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Top1. Introduction
Antenna arrays are commonly used in communication systems and are preferred over single-antenna systems when there is a need to improve the gain, directivity, have electronic beam steering at different directions, cancel interference and to have diversity (MIMO). The performance of a communication system is dependent also on the antenna system it has and hence a proper and efficacious antenna design is an important task. An antenna array’s radiation pattern has a main lobe directed towards the user and side lobes and nulls in the other directions. For efficient operation, the radiation pattern should have high directivity and low side lobe level (SLL). However, these two are conflicting features of an antenna pattern and one cannot be improved without degrading the other. Since side lobes cause wastage of power and can create interference with neighbouring systems it becomes necessary to suppress it and therefore the low SLL criterion is usually fulfilled at the cost of sacrificing gain and beamwidth. The array structure, the inter-element distance, individual amplitude and phase excitations, the number of excited (ON) elements, etc. affect the radiation pattern.
Literature shows that various researchers have applied different types of evolutionary algorithms to antenna problems to achieve certain synthesis goals. Few among them are Moth Flame Optimization (Das, Mandal, Ghoshal & Kar, 2018), comprehensive learning particle swarm optimizer (Ismaiel et al., 2018), Cuckoo search algorithm (Nora, Oudira & Dumond, 2019), probability-based coevolving particle swarm optimization (Pan, Wang, Guo & Wu, 2019), improved fruit-fly optimization algorithm (Darvish & Ebrahimzadeh, 2018), genetic algorithm (GA) (Cen, Yu, Ser & Cen, 2012; Chen, He & Han, 2006), differential evolution (DE) (Lin, Qing & Feng, 2010; Zhang, Jia, & Yao, 2013), particle swarm optimization (PSO) (Bhattacharya, Bhattacharyya & Garg, 2012; Khodier & Al-Aqeel, 2009), ant colony optimization (ACO) (Rajo-lglesias & Quevedo-Teruel, 2007), invasive weed optimization (IWO) (Karimkashi & Kishk, 2010), bacteria foraging algorithm (BFA) (Guney & Basbug, 2008), firefly algorithm (FA) (Zaman & Abdul, 2012), biogeography based optimization (BBO) (Singh, 2010), ant lion optimization (ALO) (Saxena & Kothari, 2016), cat swarm optimization (CSO) (Pappula & Ghosh, 2014; Pappula & Ghosh, 2017),etc. All these algorithms try to suppress the SLL or achieve a target SLL by optimizing the inter-element spacing, position of the antenna elements, weights of amplitudes and phases of complex feeding currents, ON-OFF element combinations (thinning), time-modulated pulse width for time-modulated arrays, or placing nulls in the desired interference directions. Few more recent optimization works on antenna arrays are mentioned in section 3.