A review of multiobjective digital filter design using optimization algorithms

FIR filters support DSP systems because they offer stability plus linear phase response while remaining easily adaptable. Creating FIR filters requires juggling multiple design goals, including passband ripple, stopband ripple, transition width, and hardware complexity limitations. Windowing and frequency sampling techniques, as traditional design methods, struggle to reach peak performance in demanding precise signal processing situations. Different optimization methods have emerged to tackle these challenges, including PSO (Particle swarm optimization), SSO (SALP Swarm optimization), GA (Genetic algorithms), and LAPO (Lightning attachment procedure optimization). The methods help optimize filter settings while lowering the calculation burden and device needs. This review evaluates various optimization algorithms for the design of FIR filters, highlighting their strengths and weaknesses. Based on simulation outcomes, LAPO and MOABC outperform conventional methods by narrowing transition widths and minimizing passband and stopband errors. FIR filters based on LAPO exhibit significant potential for real-time applications, achieving a passband ripple of 0.1263 and a stopband ripple of 0.0149. This research delves into implementing these algorithms using Field Programmable Gate Arrays and details how to address their performance requirements without incurring significant losses in processing speed. The final analysis offers optimized strategies for medical signal analysis, image processing, and real-time data systems.