Nature-inspired Algorithm-based Optimization for Beamforming of Linear Antenna Array System

Abstract

Nature-inspired algorithms have brought great revolution in all fields of electromagnetics where the optimization of certain parameters is highly complex and nonlinear. With the help of proper design of the cost function or the fitness function in terms of optimizing parameters, any type of problem can be solved. The nature-inspired algorithms play an important role in the optimal design of antenna array with better radiation characteristics. In this work, hyper-beamforming of linear antenna array has been taken as an example of nature- inspired optimization in antenna array system. An emerging nature-inspired optimization technique has been applied to design the optimal array to reduce the side lobes and to improve the other radiation characteristics to show the effect of the optimization on design via the nature-inspired algorithms. Various nature-inspired algorithms have been considered for the optimization. Flower pollination algorithm (FPA) is applied to determine the optimal amplitude coefficients and the spacing between the elements of the array of the optimized hyper-beamforming of linear antenna array. FPA keeps the best solution until it reaches the end of the iteration. The results obtained by the FPA algorithm have been compared with those of other stochastic algorithms, such as real-coded genetic algorithm (RGA), particle swarm optimization (PSO), differential evolution (DE), firefly algorithm (FFA), simulated annealing (SA), artificial immune system (AIS), and artificial bee colony (ABC). Optimal hyper-beamforming of the same obtained by FPA can obtain the best improvement in side lobe level (SLL) with fixed first null beam width (FNBW). Directivity of the array is calculated by using Simpsons 1/3 rule. The entire simulation has been done for 10-, 14-, and 20-element linear antenna arrays.