@inproceedings{SisLab3252,
       booktitle = {2018 IEEE 18th Biennial Conference on Electromagnetic Field Computations (CEFC2018)},
           month = {October},
           title = {Worst Case Scenario Robust Optimization Utilizing Adaptive Dynamic Taylor Kriging and Differential Evolution Algorithm},
          author = {Bin Xia and Minh Trien Pham and Ziyan Ren Ren and Yanli Zhang and Chang Seop Koh},
            year = {2018},
             url = {https://eprints.uet.vnu.edu.vn/eprints/id/eprint/3252/},
        abstract = {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/{\ensuremath{\lambda}}-best/1/bin, is adopted to search for the global robust optimal solution.}
}