Nature-Inspired Multi-Objective Reconfiguration of Antennas: Harnessing the GWO, FFO, and ALO for Adaptive Multiple Application Performance

Abstract

In the evolving landscape of wireless communication, there is an ever-growing need for versatile and efficient antenna systems that can cater to diverse applications. The importance of such systems transcends academia, holding profound societal implications, as they form the backbone of modern communication, IoT infrastructure, and numerous other emerging technologies. Existing methodologies for antenna reconfiguration often suffer from limited adaptability, suboptimal performance, and high-power consumptions. This paper introduces a novel, multi-objective reconfiguration approach for antennas, harnessing the prowess of three nature-inspired optimization algorithms: the Grey Wolf Optimizer (GWO), Firefly Optimizer (FFO), and Ant Lion Optimizer (ALO), each targeting specific antenna parameters - Frequency Range, Polarization, and Beamwidth levels. Our proposed model overcomes the challenges posed by conventional techniques, providing a harmonized optimization process that tailors antenna performance to specific applications dynamically. Preliminary results, when benchmarked against current methods, are promising for different scenarios. We report an 8.5% improvement in gain, a reduction of 2.9% in power consumption, a 3.5% enhancement in bandwidth, and a 2.5% betterment in polarization performance levels. These advances not only pave the way for more efficient and adaptable antenna systems but also underscore the potential of integrating multiple nature-inspired optimization techniques in antenna design and other realms of wireless communications.