Runner-Root Algorithm to Control Sidelobe Level and Null Depths in Linear Antenna Arrays

Nature-inspired metaheuristic algorithms have significantly influenced the optimization problems, and there has been a great success in finding optimum solutions. The main aim of this paper is to introduce one such technique, runner-root algorithm (RRA), to the antenna community. RRA is based on the concept of plant intelligence to solve an optimization problem resembling a scenario where a plant finds the best location for its survival. The main advantage of RRA over other algorithms is the execution of exploration (global search) and exploitation (local search) phases independently with a built-in mechanism of reinitialization to overcome trapping in local minima. Hence, attempt has been made for the first time to apply this algorithm to optimize excitation current amplitudes and positions of antenna elements for linear array configurations with the objective of suppressing the sidelobe levels and/or placing nulls at prescribed locations referring to the radiation patterns. Different case studies have illustrated that RRA is capable of performing linear antenna array optimization with promising results in comparison with other well-established metaheuristic algorithms. And hence such demonstration has identified RRA as a potential candidate in the optimization domain.