Optimization of time modulated array antennas based on improved gray wolf optimizer

With the widespread application of intelligent optimization algorithms in engineering and scientific fields, the need for their improvement and optimization is increasingly evident. This paper proposes an Improved Gray Wolf Optimization (IGWO) algorithm based on the Gray Wolf Optimization (GWO) method, aiming to enhance global search capability and convergence speed. The proposed IGWO algorithm integrates goodset initialization and the golden sine strategy to enhance global search efficiency and improve local search accuracy. The algorithm is specifically applied to the temporal modulation optimization of antenna arrays, where the switching durations of array elements are modulated to optimize the synthesis of the radiation pattern. The core goal of temporal modulation is to adjust the switching times of individual elements to alter the array’s radiation pattern, achieving effects such as sidelobe suppression, main beam optimization, and reduction of harmonic distortion. Experimental validation using four benchmark functions demonstrates that the IGWO algorithm significantly outperforms traditional algorithms, including the seagull optimization algorithm, hunger games search optimization algorithm, seahorse optimizer, and GWO algorithm, across all test cases. In addition, IGWO is applied to optimize three time-modulation models, particularly those that optimize both connection durations and inter-element spacing simultaneously. Despite higher second harmonic distortion than the differential evolution with wavelet mutation algorithm in some cases, IGWO consistently excels in minimizing fundamental sidelobes, first harmonics, and second harmonics. Experimental results validate the algorithm’s fast convergence and high optimization accuracy in optimizing the radiation pattern of antenna arrays, showcasing its effectiveness and practicality in addressing electromagnetic problems.