AI-Driven Metamaterial Antenna Design: A Review
Keywords:
Antenna optimization; Artificial intelligence; Evolutionary methods; PSADEA; SADEA; SADEA-II; Surrogate model-based optimization.Abstract
The emergence of artificial intelligence (AI) has accelerated the design of microwave devices, including antennas, enhancing throughput and reducing time-to-market. This is mostly due to the fact that design automation via optimization has replaced labor-intensive manual design methods that rely on trial and error without assurance of effective results. Surrogate model-based optimization (SMBO) approaches are leading the way in the quick design of antennas via optimization, mostly owing to their enhancement of efficiency regarding computing costs.
The Surrogate Model Assisted Differential Evolution for Antenna Synthesis (SADEA) algorithm family is a category of cutting-edge Sequential Model-Based Optimization (SMBO) techniques. This study illustrates the use and benefits of the SADEA algorithm family via two case studies of actual antenna design challenges. The antenna design challenges include optimizing a multi-layered small multiple-input multiple-output (MIMO) antenna array for wireless communications and a microwave imaging antenna for ultra-wideband (UWB) body-centric applications. In both instances, the SADEA algorithm family achieved excellent design solutions in a reasonable timeframe, and the quality of these solutions is corroborated by the close alignment between the simulated and measured results of the fabricated, operational prototypes of the antennas. In both instances, the efficacy of the SADEA algorithm family is juxtaposed with the 2019 Computer Simulation Technology - Microwave Studio (CSTMWS) optimizers, namely the trust region framework (TRF) and particle swarm optimization (PSO). Comparative results indicate that the SADEA algorithm family consistently achieves highly satisfactory design solutions across all iterations, utilizing a reasonable optimization duration, whereas the alternative optimizers consistently fail to meet design specifications and/or produce designs with geometric inconsistencies.
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