Optimizing circular antenna arrays with artificial rabbits optimization for beam control and side lobe reduction

Circular antenna arrays (CAA) are substantially utilized for numerous wireless applications such as beamforming, fifth generation (5G) communication, and Internet of Things (IoT) applications however, it is frequently challenging to retain the high directivity or beam width along with maintaining the low side lobe level (SLL). Furthermore, several conventional techniques can be used to synthesize the array for real-time implementation, but they encounter difficulties in retaining the low SLL along with the narrow beam width. An optimization problem is formulated in this research work to achieve the necessary orientation of the main lobe, suppress the SLL, and achieve the requisite beam width. To estimate the controlling parameters quickly and efficiently, a novel nature-inspired artificial rabbits optimization (ARO) is implemented for the 12 and 24 elements of CAA. This study optimizes CAA using ARO to achieve low SLL and narrow First Null Beam Width (FNBW). ARO outperforms algorithms like Spider Wasp Optimizer (SWO), Zebra Optimization Algorithm (ZOA), Osprey Optimization Algorithm (OOA), Kepler optimization algorithm (KOA), Mountain Gazelle Optimizer (MGO), and Artificial Hummingbird Optimization Algorithm (AHOA) respectively in achieving SLL of -32.91 dB for 12 elements and − 29.15 dB for 24 elements, with FNBW of 80° and 38°, respectively. Dynamic controlled variables like amplitude and phase are used for circular antenna array synthesis. Identified the Pareto frontier optimal trade-off between SLL suppression and FNBW. Circular antenna array figure of merits (FOM) evaluated with a metaheuristic approach.