๐ค AI Summary
This work proposes a two-stage fully automated optimization framework to address the limitations of traditional antenna design, which heavily relies on manual expertise and struggles to efficiently generate topologies meeting target performance specifications. The approach first models the antenna as a pixelated connectivity graph and employs global optimization to produce free-form topologies. Subsequently, a surrogate modelโassisted local search algorithm refines the geometric parameters to precisely satisfy design requirements. Requiring no prior knowledge, the method effectively integrates global exploration with local fine-tuning. Its efficacy and generality are demonstrated through successful automatic generation of high-performance structures in both broadband and dual-band monopole antenna designs.
๐ Abstract
Development of modern antennas is a cognitive process that intertwines experience-driven determination of topology and tuning of its parameters to fulfill the performance specifications. Alternatively, the task can be formulated as an optimization problem so as to reduce reliance of geometry selection on engineering insight. In this work, a bi-stage framework for automatic generation of antennas is considered. The method determines free-form topology through optimization of interconnections between components (so-called pixels) that constitute the radiator. Here, the process involves global optimization of connections between pixels followed by fine-tuning of the resulting topology using a surrogate-assisted local-search algorithm to fulfill the design re-quirements. The approach has been demonstrated based on two case studies concerning development of broadband and dual-band monopole antennas.