Adaptive Metaheuristic Optimization of Frequency-Selective Multi-band Antennas for Enhanced Electromagnetic Performance

Abstract

With advanced wireless systems gaining market traction, there is a need for small, high-performance antennas. Design strategies must be efficient and accurate so antennas can operate across multiple bands with good gain and impedance matching. This study presents a flexible metaheuristic optimization methodology for designing a multi-band antenna with Frequency Selective Surfaces (FSS). The design process involves the step-by-step evolution of its FSS unit cell, starting from a circular structure followed by an annular ring and an adyne triangular protrusion to enhance resonance characteristics and surface current distribution. The rectangular patch with its rectangular protrusions forms the key radiating component of the antenna at the end. An evolved frequency-selective surface (FSS) cell improves frequency selectivity and gain. The Adaptive Enhanced Diversified Hiking Optimization Algorithm (AEDHOA) achieves a good trade-off between exploration and exploitation, with accelerated convergence and improved optimization accuracy. Simulations and experiments both show that far-field radiation performance improves significantly when FSS elements are added to the radiating surface. At 3.8 GHz, the antenna with FSS under the patch achieves a return loss of −53 dB, a VSWR of 1.3, and a gain of 4.01 dBi. Compared with traditional methods, the AEDHOA reduces computation iterations by 35 % and increases the fitness value by 27 %. This work demonstrates a scalable and computationally efficient design approach for multi-band antennas suitable for next-gen wireless, IoT, and radar applications.