Y., Rotaru, M. and Sykulski, J.K. (2015) Six sigma quality approach to robust optimization. IEEE Transactions on Magnetics, 51 (3), 1-4. (doi:10.1109/TMAG.2014.2360435).

Record type: Article

Abstract

In electromagnetic design, uncertainties in design variables are inevitable, thus in addition to pursuing the theoretical optimum of the objective function the evaluation of robustness of the optimum solution is also critical. Several methodologies exist to tackle robust optimization, such as worst case optimization and gradient index; this paper investigates the use of standard deviation and mean value of objective function under uncertainty of variables. A modified Kriging model with the ability of balancing exploration and exploitation is employed to facilitate the objective function prediction. Two TEAM benchmark problems are solved using different methodologies to compare the advantages and disadvantages of different robust optimization approaches.

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Accepted/In Press date: September 2014
Published date: March 2015
Keywords: Gradient index (GI), kriging, six sigma quality (SSQ) approach, worst case optimization (WCO)
Organisations: EEE

Identifiers

Local EPrints ID: 377091
URI: http://eprints.soton.ac.uk/id/eprint/377091
ISSN: 0018-9464
PURE UUID: 9e43db0f-ee88-4087-8b4f-fcbed035a29a
ORCID for J.K. Sykulski: ORCID iD orcid.org/0000-0001-6392-126X

Catalogue record

Date deposited: 15 May 2015 08:46
Last modified: 10 Sep 2019 00:57

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Contributors

Author: S. Xiao
Author: Y. Li
Author: M. Rotaru
Author: J.K. Sykulski ORCID iD

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