Improving Design Accuracy of a Finite Impulse Response Fractional Order Digital Differentiator Filter Using Quantum-inspired Evolutionary Algorithm

In this paper, a quantum-inspired evolutionary algorithm (QiEA) is applied to determine the optimal coefficients of a finite impulse response fractional-order digital differentiator filter. QiEA brings the power of quantum-inspired methods to the principles of natural evolution, which uses operators to gradually improve solutions over time. The design accuracy in terms of absolute magnitude error (AME) and phase error achieved in QiEA was compared with the AME achieved by the genetic algorithm (GA) and cuckoo search algorithm (CSA). In addition, we investigated the impact of increasing population sizes on these design parameters of the fractional order (FOD) digital differentiator filter. The simulation results demonstrate conclusively that the FOD digital differentiator's design accuracy using QiEA beats both CSA and GA in terms of accuracy, error function reduction, and identifying optimal filter coefficients.