Accelerating Convergence in LMS Adaptive Filters Using Particle Swarm Optimization: A Hybrid Approach for Real-Time Signal Processing

Adaptive filtering, particularly with Least Mean Square (LMS) algorithms, is foundational in applications such as noise cancellation, system identification, and control systems. Despite their simplicity and effectiveness, traditional LMS algorithms are hindered by slow convergence and numerical instability. This paper introduces a novel hybrid framework that integrates Particle Swarm Optimization (PSO) with advanced LMS variants—including ZA-LLMS, RZA-LLMS, ZA-VSS-LMS, and RZA-VSS-LMS—to address these limitations. By leveraging PSO’s ability to optimize weight coefficients dynamically, the proposed algorithms significantly enhance convergence speed and reduce mean square error (MSE), outperforming traditional methods. Experimental evaluations using Additive White Gaussian Noise (AWGN) and Colored Gaussian Sequence (CGS) noise demonstrate the hybrid framework's robustness, achieving up to 67\% reduction in iterations. This advancement paves the way for real-world applications requiring high-speed adaptive filtering, such as real-time signal processing, telecommunication systems, and medical diagnostics.