1. Airports Authority of India, New Delhi, India
2. IBMM Research, Khartoum, Sudan
Email:
skhalid@ibmmacl.org (S.K.)
*Corresponding author
Manuscript received May 15, 2025; accepted August 11, 2025; published August 19, 2025.
Abstract—The combination of Machine Learning (ML) and Artificial Intelligence (AI) has radically transformed the way antennas are designed, as well as how they are optimized. The comprehensive evaluation further conveys their critical role, pertinence, and the purpose of study in advancing this field. It starts by reviewing the classic antenna design methods, based on analytical methods and empirical expressions, and then examines how AI and ML complement these traditional methods. Several optimization methods are analyzed, such as genetic algorithms, neural networks, particle swarm optimization, and reinforcement learning. The methods have a major role to play in achieving effective design exploration, improving bandwidth performance of up to 40% in planar arrays, and resulting in the minimization of computational requirements of up to 90% when compared to the conventional techniques. The review encompasses particular case studies that demonstrate these enhancements, discusses their combination with the electromagnetic simulation program, and analyzes the effectiveness of the various AI/ML techniques regarding precision, extensiveness, and versatility. Lastly, the paper covers some current challenges; the problem of reliability of AI models within operating radio frequency conditions or the issues of generalization in conjunction with a variety of frequency bands, to name but a few, as the fundamental areas that future research requires. It is the aim of this multidisciplinary overview to take the performance and practical application of AI-guided antenna designs to the next level.
Keywords—antenna design, artificial intelligence, optimization techniques, genetic algorithms, neural networks, electromagnetic simulation, antenna array synthesis, smart antennas, surrogate modelling, reinforcement learning, metamaterials
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Cite: Saifullah Khalid, "Artificial-Intelligence-Driven Antenna Design and Optimization: A Comprehensive Review," International Journal of Modeling and Optimization, vol. 15, no. 2, pp. 39-53, 2025.
Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
