Design of Higher Order Digital IIR Low Pass Filter Using Hybrid Differential Evolution

In this paper, hybrid technique with differential evolution (DE) method as a global search technique and binary successive approximation (BSA) based evolutionary search method as a local search technique is proposed. Evolutionary search is used for the exploration and pattern search method for exploitation. To initiate with good population, the opposition based learning strategy is applied. Migration strategy is applied to maintain the diversity in the population. The above proposed hybrid technique has been applied effectively to solve the multi-parameter optimization problem of higher order low-pass stable digital IIR filter design. The obtained results prove that the proposed technique is better than or comparable to other algorithms used by other researchers and can be applied to design HP, BP and BS IIR filters.

[1]  Mehmet Bahadır Çetinkaya,et al.  A novel and efficient algorithm for adaptive filtering: Artificial bee colony algorithm , 2011, Turkish Journal of Electrical Engineering and Computer Sciences.

[2]  Tung-Kuan Liu,et al.  Optimal design of digital IIR filters by using hybrid taguchi genetic algorithm , 2006, IEEE Trans. Ind. Electron..

[3]  Yadwinder Singh Brar,et al.  Predator Prey Optimization Method For The Design Of IIR Filter , 2013 .

[4]  M. Lightner,et al.  Multiple criterion optimization for the design of electronic circuits , 1981 .

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

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

[7]  Hamid R. Tizhoosh,et al.  Opposition-Based Reinforcement Learning , 2006, J. Adv. Comput. Intell. Intell. Informatics.

[8]  Jean-Michel Renders,et al.  Hybrid methods using genetic algorithms for global optimization , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[9]  John G. Proakis,et al.  Digital Signal Processing: Principles, Algorithms, and Applications , 1992 .

[10]  Chun-Liang Lin,et al.  Structure-specified IIR filter and control design using real structured genetic algorithm , 2009, Appl. Soft Comput..

[11]  J. S. Dhillon,et al.  Economic-emission load dispatch using binary successive approximation-based evolutionary search , 2009 .

[12]  G. Vanuytsel,et al.  Efficient hybrid optimization of fixed-point cascaded IIR filter coefficients , 2002, IMTC/2002. Proceedings of the 19th IEEE Instrumentation and Measurement Technology Conference (IEEE Cat. No.00CH37276).

[13]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[14]  Nurhan Karaboga,et al.  Digital IIR Filter Design Using Differential Evolution Algorithm , 2005, EURASIP J. Adv. Signal Process..

[15]  M.M.A. Salama,et al.  Opposition-Based Differential Evolution , 2008, IEEE Transactions on Evolutionary Computation.

[16]  Bing Lam Luk,et al.  Digital IIR filter design using particle swarm optimisation , 2010, Int. J. Model. Identif. Control..

[17]  Jyh-Horng Chou,et al.  Design of Optimal Digital IIR Filters by Using an Improved Immune Algorithm , 2006, IEEE Transactions on Signal Processing.

[18]  Andrew Luk,et al.  Fast convergent genetic search for adaptive IIR filtering , 1994, Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing.

[19]  Kim-Fung Man,et al.  Design and optimization of IIR filter structure using hierarchical genetic algorithms , 1998, IEEE Trans. Ind. Electron..

[20]  Malcolm Irving,et al.  Differential evolution algorithm for static and multistage transmission expansion planning , 2009 .

[21]  B. A. Shenoi,et al.  Introduction to Digital Signal Processing and Filter Design , 2005 .

[22]  E. I. Jury,et al.  Theory and application of the z-transform method , 1965 .

[23]  Jaspreet Singh Dhillon,et al.  Digital IIR Filter Design using Real Coded Genetic Algorithm , 2013 .