%0 Conference Paper
%A Xia, Bin
%A Pham, Minh Trien
%A Ren, Ziyan Ren
%A Zhang, Yanli
%A Koh, Chang Seop
%B 2018 IEEE 18th Biennial Conference on Electromagnetic Field Computations (CEFC2018)
%C Hangzhou, China
%D 2018
%F SisLab:3252
%T Worst Case Scenario Robust Optimization Utilizing Adaptive Dynamic Taylor Kriging and Differential Evolution Algorithm
%U https://eprints.uet.vnu.edu.vn/eprints/id/eprint/3252/
%X For the robust optimal design of electromagnetic problems under uncertainties, the robustness evaluation is the critical problem. This paper presents a surrogate model based worst case scenario optimization algorithm, where the adaptive dynamic Taylor Kriging is incorporated to construct a higher accurate surrogate model. Finally, an improved differential evo- lution algorithm, DE/λ-best/1/bin, is adopted to search for the global robust optimal solution.