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Recently, there has been increasing interest in integrating artificial intelligence (AI) and machine learning (ML) techniques into the optimization and measurement of antenna designs, driven by their potential to improve both design efficiency and performance. This review examines the latest advancements in applying AI/ML approaches to antenna design optimization. It explores the use of various AI/ML algorithms such as neural networks, decision trees, genetic algorithms, and particle swarm optimization in this context. The paper also highlights the challenges and limitations associated with employing AI/ML in antenna design, optimization, and measurement, while identifying future research opportunities in the field. The review concludes that AI/ML approaches have the capacity to transform antenna design by offering quicker and more precise solutions to complex problems. However, since the field is still in its early stages, continuous research and development are necessary to address these challenges and fully capitalize on the capabilities of AI/ML in optimizing antenna design.
Gajbhiye et al. (Thu,) studied this question.