Optimization of split-ring resonator slots using levy-opposition-enhanced Newton Raphson method for high-gain UWB Vivaldi antenna design.

This paper presents a novel hybrid metaheuristic optimization algorithm, termed Lévy-opposition-enhanced Newton-Raphson-based optimization (NRBO-LO), and its application to the design of a high-gain ultra-wideband (UWB) antipodal Vivaldi antenna. The proposed algorithm enhances the conventional Newton-Raphson-based optimizer by integrating random opposition learning to improve population diversity and Lévy flight-based guided learning to strengthen exploitation, thereby mitigating premature convergence. The effectiveness of NRBO-LO is first rigorously evaluated using a comprehensive set of unimodal, multimodal, and composite benchmark functions, where it demonstrates superior convergence accuracy, robustness, and stability compared to several well-established metaheuristic algorithms. Following algorithmic validation, NRBO-LO is employed within a MATLAB-CST co-simulation framework to optimize the geometrical parameters of split-ring resonator (SRR) slots embedded on both the radiating surface and ground plane of a compact antipodal Vivaldi antenna fabricated on a low-cost FR-4 substrate. The optimized antenna occupies an area of 40 × 40 mm