Optimization of linear phase FIR band pass filter using Particle Swarm Optimization with Constriction Factor and Inertia Weight Approach

In this paper, swarm and evolutionary algorithms have been applied for the design of digital filters. Genetic algorithm (GA) and an improved Particle swarm optimization (PSO) called Particle Swarm Optimization with Constriction Factor and Inertia Weight Approach (PSOCFIWA) have been used here for the design of linear phase band pass finite impulse response (FIR) filters. The fitness function is based on the squared error between the actual and the ideal filter response. PSOCFIWA seems to be promising optimization tool for FIR filter design especially in a dynamic environment where filter coefficients have to be adapted and fast convergence is of importance. Digital filter plays an important role in today's world of communication and computation. On the other hand, to design a digital finite impulse response (FIR) filter satisfying all the required conditions is a challenging one. In this paper, we have introduced an iterative method to find the optimal solution of optimal FIR filter design. FIR filter design is a multi-modal optimization problem. The conventional gradient based optimization techniques are not efficient for digital filter design. Given the filter specification to be realized, PSOCFIWA algorithm generates a set of filter coefficients and tries to meet the ideal frequency characteristic. In this paper, for the given problem, the realization of the FIR band pass filters of different order has been performed. The magnitude responses are demonstrated for the different design techniques of digital FIR filters. The simulation results have been compared with the well accepted evolutionary algorithm such as genetic algorithm (GA). The results justify that the proposed FIR filter design approach using PSOCFIWA outperforms to that of GA, not only in the accuracy of the designed filter but also in the convergence speed and solution quality.