# MAOA: A Swift and Effective Optimization Algorithm for Linear Antenna Array Design
## 1 Introduction  
The rapid evolution in the field of communication technology significantly affects the routine behavior of humans in society. The widespread adoption of mobile communication is profoundly shaping modern lifestyles [1]. In this dynamic environment, the importance of efficient antenna systems cannot be overstated, but their design and synthesis remain a complex and challenging task. One of the most sought-after and adopted layouts forthis purpose is the linear antenna array (LAA); in its configuration, the number of antenna elements is placed in a linear fashion [2]. Estimating the parameters of these antenna arrays for obtaining the desired radiation performance is called pattern synthesis. Mostly, the system is required to maintain a low SLL to overcome the interference effect that occurs with adjacent systems working within the same frequency range. A narrow first null beamwidth (FNBW) is also desired in the antenna array system, so that it shows a high level of directivity during its operation [3]. Satisfying both the conditions of low SLL and narrow beamwidth simultaneously is very tedious work, because both aspects are dependent on each other. Improving one aspect may degrade the performance of the other [4]. In many situations, the beamwidth and the system gain are compromised to reduce the SLL [5]. The other requirement from the antenna array is that it should place the deeper nulls in the intended direction, so that the interfering sources are avoided, but a large electromagnetic population hinders these placements to a greater extent [6]. So, the major aspects which are required in an antenna array are that it must have a low side lobe level with constant FNBW and that the nulls are oriented toward the directions from where the interference is coming from. The parameters that affect the radiation performance of an antenna array are the shape of the array, interelement spacing of the elements, current amplitude, and its phase [7]. In an LAA, SLL minimization and placement of nulls in the desired direction are obtained by using two methods [8]. The first is by optimizing the interelement spacing considering uniform current excitation; the second is by fixing the spacing constant and optimizing the amplitude of the excitation current [9]. The optimization strategies are broadly classified into deterministic and stochastic techniques. The deterministic approach requires a good initial guess for its implementation, and its computational time is too high when handling complex optimization problems. Also, this type of approach is highly sensitive and prone to being confined in the local minima [10]. However, stochastic approaches, which include metaheuristic algorithms, are mostly applied to handle complex optimization scenarios. Many developed metaheuristic approaches are influenced by natural phenomena and swarm intelligence [11]. These approaches are well suited for performing pattern synthesis in an antenna array [12]. Some of the evolutionary optimization algorithms applied to handle the electromagnetic problems of an antenna array are as follows: atom search [13; 14], BAT algorithm [15; 16], bacterial foraging [17], CAT swarm [2; 18; 19], dragonfly algorithm (DA) [20], flower pollination [21], grey wolf optimization (GWO) [11; 12; 22], moth flame optimization (MFO) [23], particle swarm optimization (PSO) [4; 24], social group optimization algorithm [25], Taguchi [8; 26], biogeography based optimization (BBO) [27], ant lion optimization (ALO) [28], spider monkey optimization [29], and many more. These smart algorithms are also helpful in optimizing the parameters of other kinds of antenna array configurations, such as concentric [30], conformal [31], and circular [32] antenna arrays. Moreover, in [33], the author explored swarm intelligence algorithms to improve the performance of multiple-input and multiple-output (MIMO) antennas [34; 35]. The arithmetic optimization algorithm (AOA) is a recently proposed metaheuristic algorithm by [PERSON] et al. in 2021 [34].