“Advancements in Microstrip Patch Antenna Design Using Nature-Inspired Metaheuristic Optimization Algorithms: A Systematic Review”

Research on Microstrip Patch Antennas (MPAs) has significantly increased in recent years, due to their compact design, ease of fabrication, and cost-effectiveness. However, certain aspects of MPAs, such as narrow bandwidth, low gain, and suboptimal polarization purity still need improvement. It is crucial to optimize the performance parameters of MPAs, including bandwidth and gain while maintaining a compact form factor. Although traditional optimization techniques have been employed to address these challenges, they often struggle to achieve global optima and effectively manage multiple design variables. To address these limitations, nature-inspired metaheuristic optimization algorithms have emerged as a popular alternative. This comprehensive review examines recent research on applying optimization algorithms in MPA design, discussing their advantages, drawbacks, and effectiveness in addressing MPA design challenges. The review covers widely used algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), Artificial Bee Colony (ABC) optimization, Bacterial Foraging Optimization (BFO), and Ant Colony Optimization (ACO). Additionally, it explores the potential of novel metaheuristic algorithms, including Cuckoo Search (CS), Firefly Algorithm (FA), Grey Wolf Optimization (GWO), Bat Algorithm (BA), and Invasive Weed Optimization (IWO) to enhance MPA performance. This study summarizes the impact of various optimization methods on key performance metrics of MPAs, including bandwidth, return loss, gain, radiation efficiency, and miniaturization. It synthesizes findings from previously published research, emphasizing the growing need for multi-objective and hybrid optimization techniques in MPA design. These optimization techniques facilitate the development of high-performance, compact antenna solutions for a wide range of wireless communication applications while ensuring computational efficiency. Furthermore, the paper suggests several intriguing avenues for future research in MPA optimization.