Optimizing Linear Antenna Arrays with Genetic Algorithms

This paper investigates the application of genetic algorithms (GAs) for optimizing the design of linear antenna arrays, with a primary focus on minimizing sidelobe levels (SLL) to achieve a target SLL of -20 dB. The study demonstrates the effectiveness of GAs in antenna engineering by detailing the methodology used to find excitation coefficients. Through rigorous experimentation and analysis, optimal coefficients were determined that significantly improved antenna radiation patterns compared to traditional Chebyshev designs. The results underscore the capability of GAs to enhance antenna performance by narrowing main beam widths and reducing sidelobes, crucial for improving communication quality and reducing interference in dense signal environments. Comparison with existing methods, such as Chebyshev design, highlights the superiority of GAs in terms of accuracy and computational efficiency. This study not only validates the robustness of GAs in antenna optimization but also paves the way for future advancements in wireless communications and radar technologies.