Abstract:
Abstract:
This research introduces a novel framework that combines the dwarf mongoose optimization (DMO) algorithm with the sine–cosine algorithm (SCA) for the design of real-time linear antenna array (LAA) applications. The DMO algorithm, inspired by swarm behavior found in nature, comprises distinct groups—the alpha group, scouts, and babysitters—each utilizing unique food-capturing strategies. Linear antenna arrays are widely used in next-generation communication applications such as 5G, IoT and beamforming. However, it is challenging to achieve a narrow beamwidth while also suppressing subsidiary lobes. In this study, a novel approach is proposed to address this tradeoff successfully. Through thorough evaluations and comparisons with established strategies, the method demonstrates superior performance. It achieves the lowest subsidiary lobes and the narrowest beamwidth compared to other techniques. Moreover, the proposed framework consistently maintains optimal fitness values and offers highly efficient solutions, surpassing basic and popular optimization approaches. By optimizing excitation amplitudes and positions, the method successfully solves the narrow beamwidth limitation without sacrificing low side lobe levels (SLL). The research demonstrates that the proposed approach, combining DMO and SCA, effectively addresses the beamwidth-SLL tradeoff, making it capable of handling a wide range of LAA application requirements without compromising beamwidth or SLL. Overall, this study presents a significant advancement in LAA design, offering a novel variant of the DMO algorithm incorporating SCA, with improved performance and the potential for real-time linear antenna array applications.
