Optimal Design of Digital Low Pass Finite Impulse Response Filter using Particle Swarm Optimization and Bat Algorithm

In this paper, the traditional metaheuristic Particle Swarm Optmization (PSO) and the Bat Algorithm (BA) are used to optimal design digital low pass (LP) Finite Impulse Response (FIR) filters. These filters have a wide range of applications because of their characteristics. They are easy to be designed, they have guaranteed bounded input-bounded output (BIBO) stability and can be designed to present linear phase at all frequencies. Traditional optimization methods based on gradient are susceptible to getting trapped on a local optima solution when they are applied to optimize multimodal problems, such as the FIR filter design. Here, to overcome this drawback, the aforementioned metaheuristics are adopted to obtain the coefficients of low pass FIR filters of order 20 and 24. The performance of BA and PSO algorithms are compared with the classical Parks and McClellan (PM) filter design algorithm, which is a deterministic procedure. For this comparison is considered the filters pass band and stop band ripples, transition width and statistical data. The simulation results demonstrate that the proposed filter design approach using BA algorithm outperforms PM and PSO.