HYBRID GENETIC ALGORITHM AND PARTICLE SWARM OPTIMIZATION FOR OPTIMAL BEAMFORMING IN SMART ANTENNA SYSTEMS
Abstract
ABSTRACT
This project presents the design and implementation of a hybrid optimization algorithm that combines Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for optimal beamforming in smart antenna systems. Beamforming plays a vital role in enhancing wireless communication by improving signal-to-noise ratio (SNR), mitigating interference, and directing antenna beams toward desired users. Traditional optimization methods often fail in highly dynamic environments due to their limited convergence speed and computational inefficiency. To address these limitations, this study proposes a GA–PSO hybrid algorithm that leverages GA’s global search capability and PSO’s rapid convergence. A simulation model of a smart antenna array was developed using MATLAB, with performance evaluated in terms of beam pattern optimization, convergence rate, and interference suppression. Results demonstrate that the hybrid GA–PSO algorithm outperforms standalone GA and PSO approaches, achieving higher SNR, faster convergence, and better stability. These findings highlight the effectiveness of hybrid metaheuristic algorithms in optimizing smart antenna designs, providing a scalable and robust solution for next-generation wireless networks such as 5G and 6G.
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