Abstract—The optimization of a 2.4 GHz microstrip patch antenna is presented through a comparative investigation of Particle Swarm Optimization (PSO) and Gray Wolf Optimizer (GWO). These metaheuristic approaches are applied to determine the antenna design parameters, including patch length, patch width, dielectric constant and feed position, for an operating frequency of 2.4 GHz. Two efficient nature-inspired metaheuristic algorithms, PSO and GWO, are adopted for the microstrip patch antenna optimization. Implementing such optimization techniques is critical to improve the radiation efficiency of antenna systems, especially for 5G-related applications such as Wireless Local Area Networks (WLANs) and the Internet of Things (IoT). For these scenarios, reliability and operational efficiency are key considerations in the design and optimization of microstrip antennas. The main aim of this work is to systematically improve critical radiation-related performance metrics, namely return loss (S₁₁), bandwidth, gain and Voltage Standing Wave Ratio (VSWR), by tuning design variables including patch dimensions and feed location. Optimization codes for PSO and GWO are developed on the MATLAB platform to facilitate comprehensive computational analysis. Extensive simulation studies are carried out to verify the effectiveness of each optimization algorithm in achieving target antenna performance. Simulation results and convergence curves of PSO and GWO demonstrate that GWO outperforms PSO in multiple radiation-related indicators. Specifically, GWO achieves a faster convergence rate, exhibits stronger optimization capability, and yields higher-quality final antenna designs.
 
Keywords—microstrip patch antenna, length extension, particle swarm optimization, gray wolf optimizer, return loss, Voltage Standing Wave Ratio (VSWR), gain, bandwidth, metaheuristics, convergence, antenna optimization