Skip to Main Content
FIR filter design involves multi-modal, multiparameter optimization. Different optimization techniques can be utilized to determine the impulse response coefficient of a filter and try to meet the ideal frequency response characteristics. This paper presents an optimal design of linear phase digital band pass finite impulse response (FIR) filter using Novel Particle Swarm Optimization (NPSO) algorithm. NPSO is an improved particle swarm optimization (PSO) that proposes a new definition for the velocity vector and swarm updating and hence the solution quality is improved. The inertia weight has been modified for the PSO to enhance its search capability to obtain the global optimal solution. The key feature of the applied modified inertia weight mechanism is to monitor the weights of particles, which linearly decrease in general applications. In the design process, the filter length, pass band and stop band frequencies, feasible pass band and stop band ripple sizes are specified. Evolutionary algorithms like real code genetic algorithm (RGA), particle swarm optimization (PSO), differential evolution (DE), and the novel particle swarm optimization (NPSO) have been employed for the design of linear phase FIR band pass (BP) filter. A comparison of simulation results reveals the optimization efficacy of the algorithm over the prevailing optimization techniques for the solution of the multimodal, non-differentiable, highly non-linear, and constrained FIR filter design problems.