Potential of Particle Swarm Optimization and Genetic Algorithms for FIR Filter Design

This article studies the performance of two metaheuristics, particle swarm optimization (PSO) and genetic algorithms (GA), for FIR filter design. The two approaches aim to find a solution to a given objective function but employ different strategies and computational effort to do so. PSO is a more recent heuristic search method than GA; its dynamics exploit the collaborative behavior of biological populations. Some researchers advocate the superiority of PSO over GA and highlight its capacity to solve complex problems thanks to its ease of implementation. In this paper, different versions of PSOs and GAs including our specific GA scheme are compared for FIR filter design. PSO generally outperforms standard GAs in some performance criteria, but our adaptive genetic algorithm is shown to be better on all criteria except CPU runtime. The study also underlines the importance of introducing intelligence in metaheuristics to make them more efficient by embedding self-tuning strategies. Furthermore, it establishes the potential complementarity of the approaches when solving this optimization problem.

[1]  Russell C. Eberhart,et al.  Tracking and optimizing dynamic systems with particle swarms , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[2]  Djamel Chikouche,et al.  An advanced genetic algorithm for designing 2-D FIR filters , 2011, Proceedings of 2011 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing.

[3]  Adel Nadjaran Toosi,et al.  Artificial fish swarm algorithm: a survey of the state-of-the-art, hybridization, combinatorial and indicative applications , 2012, Artificial Intelligence Review.

[4]  Jae S. Lim,et al.  Advanced topics in signal processing , 1987 .

[5]  Francesco Grimaccia,et al.  Genetical Swarm Optimization: an Evolutionary Algorithm for Antenna Design , 2006 .

[6]  W. Chang,et al.  Design of a higher-order digital differentiator using a particle swarm optimization approach , 2008 .

[7]  Yuhui Shi,et al.  Particle swarm optimization: developments, applications and resources , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[8]  Hung-Ching Lu,et al.  Design of arbitrary FIR log filters by genetic algorithm approach , 2000, Signal Process..

[9]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[10]  Durbadal Mandal,et al.  Swarm intelligence based optimal linear fir high pass filter design using Particle Swarm Optimization with Constriction Factor and Inertia Weight Approach , 2011, 2011 IEEE Student Conference on Research and Development.

[11]  R. Kaur,et al.  Design of FIR Filter Using Particle Swarm Optimization Algorithm for Audio Processing , 2012 .

[12]  Sakti Prasad Ghoshal,et al.  FIR Filter Design using Particle Swarm Optimization with Constriction Factor and Inertia Weight Approach , 2011 .

[13]  Y. Rahmat-Samii,et al.  Particle swarm, genetic algorithm, and their hybrids: optimization of a profiled corrugated horn antenna , 2002, IEEE Antennas and Propagation Society International Symposium (IEEE Cat. No.02CH37313).

[14]  Ajith Abraham,et al.  A fuzzy adaptive turbulent particle swarm optimisation , 2007 .

[15]  Djamel Chikouche,et al.  Evolutionary techniques for the synthesis of 2-D FIR filters , 2011, 2011 IEEE Statistical Signal Processing Workshop (SSP).

[16]  John H. Holland,et al.  Genetic Algorithms and the Optimal Allocation of Trials , 1973, SIAM J. Comput..

[17]  Nurhan Karaboga,et al.  Design of Digital FIR Filters Using Differential Evolution Algorithm , 2006 .

[18]  Sakti Prasad Ghoshal,et al.  Craziness based Particle Swarm Optimization algorithm for FIR band stop filter design , 2012, Swarm Evol. Comput..

[19]  Ai-Qin Mu,et al.  A Modified Particle Swarm Optimization Algorithm , 2009 .

[20]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[21]  Ling Cen,et al.  A hybrid genetic algorithm for the design of FIR filters with SPoT coefficients , 2007, Signal Process..

[22]  Sakti Prasad Ghoshal,et al.  Linear Phase High Pass FIR Filter Design using Improved Particle Swarm Optimization , 2011 .

[23]  Wei-Der Chang.  Two-dimensional fractional-order digital differentiator design by using differential evolution algorithm , 2009, Digit. Signal Process..

[24]  Naim Dahnoun,et al.  Studies in Computational Intelligence , 2013 .

[25]  Lifang Zhou,et al.  Satisfactory Optimization Design Of FIR Digital Filter Based On Adaptive Particle Swarm Optimization , 2007, 2007 IEEE International Conference on Control and Automation.

[26]  Shu Jun,et al.  A Hybrid of Differential Evolution and Particle Swarm Optimization for Global Optimization , 2009, 2009 Third International Symposium on Intelligent Information Technology Application.

[27]  Pascal Bouvry,et al.  Particle swarm optimization: Hybridization perspectives and experimental illustrations , 2011, Appl. Math. Comput..

[28]  Colin R. Reeves,et al.  A genetic algorithm for flowshop sequencing , 1995, Comput. Oper. Res..

[29]  Holger Blume,et al.  FIR-filter design with spatial and frequency design constraints using evolution strategies , 1998, Signal Process..

[30]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[31]  Andries Petrus Engelbrecht,et al.  Data clustering using particle swarm optimization , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[32]  Sakti Prasad Ghoshal,et al.  Novel Particle Swarm Optimization for Low Pass FIR Filter Design , 2012 .

[33]  D. Suckley.  Genetic algorithm in the design of FIR filters , 1991 .

[34]  Jehad I. Ababneh,et al.  Linear phase FIR filter design using particle swarm optimization and genetic algorithms , 2008, Digit. Signal Process..

[35]  Andries Petrus Engelbrecht,et al.  Differential Evolution Based Particle Swarm Optimization , 2007, 2007 IEEE Swarm Intelligence Symposium.

[36]  M.N.S. Swamy,et al.  Design of two-dimensional recursive filters using genetic algorithms , 2003 .

[37]  Xiaodong Li,et al.  This article has been accepted for inclusion in a future issue. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 1 Locating and Tracking Multiple Dynamic Optima by a Particle Swarm Model Using Speciation , 2022 .

[38]  Francisco Herrera,et al.  Analysis of new niching genetic algorithms for finding multiple solutions in the job shop scheduling , 2012, J. Intell. Manuf..

[39]  Russell C. Eberhart,et al.  Comparison between Genetic Algorithms and Particle Swarm Optimization , 1998, Evolutionary Programming.

[40]  Hung-Ching Lu,et al.  Complex genetic algorithm approach for designing equiripple complex FIR digital filters with weighting function , 2000, Signal Process..

[41]  Wang Jiaying,et al.  A modified particle swarm optimization algorithm , 2005 .