## 1 Introduction
Furthermore, the design of circular arrays by comparing the performance of PSO, GAs, and DE has been presented in [28]. Finally, the optimization problem of patch antenna design has been extensively addressed in the literature by utilizing various evolutionary algorithms (EAs), i.e., GAs [29, 30], PSO [31], DE [15, 32], and Teaching-Learning Based Optimization (TLBO) [33]. Swarm intelligence (SI) algorithms belong to one out of the three categories of meta-heuristics. The other two are evolutionary [34] or physics-based algorithms [35]. Most of them emulate the social processes of various swarms or herds of creatures in nature [36, 37]. There is an extensive list of SI algorithms in the literature. The most known SI algorithms are the PSO [38], the Ant Colony Optimization (ACO) [39], the Firefly Algorithm (FA) [40], the Artificial Bee Colony (ABC) [41], the Bat-Inspired Algorithm [42], the Particle Fish Swarm Algorithm (PFSA) [43], the Monkey Search Algorithm (MS) [44], the Cuckoo Search Algorithm (CS) [45], and the Krill Herd Algorithm (KH) [46]. SI systems usually consist of a population of members that interact cooperatively with one another and with their natural environment. Generally speaking, SI algorithms exhibit some advantages compared to other meta-heuristics including maintaining information on the search space during the iterative process and having a smaller number of control parameters to adjust. In this work, three emerging algorithms, as representative examples of SI algorithms, namely the Grey Wolf Optimizer (GWO) [36], the While Optimization Algorithm (WOA) [35], and the Salp Swarm Algorithm (SSA) [37] are studied and evaluated in terms of their effectiveness and performance. To this end, well-known benchmark functions are utilized to perform fair comparisons. Starting from the outcomes of such a comparative analysis, such emerging SI techniques are applied to the design of a linear antenna array and an aperture-coupled E-shaped patch antenna for 5G user equipment devices. The objectives of this work are (a) to give a detailed description of the three emerging algorithms in a similar and user-friendly language, (b) to perform a comparative analysis of such SI algorithms in various antenna design problems, (c) to apply the best performed SI algorithm in the design of a dual-band aperture-coupled patch antenna operating in the millimeter-wave frequency bands of 5G communication systems, and (d) to demonstrate a complete design framework for a dual-band aperture-coupled 5G patch antenna base on an emerging SI algorithm. The main motivation of this work originates from the problem complexity of antenna design in 5G and millimeter-wave frequency bands. Such a design, in several or even most cases, cannot be addressed analytically. Therefore, this problem in electromagnetics should be recast as an optimization one. The use of emerging SI algorithms, such as the selected ones, could be beneficial to this type of optimization problem since they usually lack any control parameters. As a result, feasible solutions to the optimization problem can be obtained in a more efficacious way. The contribution of this work is summarized as follows. Firstly, a detailed description of the three emerging SI algorithms, namely the GWO, the WOA, and the SSA by applying a unified design framework is introduced. Secondly, a comparative performance between the three emerging SI algorithms is carried out focusing both on numerical benchmark functions and antenna design problems. Thirdly, a dual-band aperture-coupled patch antenna operating in the 5G millimeter-wave frequency bands is designed and optimized by utilizing the WOA algorithm. To the best of the authors' knowledge, this is the first time that the WOA algorithm has been applied to address the optimization problem of designing a dual-band aperture-coupled 5G millimeter-wave patch antenna. The remainder of the paper is organized as follows. In Section 2, GWO, WOA, and SSA are described, whereas in Section 3 the related state-of-the-art literature is briefly reviewed.