eprintid: 3252
rev_number: 9
eprint_status: archive
userid: 11
dir: disk0/00/00/32/52
datestamp: 2018-12-13 07:49:10
lastmod: 2018-12-13 07:49:10
status_changed: 2018-12-13 07:49:10
type: conference_item
metadata_visibility: show
creators_name: Xia, Bin
creators_name: Pham, Minh Trien
creators_name: Ren, Ziyan Ren
creators_name: Zhang, Yanli
creators_name: Koh, Chang Seop
creators_id: tiandixiabin@163.com
creators_id: trienpm@vnu.edu.vn
creators_id: kohcs@cbnu.ac.kr
title: Worst Case Scenario Robust Optimization Utilizing Adaptive Dynamic Taylor Kriging and Differential Evolution Algorithm
ispublished: pub
subjects: ECE
divisions: fac_fet
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/λ-best/1/bin, is adopted to search for the global robust optimal solution.
date: 2018-10-28
date_type: published
official_url: http://www.cefc2018.org/system/Web/ueditor/asp/upload/file/20181008/15389866299155943.pdf
full_text_status: public
pres_type: paper
event_title: 2018 IEEE 18th Biennial Conference on Electromagnetic Field Computations (CEFC2018)
event_location: Hangzhou, China
event_dates: 28-31 October 2018
event_type: conference
refereed: TRUE
citation:   Xia, Bin and Pham, Minh Trien and Ren, Ziyan Ren and Zhang, Yanli and Koh, Chang Seop  (2018) Worst Case Scenario Robust Optimization Utilizing Adaptive Dynamic Taylor Kriging and Differential Evolution Algorithm.  In: 2018 IEEE 18th Biennial Conference on Electromagnetic Field Computations (CEFC2018), 28-31 October 2018, Hangzhou, China.     
document_url: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/3252/1/3-WC-ADTK_final.pdf