Optimizing High-Frequency Antenna Design through Genetic Algorithm-Based Electromagnetic Simulation

The emergence of sixth-generation (6G) wireless networks demands broadband antennas capable of ultra-high data throughput and seamless global connectivity. This study presents a genetic-algorithm (GA) optimization framework to enhance antenna performance, focusing on patch dimensions, ground-plane size, and feed position. Full-wave electromagnetic simulations were performed in CST Microwave Studio and ANSYS HFSS, employing defined mesh sizes, solver types, and boundary conditions to ensure accurate evaluation. The GA-based optimization achieved an impedance bandwidth of 3.2–6.1 GHz, a peak gain improvement of 2.8 dB, and radiation efficiency exceeding 92%, outperforming conventional gradient-based tuning. The optimized antenna exhibited stable S-parameters and an omnidirectional radiation pattern across the target spectrum, confirming reliable operation at high frequencies. This approach highlights the advantages of evolutionary algorithms in enabling efficient, manufacturable, and high-performance broadband antenna designs for next-generation wireless systems. Beyond immediate 6G applications, the methodology can be extended to millimeter-wave and terahertz antennas, supporting continued innovation in ultra-reliable, high-capacity wireless communications.