A novel artificial intelligence‐based antenna designing model for optimizing <scp>5G</scp> mobile communication

Abstract Antenna designing involves the creation of devices to transmit or receive electromagnetic signals for various applications. It encompasses optimizing parameters such as size, shape, and frequency response to achieve desired performance characteristics, including signal strength, bandwidth, and efficiency, across diverse communication systems and frequencies. In this research, we intend to establish a novel artificial intelligence (AI)‐based antenna designing model for optimizing 5th generation (5G) mobile communication. We proposed Multi‐Objective Zebra Optimization (MOZO) to enhance 5G antenna design, aiming to minimize return loss () and maximize gain ( G ) concurrently. MOZO efficiently explores design space, offering Pareto‐optimal solutions that balance objectives. This approach ensures optimal antenna performance across the desired frequency range, advancing 5G communication efficiency. Our model enhances the functionality of a small, rectangular patch antenna designed for the 5G band, specifically operating at 30 GHz. Additionally, we conducted a comparative analysis between the performance of our optimized rectangular 5G antenna and several recently employed 5G‐millimeter wave (mmWave) antennas. This comparison sheds light on the efficacy and competitiveness of our optimized design within the 5G‐mmWave antenna landscape.