FIR low pass filter design using Craziness base Particle Swarm Optimization Technique

This paper is a study of linear phase low pass FIR filter design using different particle swarm optimization techniques (PSO). FIR filter design is basically a multi-modal optimization problem. Evolutionary algorithms like particle swarm optimization (PSO) can be used for the design of linear phase FIR low pass (LP) filter. Different improved particle swarm optimizations are proposed to address different velocity vector and particle position updating scopes. The modified inertia weight of PSO enhances the search capability for obtaining the global optimal solution. The proposed modification is to monitor the linearly decreasing weights of particles. In this work we used Craziness based Particle Swarm Optimization algorithm (CRPSO) and checked the optimized output to make a comparative study of the conventional PSO techniques. The simulation result defines the optimization efficacy of the CRPSO algorithm for the solution of the non-linear, multimodal and non-differentiable FIR filter design problems.