Optimal linear phase FIR filter design using particle swarm optimization with constriction factor and inertia weight approach with wavelet mutation

Abstract

In this paper, Particle Swarm Optimization with Constriction Factor and Inertia Weight Approach is hybridized with Wavelet Mutation (PSOCFIWA-WM) strategy for the optimal design of linear phase FIR filters. Real coded genetic algorithm (RGA), particle swarm optimization (PSO) and particle swarm optimization with constriction factor and inertia weight (PSOCFIWA) have also been adopted for the sake of comparison. PSOCFIWA-WM incorporates a new definition of swarm updating in PSOCFIWA with the help of wavelet based mutation. Wavelet mutation enhances the effectiveness of PSOCFIWA to explore the multidimensional solution space more effectively. In this design approach, filter length, pass band and stop band edge frequencies, feasible pass band and stop band ripple sizes are specified. A comparison of simulation results reveals the optimization superiority of the proposed technique over the other optimization techniques for the solution of FIR low pass (LP), high pass (HP), band pass (BP) and band stop (BS) filter designs.