Multi-band FIR filter design using Particle Swarm Optimization with Minimax initialization

2012 ◽  
Author(s):  
Ruksaphol Ngamtawee ◽  
Paramote Wardkein
Keyword(s):  
Particle Swarm Optimization ◽  
Filter Design ◽  
Particle Swarm ◽  
Fir Filter ◽  
Swarm Optimization ◽  
Fir Filter Design ◽  
Multi Band

scholarly journals Moth Flame Optimization Algorithm for Optimal FIR Filter Design

2021 ◽  
Vol 13 (5) ◽  
pp. 24-34
Author(s):  
Zainab Muhammad Adamu ◽  
◽  
Emmanuel Gbenga Dada ◽  
Stephen Bassi Joseph
Keyword(s):  
Particle Swarm Optimization ◽  
Filter Design ◽  
Stop Band ◽  
Particle Swarm ◽  
Fir Filter ◽  
Pass Band ◽  
Transition Width ◽  
Swarm Optimization ◽  
Fitness Value ◽  
Fir Filter Design

This paper presents the application of Moth Flame optimization (MFO) algorithm to determine the best impulse response coefficients of FIR low pass, high pass, band pass and band stop filters. MFO was inspired by observing the navigation strategy of moths in nature called transverse orientation composed of three mathematical submodels. The performance of the proposed technique was compared to those of other well-known high performing optimization techniques like techniques like Particle Swarm Optimization (PSO), Novel Particle Swarm Optimization (NPSO), Improved Novel Particle Swarm Optimization (INPSO), Genetic Algorithm (GA), Parks and McClellan (PM) Algorithm. The performances of the MFO based designed optimized FIR filters have proved to be superior as compared to those obtained by PSO, NPSO, INPSO, GA, and PM Algorithm. Simulation results indicated that the maximum stop band ripples 0.057326, transition width 0.079 and fitness value 1.3682 obtained by MFO is better than that of PSO, NPSO, INPSO, GA, and PM Algorithms. The value of stop band ripples indicated the ripples or fluctuations obtained at the range which signals are attenuated is very low. The reduced value of transition width is the rate at which a signal changes from either stop band to pass band of a filter or vice versa is very good. Also, small fitness value in an indication that the values of the control variable of MFO are very near to its optimum solutions. The proposed design technique in this work generates excellent solution with high computational efficiency. This shows that MFO algorithm is an outstanding technique for FIR filter design.

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