Versatile unsupervised design of antennas using flexible parameterization and computational intelligence methods

Developing contemporary antennas is a challenging endeavor that requires considerable engineering insight. The most laborious stage is to devise an antenna architecture that delivers the required functionalities, e.g., multiband operation. Iterative by nature (hands-on topology modifications, parametric studies, trial-and-error geometry selection), it typically takes many weeks and requires considerable engagement from a human expert. Consequently, only a few possible design options concerning the fundamental antenna geometry may be considered. Automated topology rendition and geometry parameter optimization are highly relevant, especially from the industrial perspective. Therein, reducing time-to-market and limiting the involvement of trained experts is critical. This research proposes an innovative procedure for unsupervised development of planar antennas. Our method leverages flexible antenna parameterization based on re-sizable elliptical patches. It permits the realization of a massive number of geometries of diverse shapes and complexities using a small number of decision variables. Computational intelligence methods are employed to conduct antenna evolution exclusively based on specifications and possible constraints (e.g., maximum size). Fine-tuning of the structure geometry is achieved through low-cost local search routines. Our methodology is demonstrated by designing several antennas featuring distinct characteristics (broadband, single-, dual- and triple-band). The obtained results, supported by experimental data, underscore the presented approach's versatility and capability to render unconventional topologies at reasonably low computational expenses. As mentioned earlier, the design process is fully automated without human expert involvement.