Kriging based robust optimisation algorithm for minimax problems in electromagnetics

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.

[1]  Jan K. Sykulski,et al.  A Dual Kriging Approach With Improved Points Selection Algorithm for Memory Efficient Surrogate Optimization in Electromagnetics , 2016, IEEE Transactions on Magnetics.

[2]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[3]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[4]  Julien Marzat,et al.  Worst-case global optimization of black-box functions through Kriging and relaxation , 2012, Journal of Global Optimization.

[5]  Thomas J. Santner,et al.  The Design and Analysis of Computer Experiments , 2003, Springer Series in Statistics.

[6]  Jan K. Sykulski,et al.  Six Sigma Quality Approach to Robust Optimization , 2015, IEEE Transactions on Magnetics.

[7]  Fred van Keulen,et al.  Efficient Kriging-based robust optimization of unconstrained problems , 2014, J. Comput. Sci..

[8]  Bernhard Sendhoff,et al.  Robust Optimization - A Comprehensive Survey , 2007 .

[9]  Genichi Taguchi,et al.  Introduction to quality engineering.... , 2014 .

[10]  N. Hu.  Tabu search method with random moves for globally optimal design , 1992 .

[11]  N. Takahashi,et al.  Investigation of simulated annealing method and its application to optimal design of die mold for orientation of magnetic powder , 1996 .

[12]  Dong-Heon Kang,et al.  A robust optimization using the statistics based on kriging metamodel , 2006 .

[13]  S. Brisset,et al.  A new tabu search method for optimization with continuous parameters , 2004, IEEE Transactions on Magnetics.