DocumentCode :
2126674
Title :
Efficient GA-based electromagnetic optimization using HDMR-generated surrogate models
Author :
Yucel, Abdulkadir C. ; Michielssen, Eric
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., Univ. of Michigan, Ann Arbor, MI, USA
fYear :
2012
fDate :
8-14 July 2012
Firstpage :
1
Lastpage :
2
Abstract :
An efficient surrogate modelling technique that supports the genetic algorithm (GA) -based optimization of electromagnetic (EM) devices is presented. The proposed method leverages high dimensional model representation (HDMR) expansions, which approximate observables or objective functions as series of iteratively constructed component functions involving only the most strongly interacting design variables. The contributions that feature in HDMR expansions are approximated via a multi-element probabilistic collocation (ME-PC) method. The proposed method is capable of generating surrogate models of rapidly varying observables or objective functions that involve a large number of design parameters. The efficiency and accuracy of the proposed method are demonstrated via its application to the placement of stacked patch antennas in a linear array.
Keywords :
electromagnetic devices; genetic algorithms; iterative methods; linear antenna arrays; microstrip antenna arrays; EM devices; GA-based electromagnetic optimization; HDMR expansion; HDMR-generated surrogate models; ME-PC method; electromagnetic devices; genetic algorithm; high-dimensional model representation expansion; iteratively-constructed component functions; linear array; multielement probabilistic collocation method; stacked patch antennas; Antennas;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Antennas and Propagation Society International Symposium (APSURSI), 2012 IEEE
Conference_Location :
Chicago, IL
ISSN :
1522-3965
Print_ISBN :
978-1-4673-0461-0
Type :
conf
DOI :
10.1109/APS.2012.6347953
Filename :
6347953
Link To Document :
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