Synthesis of circular array antennas using memetic algorithms for enhanced beam pattern and reduced side lobes

Advanced communication and radar systems and satellite technology heavily depend on circular array antenna design problems because they affect the radiation pattern features while also reducing SLL and boosting directivity. Traditional optimization approaches including Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) have both benefits and disadvantages because they exhibit slow convergence and local traps while showing diminished beam forming performance. The proposed hybrid memetic algorithm framework consists of GA and Gradient-Based Optimization to achieve effective design parameter optimization of circular antenna arrays while overcoming traditional limitations. The proposed approach fine-tunes element spacing, excitation amplitudes, and phase shifts, achieving a 20 dB reduction in SLL, a 15% improvement in directivity, and a 30% increase in computational efficiency compared to conventional methods. However, comparative performance analysis with GA and PSO shows that the Memetic Algorithm is superior in producing optimized beam patterns in less number of iterations. The effectiveness of the proposed methodology has been validated using MATLAB-based simulations, which indicate that the methodology can be practically used for various applications involving wireless communication networks, radar systems, and satellite communication. Moreover, the scalability of this approach indicates promising future expansions to real-time adaptive optimization using machine learning approaches in antenna design. These findings indicate that the Memetic Algorithm is a robust and computationally efficient solution for circular array antenna synthesis, outperforming existing evolutionary algorithms in terms of radiation pattern optimization, sidelobe suppression, and adaptive beamforming capabilities.