TY  - CONF
ID  - SisLab3252
UR  - http://www.cefc2018.org/system/Web/ueditor/asp/upload/file/20181008/15389866299155943.pdf
A1  - Xia, Bin
A1  - Pham, Minh Trien
A1  - Ren, Ziyan Ren
A1  - Zhang, Yanli
A1  - Koh, Chang Seop
Y1  - 2018/10/28/
N2  - 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/?-best/1/bin, is adopted to search for the global robust optimal solution.
TI  - Worst Case Scenario Robust Optimization Utilizing Adaptive Dynamic Taylor Kriging and Differential Evolution Algorithm
M2  - Hangzhou, China
AV  - public
T2  - 2018 IEEE 18th Biennial Conference on Electromagnetic Field Computations (CEFC2018)
ER  -