Thinned Eisenstein Fractal Antenna Array Using Multi-Objective Optimization for Wideband Performance

Abstract

This paper introduces a novel framework for designing wideband antenna arrays using self-similar Eisenstein fractal geometries combined with multi-objective evolutionary optimization techniques. The approach employs multi-objective binary differential evolution (MO-BDE) for array thinning and multi-objective particle swarm optimization (MO-PSO) for optimizing amplitude excitations. This integrated methodology reduces the number of active elements while enhancing overall array performance. The optimization process targets minimizing peak side lobe levels and maximizing directivity over a broad frequency range. Two designs are explored: one optimized at a primary frequency, and another providing consistent wideband behavior. The proposed method achieves a 37.5% reduction in active elements. Design A shows an SLL reduction of −12 dB at the target frequency, while Design B maintains up to −3 dB SLL improvement across the bandwidth. The results confirm the efficacy of the proposed synthesis method for developing scalable, energy-efficient antenna arrays for next-generation systems.