Digital FIR Filter Design by PSO and its v ariants Attractive and Repulsive PSO(ARPSO) & Craziness based PSO(CRPSO)
Zain Ali1, Bharat Lal Harijan2, Tayab Din Memon3, Nazmus Nafi4, Ubed-u-Rahman Memon5
1Zain Ali*, Institute of Information and Communication Technologies Mehran UET, Jamshoro, Pakistan.
2Bharat Lal Harijan, Department of Electronic Engineering, Mehran UET Jamshoro, Pakistan.
3Tayab Din Memon, School of Information Technology and Engineering, Melbourne Institute of Technology, Melbourne, Australia. Department of Electronic Engineering, Mehran UET Jamshoro, Pakistan.
4Nazmus Nafi, School of Information Technology and Engineering, Melbourne Institute of Technology, Victoria, Australia.
5Ubed-u-Rahman Memon, Haptics Human Robotics and Condition Monitoring Lab, Mehran UET Jamshoro, Pakistan.
Manuscript received on March 09, 2021. | Revised Manuscript received on March 15, 2021. | Manuscript published on March 30, 2021. | PP: 136-141 | Volume-9 Issue-6, March 2021. | Retrieval Number: 100.1/ijrte.F5515039621 | DOI: 10.35940/ijrte.F5515.039621
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Digital filters play a major role in signal processing that are employed in many applications such as in control systems, audio or video processing systems, noise reduction applications and different systems for communication. In this regard, FIR filters are employed because of frequency stability and linearity in their phase response. FIR filter design requires multi-modal optimization problems. Therefore, PSO (Particle Swarm Optimization) algorithm and its variants are more adaptable techniques based upon particles’ population in the search space and a great option for designing FIR filter. PSO and its different variants improve the solution characteristic by providing a unique approach for updating the velocity and position of the swarm. An optimized set of filter coefficient is produced by PSO and its variant algorithms which gives the optimized results in passband and stopband. In this research paper, Digital FIR filter is effectively designed by using PSO Algorithm and its two variants ARPSO and CRPSO in MATLAB. The outcomes prove that the filter design technique using CRPSO is better than filter design by PM algorithm. PSO and ARPSO Algorithms in the context of frequency spectrum and RMS error.
Keywords: Craziness based Particle Swarm Optimization (CRPSO), Attractive and Repulsive Particle Swarm Optimization (ARPSO), Particle Swarm Optimization (PSO), Lowpass filter.