On Fast Multi-objective Optimization of Antenna Structures Using Pareto Front Triangulation and Inverse Surrogates

Design of contemporary antenna systems is a challenging endeavor, where conceptual developments and initial parametric studies, interleaved with topology evolution, are followed by a meticulous adjustment of the structure dimensions. The latter is necessary to boost the antenna performance as much as possible, and often requires handling several and often conflicting objectives, pertinent to both electrical and field properties of the structure. Unless the designer’s priorities are already established, multi-objective optimization (MO) is the preferred way of yielding the most comprehensive information about the best available design trade-offs. Notwithstanding, MO of antennas has to be carried out at the level of full-wave electromagnetic (EM) simulation models which poses serious difficulties due to high computational costs of the process. Popular mitigation methods include surrogate-assisted procedures; however, rendering reliable metamodels is problematic at higher-dimensional parameter spaces. This paper proposes a simple yet efficient methodology for multi-objective design of antenna structures, which is based on sequential identification of the Pareto-optimal points using inverse surrogates, and triangulation of the already acquired Pareto front representation. The two major benefits of the presented procedure are low computational complexity, and uniformity of the produced Pareto set, as demonstrated using two microstrip structures, a wideband monopole and a planar quasi-Yagi. In both cases, ten-element Pareto sets are generated at the cost of only a few hundreds of EM analyses of the respective devices. At the same time, the savings over the state-of-the-art surrogate-based MO algorithm are as high as seventy percent.