Performance evaluation of linear antenna array using quasi opposition modified particle swarm algorithm

Linear antenna arrays find extensive application in the communication systems of the future, including IoT, 5 G, and beamforming technologies. However, sustaining subsidiary lobes while keeping a tight beamwidth remains a challenge. In this paper, an enhanced version of Artificial Hummingbird Algorithm (AHOA) is presented. AHOA is a kind of particle swarm algorithm based on the unique flying abilities and clever foraging techniques of hummingbirds seen in nature. In this research, a hybridization of AHOA and quasi opposition based learning is presented for linear antenna array applications. The quasi opposition learning based artificial hummingbird method has been developed to produce more accurate outcomes for further complicated tasks and is named as Quasi Opposition Based Artificial Hummingbird Algorithm. The approach is evaluated across various communication needs of the linear array, and the results are compared with those obtained from other conventional methods. In comparison to the other approach, the proposed strategy delivers the lowest subsidiary lobes along with the narrow beamwidth without any grating lobes. Thus, the proposed approach is capable of managing diverse linear array applications without sacrificing beamwidth or subsidiary lobes levels.