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http://yadda.icm.edu.pl:80/baztech/element/bwmeta1.element.baztech-df3c5b7d-aeed-4b39-94bc-673e2f5a59c7

Czasopismo

Archives of Electrical Engineering

Tytuł artykułu

Kriging based robust optimisation algorithm for minimax problems in electromagnetics

Autorzy Li, Y.  Rotaru, M.  Sykulski, J. K. 
Treść / Zawartość
Warianty tytułu
Języki publikacji EN
Abstrakty
EN The paper discusses some of the recent advances in kriging based worst-case design optimisation and proposes a new two-stage approach to solve practical problems. The efficiency of the infill points allocation is improved significantly by adding an extra layer of optimisation enhanced by a validation process.
Słowa kluczowe
EN worst-case optimisation   minimax problems   kriging   robust design  
Wydawca Polish Academy of Sciences, Electrical Engineering Committee
Czasopismo Archives of Electrical Engineering
Rocznik 2016
Tom Vol. 65, nr 4
Strony 843--854
Opis fizyczny Bibliogr. 15 poz., rys., tab., wz.
Twórcy
autor Li, Y.
  • Electronics and Computer Science University of Southampton Southampton, United Kingdom
autor Rotaru, M.
  • Electronics and Computer Science University of Southampton Southampton, United Kingdom
autor Sykulski, J. K.
  • Electronics and Computer Science University of Southampton Southampton, United Kingdom, jks@soton.ac.uk
Bibliografia
[1] Taguchi G., Introduction to Quality Engineering, American Supplier Institute (1989).
[2] Beyer H. G. Sendhoff B., Robust optimisation – A comprehensive survey, Comput. Methods Appl. Mech. Engrg., vol.196, pp. 3190-3218 (2007).
[3] Lee K-H., Kang D-H., A Robust Optimization Using the Statistics Based on Kriging Metamodel, Journal of Mechanical Science and Technology (KSME Int. J.), vol. 20, no. 8, pp. 1169-1182 (2006).
[4] Marzat J., Walter E., Piet-Lahanier H., Worst-case global optimisation of black-box functions through Kriging and relaxation, Journal of Global Optimisation, Springer Verlag, vol. 55, no. 4, pp. 707-727 (2013).
[5] Rehman S., Langelaar M., van Keulen F., Efficient Kriging-based robust optimisation of unconstrained problems, Journal of Computational Science, volume 5, Issue 6, pp. 872-881 (2014).
[6] http://www.compumag.org/jsite/team.html, accessed October (2016).
[7] Alotto P. G., Baumgartner U., Freschi F. et al., SMES Optimisation Benchmark: TEAM Workshop Problem 22, Graz, Austria (2008).
[8] Takahashi N., Ebihara K., Yoshida K. et al., Investigation of simulated annealing method and its application to optimal design of die mold for orientation of magnetic powder, IEEE Trans. Magn., vol. 32, no. 3, pp. 1210-1213 (1996).
[9] Holland J. H., Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, Cambridge, MA, USA: MIT Press (1975).
[10] Kirkpatrick S., Gelatt C. D., Jr., Vecchi M. P., Optimisation by simulated annealing, Science, vol. 220, no. 4598, pp. 671-680 (1983).
[11] Hu N., Tabu search method with random moves for globally optimal design, Int. J. Num. Methods Eng., vol. 35, no. 5, pp. 1055-1070 (1992).
[12] Santner T. J., Williams B. J., Notz W. I., The Design and Analysis of Computer Experiments, New York, USA, Springer-Verlag (2003).
[13] Hajji O., Brisset S., Brochet P., A new Tabu search method for optimisation with continuous parameters, IEEE Trans. Magn., vol. 40, no. 2, pp. 1184-1187, Mar (2004).
[14] Xiao S., Rotaru M., Sykulski J. K., Six Sigma Quality Approach to Robust Optimisation, IEEE Trans. Magn., vol. 51, no. 3 (2015).
[15] Li Y., Xiao S., Rotaru M., Sykulski J. K., A Dual Kriging Approach with Improved Points Selection Algorithm for Memory Efficient Surrogate Optimisation in Electromagnetics, IEEE Transactions on Magnetics, vol. 52, no. 3, pp. 1-4 (2015).
Uwagi
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Kolekcja BazTech
Identyfikator YADDA bwmeta1.element.baztech-df3c5b7d-aeed-4b39-94bc-673e2f5a59c7
Identyfikatory
DOI 10.1515/aee-2016-0059