A beamforming study of the linear antenna array using grey wolf optimization algorithm

Abstract

<p><br />The grey wolf optimization (GWO) algorithm is considered an inspired meta-heuristic algorithm, which inspired by the social hierarchy and hunting behavior of the grey wolves. GWO has a high-performance capability of solving constrained, as well as unconstrained optimization problems. In this paper, the beamforming of smart antennas in a code division multiple access system based on the GWO algorithm is investigated. The sidelobe level (SLL) is minimized along with peak sidelobe level reduction, as well as an optimal beam pattern has been accomplished by using GWO to uniform linear antenna arrays. In this work, an amplitude is introduced as constant, while the interspacing distance between antenna array elements and the number of elements in a linear array are variables. The simulation results show that a faster convergence and likely high accurate beamforming are gained using GWO based method. Finally, it is shown that the GWO outperforms the genetic algorithm (GA) based method.</p>

Optimized multibeam synthesis of time-modulated microstrip arrays

“…The grey wolf optimization (GWO) algorithm, another bio-inspired method based on the social hierarchy and hunting behavior of the grey wolves, was implemented to exploit the beamforming capability in linear arrays [21]. Here was shown that the performance of GWO outperforms the GA. A seagull optimization algorithm (SOA) was recently implemented to synthesis linear arrays to obtain radiation patterns with low sidelobe levels with and without zeros [22].…”