## 3 Related Work  
The GWO [36] has been successfully applied to various optimization problems in antenna design. A noticeable work effort has been published in the design of antenna arrays (linear and planar). [PERSON] and [PERSON] [47] applied the GWO algorithm to the design of linear antenna arrays. Their objective was to obtain an optimal pattern synthesis by optimizing the antenna elements position in a uniform excitation and by optimizing the current distribution of antenna elements assuming uniform array spacing and phase. The pattern synthesis of antenna arrays was investigated in [48]. The authors introduced a dynamic cooperative mechanism of weight factors to update the positions of the three wolves (\(\alpha\), \(\beta\), and \(\delta\)) in the GWO algorithm. Their objective was to improve the convergence speed of the original GWO to obtain feasible solutions in the pattern synthesis of linear antenna arrays. Numerical results demonstrated an improvement of the peak SLL (sidelobe level) and the deep null steering. The authors in [49] designed and optimized a thinned concentric circular antenna array by using the GWO. Their objective was to achieve patterns with more directive characteristics and with more accurate control ofsidelobe levels. Simulated results showed satisfactory values of null depths and peak SLL. [PERSON] et al. [50] utilized a hybrid GWO-IPA (Grey Wolf Optimizer [36]-Interior Point Algorithm [51]) algorithm to mitigate the resulting errors in the radiation power pattern of an antenna array. Simulated results demonstrated that the proposed fitness function corrects the pattern distortion in terms of sidelobe level and null depth level (NDL) as well as in null limitation. In [52], the authors investigated the design of a 100-element linear and a 100-element planar array by employing the GWO algorithm and other well-known algorithms (Genetic Algorithm (GA), Particle Swarm Optimization (PSO) [38], and Imperialist Competitive (IC) [53]). Their objective was to obtain an optimal solution when applying the array thinning method. Numerical results showed that the GWO algorithm outperformed the other algorithms in terms of minimizing the SLL for both linear and planar arrays. [PERSON] et al. [54] applied the GWO to re-adjust the amplitude and the phase of a linear antenna array when a failure or damage occurs to the desired pattern. To evaluate the durability of their proposed technique, different types of failures or damages were investigated. Their results proved the applicability and the effectiveness of the utilized algorithm in mitigating the failures of linear antenna arrays. In [55], the authors initially utilized the multi-objective GWO to address the problem of maximizing the transmission efficiency in wireless power transfer (WPT) systems. However, they verified the efficacy of their proposed technique on array antennas. A noteworthy effort has been published utilizing the GWO algorithm in the design of compact antennas for next-generation communication systems. [PERSON] and [PERSON] [56] investigated the synthesis of linear arrays by the use of the GWO algorithm. They extend their study in the presented algorithm, by optimizing the design of a dual-band E-shaped antenna and a wideband magneto-electric dipole antenna. Their findings indicated a satisfactory performance of the GWO compared to other well-known meta-heuristics (GA, PSO, Differential Evolution [15; 57]). A patch antenna design operating in frequencies above 50 GHz was presented in [58]. The authors optimized the parameters (patch length, patch width, substrate thickness, and dielectric constant \(\epsilon_{r}\)) of a patch antenna by applying the opposition-based GWO algorithm. Numerical results demonstrated satisfactory performance of the obtained antenna in terms of gain, radiation pattern, characteristic impedance, directivity, and efficiency. [PERSON] et al. [59] applied the GWO algorithm to the design of a low-cost dual-band E-shaped patch antenna suitable for radio-frequency (RF) energy-harvesting (RH) applications.