Skip to main content
SpringerLink
Log in

Integrated Cat Swarm Optimization and Differential Evolution Algorithm for Optimal IIR Filter Design in Multi-Objective Framework

  • Published:
Circuits, Systems, and Signal Processing Aims and scope Submit manuscript

Abstract

This paper proposes an integrated optimization technique which combines the features of the cat swarm optimization (CSO) algorithm with the traditional differential evolution (DE) algorithm and applies it for the optimal design of digital infinite impulse response (IIR) filters. Traditional design methods treat the digital IIR filter design as a single-objective optimization problem by taking into account the minimization of magnitude response error only and lack in considering the linear phase response error and the order of the filter. The aim of this paper was to design an IIR filter in multi-objective framework by equally considering the minimization of magnitude response error, the linear phase response error and the order of the filter. Firstly, the CSO algorithm is applied for digital IIR filter design. In order to start with a better solution set, the opposition-based learning strategy is then incorporated. To further improve the performance of CSO for designing stable digital IIR filters, the DE optimization algorithm is combined with CSO hence producing an integrated algorithm called multi-objective cat swarm and differential evolution algorithm (MOCSO-DE) which has the capability to explore and exploit the solution search space locally as well as globally. The developed integrated algorithm is effectively applied for the designing of the digital IIR low-pass (LP), high-pass (HP), band-pass (BP) and band-stop (BS) filters. To evaluate the effectiveness of the developed integrated algorithm, the computational results are compared with some well-established algorithms and it is observed that the developed algorithm is superior or at least comparable to other algorithms in getting better magnitude response and linear phase response together with lowest filter order and can also be implemented for the higher-order filter designs.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. S. Chattopadhyay, S.K. Sanyal, A. Chandra, Design of FIR filters using differential evolution optimization and its effect as a pulse shaping filter in a QPSK modulated system. J. Comput. Sci. Netw. Secur. 10(1), 313–321 (2010)

    Google Scholar 

  2. S. Chen, B.L. Luk, Digital IIR filter design using particle swarm optimization. J. Model. Identif. Control 9(4), 327–335 (2010)

    Article  Google Scholar 

  3. S.-C. Chu, P.-W. Tsai, Computational intelligence based on the behavior of cats. J. Innov. Comput. Inf. Control 3, 163–173 (2007)

    Google Scholar 

  4. S. Das, P.N. Suganthan, Differential evolution: a survey of the state-of-the-art. IEEE Trans. Evol. Comput. 15(1), 4–31 (2011)

    Article  Google Scholar 

  5. S.P. Harris, E.C. Ifeachor, Automatic design of frequency sampling filters by hybrid genetic algorithm techniques. IEEE Trans. Signal Process. 46(12), 3304–3314 (1998)

    Article  Google Scholar 

  6. M. Haseyama, D. Matsuura, A filter coefficient quantization method with genetic algorithm, including simulated annealing. IEEE Trans. Signal Process. Lett. 13(4), 189–192 (2006)

    Article  Google Scholar 

  7. C.Y.F. Ho, B.W.K. Ling, Y.Q. Liu, P.K.S. Tam, K.L. Teo, Optimal design of magnitude responses of rational infinite impulse response filters. IEEE Trans. Signal Process. 54(10), 4039–4046 (2006)

    Article  Google Scholar 

  8. I. Jury, Theory and Application of the z-Transform Method (Wiley, New York, 1964)

    Google Scholar 

  9. N. Karaboga, A new design method based on artificial bee colony algorithm for digital IIR filters. J. Frankl. Inst. 346, 328–348 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  10. N. Karaboga, A. Kalinli, D. Karaboga, Designing IIR filters using ant colony optimization algorithm. J. Eng. Appl. Artif. Intell. 17(3), 301–309 (2004)

    Article  MATH  Google Scholar 

  11. R. Kaur, M.S. Patterh, J.S. Dhillon, Real coded genetic algorithm for design of IIR digital filter with conflicting objectives. J. Appl. Math. Inf. Sci. 8(5), 2635–2644 (2014)

    Article  Google Scholar 

  12. X. Li (2005) Efficient differential evolution using speciation for multimodal function optimization, in Proceedings of International Conference on Evolutionary Computation pp. 873–880

  13. W.S. Lu, A. Antoniou, Design of digital filters and filter banks by optimization: a state of the art review, in Proceedings of European Signal Processing Conference, Finland vol. 1 (2000). pp. 351–354

  14. S.K. Mitra, J.F. Kaiser, Handbook for Digital Signal Processing (Wiley, New York, 1993)

    MATH  Google Scholar 

  15. P.M. Mohan, G. Panda, Solving multi-objective problems using cat swarm optimization. Expert Syst. Appl. 39(10), 2956–2964 (2012)

    Google Scholar 

  16. N. Noman, H. Iba, Accelerating differential evolution using an adaptive local search. IEEE Trans. Evol. Comput. 12(1), 107–125 (2008)

    Article  Google Scholar 

  17. Y.S. Ong, A.J. Keane, A domain knowledge based search advisor for design problem solving environments. Eng. Appl. Artif. Intell. 15(1), 105–116 (2002)

    Article  Google Scholar 

  18. Y.S. Ong, M.H. Lim, N. Zhu, K.W. Wong, Classification of adaptive memetic algorithms: a comparative study. IEEE Trans. Syst. Man Cybern. B Cybern. 36(1), 141–152 (2006)

    Article  Google Scholar 

  19. A.V. Oppenheim, R.W. Schafer, J.R. Buck, Discrete Time Signal Processing (Prentice Hall, Englewood Cliffs, 1999)

    Google Scholar 

  20. G. Panda, P.M. Pradhan, B. Majhi, IIR system identification using cat swarm optimization. Expert Syst. Appl. 38(10), 12671–12683 (2011)

    Article  Google Scholar 

  21. A.K. Qin, V.L. Huang, P.N. Sugathan, Differential evaluation algorithm with strategy adapter for global numerical optimization. IEEE Trans. Evol. Comput. 13(2), 398–417 (2009)

    Article  Google Scholar 

  22. S. Rahnamayan, H.R. Tizhoosh, M.A. Salama, Opposition based differential evolution. IEEE Trans. Evol. Comput. 12(1), 64–79 (2008)

    Article  Google Scholar 

  23. J.M. Renders, S.P. Flasse, Hybrid methods using genetic algorithms for global optimization. IEEE Trans. Syst. Man Cybern. B 26(2), 243–258 (1996)

    Article  Google Scholar 

  24. G.A. Sum-Im Taylor, M.R. Irving, Y.H. Song, Differential evolution algorithm for static multistage transmission expansion planning. IET Gener. Transm. Distrib. 3(4), 365–384 (2009)

    Article  Google Scholar 

  25. K.S. Tang, K.F. Man, S. Kwong, Z.F. Liu, Design and optimization of IIR filter structure using hierarchical genetic algorithms. Ind. Electron. 45(3), 481–487 (1998)

    Article  Google Scholar 

  26. H.R. Tizhoosh, Opposition based reinforcement learning. J. Adv. Comput. Intell. Intell. Inform. 10(3), 578–585 (2006)

    Google Scholar 

  27. J.-T. Tsai, J.-H. Chou, Optimal design of digital IIR filters using an improved immune algorithm. IEEE Trans. Signal Process. 54(12), 4582–4596 (2006)

    Article  Google Scholar 

  28. J.-T. Tsai, J.-H. Chou, T.-K. Liu, Optimal design of digital IIR filters by using hybrid taguchi genetic algorithm. IEEE Trans. Ind. Electron. 53(3), 867–87 (2006)

    Article  Google Scholar 

  29. P.-W. Tsai, J.-S. Pan, S.-M. Chen, B.-Y. Lio, Enhanced parallel cat swarm optimization based on the taguchi method. Expert Syst. Appl. 39(10), 6309–6319 (2012)

    Article  Google Scholar 

  30. Y. Wang, B. Li, Y. Chen, Digital IIR filter design using multi-objective optimization evolutionary algorithm. Appl. Soft Comput. 11(2), 1851–1857 (2011)

    Article  Google Scholar 

  31. Z.-H. Wang, C.-C. Chang, M.-C. Li, Optimizing least-significant-bit substitution using cat swarm optimization strategy. Inf. Sci. 192, 98–108 (2012)

    Article  Google Scholar 

  32. Y. Yu, Y. Xinjie, Cooperative co-evolutionary genetic algorithm for digital IIR filter design. IEEE Trans. Ind. Electron. 54(3), 1311–1318 (2007)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Instrumentation Engineering, Sant Longowal Institute of Engineering and Technology, Longowal, Punjab, 148106, India

    Kamalpreet Kaur Dhaliwal & Jaspreet Singh Dhillon

Authors
  1. Kamalpreet Kaur Dhaliwal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jaspreet Singh Dhillon
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kamalpreet Kaur Dhaliwal.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dhaliwal, K.K., Dhillon, J.S. Integrated Cat Swarm Optimization and Differential Evolution Algorithm for Optimal IIR Filter Design in Multi-Objective Framework. Circuits Syst Signal Process 36, 270–296 (2017). https://doi.org/10.1007/s00034-016-0304-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00034-016-0304-9

Keywords

Search

Navigation