A Novel Hybrid Grey Wolf–Whale Particle Swarm Optimization Algorithm: A Metaheuristic Optimization of a Terahertz Antenna for Future Communications

A novel hybrid metaheuristic optimization algorithm for antenna design optimization is presented in this paper. The proposed algorithm, called the Hybrid Grey Wolf–Whale Particle Swarm Optimization Algorithm (HGWWOA-PSO), combines the exploration capability of Grey Wolf Optimizer (GWO), the exploitation mechanism of the Whale Optimization Algorithm (WOA), and the adaptive search strategy of Particle Swarm Optimization (PSO) to solve a multi-objective antenna design problem. The proposed hybrid algorithm is evaluated and validated based on its convergence characteristics and distribution of cost function value in comparison with GWO and WOA. The proposed HGWWOA-PSO is used to optimize the reflection coefficient, gain, and radiation efficiency of a terahertz antenna with a footprint of 0.24λo × 0.24λo at 361 GHz. The optimization targets three resonant frequencies (361 GHz, 391.6 GHz, and 611.2 GHz). The optimized antenna has a reflection coefficient of −51.35 dB, −16.7 dB, and −20 dB at 361.0 GHz, 391.6 GHz, and 611.2 GHz, respectively. The resonances at 361.0 GHz and 391.6 GHz combine to form a lower operating band, hence a dual-band antenna. The lower operating band achieves a −10 dB impedance bandwidth of 56.21 GHz (347.34–403.55 GHz), while the upper band impedance bandwidth is 183.21 GHz (524.60–707.81 GHz). The peak gain and radiation efficiency achieved are 4.5 dBi and 95%, respectively, while maintaining an omnidirectional radiation pattern in the E-plane and H-plane across both operating bands. The combination of miniaturization, multiband capability, high gain, and high radiation efficiency makes the proposed antenna an attractive candidate for terahertz communication systems.