FIR Filter Design Using Grasshopper Optimization Algorithm

Abstract

AbstractIn this paper, digital finite impulse response (FIR) low-pass filter (LPF) and high-pass filter (HPF) are designed using a novel meta-heuristic algorithm named grasshopper optimization algorithm (GOA). The GOA is meta-heuristic population-based optimization algorithm, which mimics the food searching behaviour of the grasshopper. The filter design aims to evaluate the optimal filter parameters and find the minimum objective function value so that the output of the designed filter matches with the output response of the ideal filter. Mean square error (MSE) is taken as the error objective function. The results obtained using GOA are compared with the other two algorithms, namely particle swarm optimization (PSO) algorithm and grey wolf optimization (GWO) algorithm. The simulated results reveal that GOA is best suited algorithm for FIR filter design problem.KeywordsFIR filter designMean Square ErrorParticle Swarm Optimization AlgorithmGrey Wolf Optimization AlgorithmGrasshopper Optimization Algorithm