Efficient GA-based electromagnetic optimization using HDMR-generated surrogate models

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.