This work examines the reduced-cost design optimization of dual- and multi-band antennas. The primary challenge is independent yet simultaneous control of the antenna responses at two or more frequency bands. In order to handle this task, a feature-based optimization approach is adopted where the design objectives are formulated on the basis of the coordinates of so-called characteristic points (or response features) of the antenna response. Due to only slightly nonlinear dependence of the feature points on antenna geometry parameters, optimization can be attained at a low computational cost. Our approach is demonstrated using two antenna structures with the optimum designs obtained in just a few dozen of EM simulations of the respective structure.

Re-design of a given antenna structure for various substrates is a practically important issue yet non trivial, particularly for wideband and ultra-wideband antennas. In this work, a technique for expedited redesign of ultra-wideband antennas for various substrates is presented. The proposed approach is based on inverse surrogate modeling with the scaling model constructed for several reference designs that are optimized for selected values of the substrate permittivity. The surrogate is set up at the level of coarse-discretization EM simulation model of the antenna and, subsequently, corrected to provide prediction at the high-fidelity EM model level. The dimensions of the antenna scaled to any substrate permittivity within the region of validity of the surrogate are obtained instantly, without any additional EM simulation necessary. The proposed approach is demonstrated using an ultra-wideband monopole with the permittivity scaling range from 2.2 to 4.5. Numerical validation is supported by physical measurements of the fabricated prototypes of the re-designed antennas.

Design closure, i.e., adjustment of geometry parameters to boost the performance, is a challenging stage of antenna design process. Given complexity of contemporary structures, reliable parameter tuning requires numerical optimization and can be executed using local algorithms. Yet, EM-driven optimization is a computationally expensive endeavour and reducing its cost is highly desirable. In this paper, a modification of the trust-region gradient search algorithm is proposed for accelerated optimization of antenna structures. The algorithm is based on sparse updates of antenna sensitivities involving various methods that include the Broyden formula used for selected parameters, as well as dimensionality- and convergence-dependent acceptance thresholds which enable additional speedup, and make the procedure easy to tune for various numbers of antenna parameters. Comprehensive verification executed for a set of benchmark antennas delivers consistent results and considerable cost reduction of up to 60 percent with respect to the reference algorithm. Experimental validation is also provided.