Performance Comparison of Teaching-Learning-Based Optimization and Differential Evolution Algorithms Applied to the Design of Linear Phase Digital FIR Filter

Evolutionary optimization algorithms have recently been applied for the optimal design of digital FIR filters. These algorithms approximate the frequency response of the desired filter by optimizing the coefficients of the actual filter. In this paper, a recent method, namely, Teaching-Learning-Based Optimization (TLBO) and Differential Evolution (DE) algorithms are applied for the design of linear phase digital FIR filter. The results obtained for the case of TLBO algorithm are compared statistically with those obtained using DE algorithm. The results showed faster convergence in the case of TLBO method. However, the approximation to the desired frequency response is found to be better in the case of DE algorithm. The frequency responses obtained using these algorithms are compared with those obtained using Parks-McClellan method of FIR filter design, and it is found that better performance is exhibited by TLBO and DE algorithms.