FIR band stop filter optimization by improved particle swarm optimization

This paper proposes a novel optimal design of linear phase digital band stop finite impulse response (FIR) filter using Improved Particle Swarm Optimization (IPSO) technique. FIR filter design is a multi-modal optimization problem. An iterative method is introduced to find the optimal solution of FIR filter design problem. Particle Swarm Optimization (PSO) is similar to the Genetic Algorithm (GA) in that it performs a structured randomized search of an unknown parameter space by manipulating a population of parameters to converge to a suitable solution. IPSO is an improved PSO that proposes a new definition for the velocity vector and swarm updating and hence the solution quality is improved. Evolutionary algorithms like real code genetic algorithm (RGA), particle swarm optimization (PSO), improved particle swarm optimization (IPSO) have been used here for the design of linear phase band stop FIR filter. In the design process, the filter length, pass band and stop band frequencies, feasible pass band and stop band ripple sizes are specified. 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 filter design problems.