Scalability of surrogate-assisted multi-objective optimization of antenna structures exploiting variable-fidelity electromagnetic simulation models

Abstract

AbstractMulti-objective optimization of antenna structures is a challenging task owing to the high computational cost of evaluating the design objectives as well as the large number of adjustable parameters. Design speed-up can be achieved by means of surrogate-based optimization techniques. In particular, a combination of variable-fidelity electromagnetic (EM) simulations, design space reduction techniques, response surface approximation models and design refinement methods permits identification of the Pareto-optimal set of designs within a reasonable timeframe. Here, a study concerning the scalability of surrogate-assisted multi-objective antenna design is carried out based on a set of benchmark problems, with the dimensionality of the design space ranging from six to 24 and a CPU cost of the EM antenna model from 10 to 20 min per simulation. Numerical results indicate that the computational overhead of the design process increases more or less quadratically with the number of adjustable geometric parameters of the antenna structure at hand, which is a promising result from the point of view of handling even more complex problems.Keywords: computer-aided design (CAD)antenna designmulti-objective optimizationelectromagnetic simulationsurrogate modellingevolutionary algorithms AcknowledgementThe authors would like to thank Computer Simulation Technology AG, Darmstadt, Germany, for making CST Microwave Studio available.Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationFundingThis work has been partially supported by the Icelandic Centre for Research (RANNIS) [grant 141272051] and by the National Science Center, Poland [grants 2013/11/B/ST7/04325 and 2014/12/T/ST7/00045].