Efficient Multiobjective Antenna Optimization with Tolerance Analysis Through the Use of Surrogate Models

John A. Easum, Jogender Nagar, Pingjuan Li Werner, Douglas Henry Werner

Research output: Contribution to journalArticle

10 Scopus citations

Abstract

An efficient, black-box multiobjective optimization technique is presented, which is capable of simultaneously optimizing designs for performance as well as robustness when input tolerance values are not known a priori. During the optimization process, adaptive statistical surrogate mappings between input variables and output objectives are formulated within a model selection framework. These statistical models can be evaluated in fractions of a second and serve as an efficient surrogate for a more computationally intensive process, such as an electromagnetic simulation. By exploiting the speed offered from surrogate modeling techniques, new, high-performance designs can be quickly identified. In addition, complete tolerance analysis can be conducted within the optimization loop, which provides designers with critical information regarding the robustness of designs. To demonstrate the effectiveness of this approach, it will be applied to the optimization of a capacitively loaded monopole and a wideband Vivaldi antenna.

Original languageEnglish (US)
Article number8466860
Pages (from-to)6706-6715
Number of pages10
JournalIEEE Transactions on Antennas and Propagation
Volume66
Issue number12
DOIs
StatePublished - Dec 1 2018

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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