Rapid Multiobjective Antenna Design Using Point-By-Point Pareto Set Identification and Local Surrogate Models

Abstract

Antenna design is inherently a multicriterial problem. Determination of the best possible tradeoffs between conflicting objectives (a so-called Pareto front), such as reflection response, gain, and antenna size, is indispensable from the designer’s point of view, yet challenging when high-fidelity electromagnetic (EM) simulations are utilized for performance evaluation. Here, a novel and computationally efficient methodology for multiobjective optimization of antenna structures is presented. In our approach, the tradeoff designs are obtained by moving along the Pareto front and identifying the subsequent Pareto-optimal solutions using surrogate-based optimization techniques. Computational efficiency of the process is achieved by employing coarse-discretization EM simulations and local response surface approximation (RSA) models. The proposed approach is demonstrated using a compact ultrawideband (UWB) monopole antenna with a representation of the Pareto front obtained at the cost corresponding to just a few dozen of evaluations of the high-fidelity EM antenna model. Experimental validation is also provided.