Spiral Random Walk-Enhanced Puma Optimizer for Side Lobe Suppression and Null Control in Array Antenna Design

This study proposes a spiral random walk enhanced puma optimizer (SRW-PO) for array antenna design. The method preserves the hunting and ambush search structure of the classical puma optimizer (PO) while embedding two complementary mechanisms into its update process. The adaptive random walk operator improves population diversity and enables weak candidate solutions to escape unproductive regions. The spiral local search operator strengthens exploitation around the current best solution. This structure provides a balanced search process for highly nonlinear antenna synthesis problems involving side lobe suppression, null control, and excitation dynamic range. SRW-PO is evaluated on representative linear array antenna design cases and further assessed through a focused CEC2022 benchmark comparison with classical PO, GWO, WOA, and GA. The results indicate stronger convergence, more consistent objective cost reduction, and competitive computational efficiency. In the CEC2022 benchmark, SRW-PO achieves the best average rank and ranks first for most benchmark functions. In antenna synthesis experiments, it provides accurate radiation pattern shaping, effective side lobe reduction, and deep null placement. These findings indicate that the embedded spiral and random walk mechanisms improve the search quality and optimization consistency of classical PO for array antenna design.