Particle Swarm Optimization – Based Digital FIR Filter Design

Abstract

Digital filters are integral to digital signal processing, separating signals contaminated by interference and restoring distorted ones. Finite Impulse Response (FIR) filters, outperforming IIR filters with stable frequency and linear phase response, pose multi-modal optimization challenges. These issues find solutions in optimization methods like Particle Swarm Optimization (PSO) and Improved Cuckoo Search Algorithm, aiming to minimize errors between actual and ideal responses. PSO, a stochastic bio-inspired optimization approach with straightforward implementation and parameter-controlled convergence, excels in multidimensional optimization amidst complex search spaces. This study employs the Kaiser Window Function to design a MATLAB-based digital band-pass FIR filter, optimizing it with PSO. Robustly comparing PSO and Improved Cuckoo Search Algorithm (ICSA), the results highlight the superior performance of the PSO-designed FIR filter across the frequency spectrum.