Advertisement

Circuits, Systems, and Signal Processing

, Volume 37, Issue 5, pp 2117–2141 | Cite as

Design of Digital IIR Filter with Conflicting Objectives Using Hybrid Predator–Prey Optimization

  • D. S. Sidhu
  • J. S. Dhillon
Article
  • 110 Downloads

Abstract

The design of digital IIR filter as a single-objective optimization problem using evolutionary algorithms has gained much attention in the previous years. In this paper, the design of filter is treated as a multi-objective problem by simultaneously minimizing the magnitude response error, linear phase response error and optimal order within the stability constraints. The global search technique, predator–prey optimization (PPO), has been applied to design the digital IIR filter. The global search technique has been hybridized with binary successive approximation (BSA)-based evolutionary search method for exploring the search space locally. The relative performance of PPO and hybrid PPO has been evaluated by applying these techniques to standard mathematical test functions. The above-proposed hybrid search technique has been applied to achieve the solution for multi-parameter and multi-objective optimization problem of low-pass (LP), high-pass (HP), band-pass (BP) and band-stop (BS) digital IIR filter design. The results obtained from the proposed technique are compared with the results of other algorithms applied by other researchers for the design of digital IIR filter.

Keywords

Predator–prey optimization (PPO) Binary successive approximation (BSA) Digital IIR filter Multi-objective problem 

References

  1. 1.
    S. Chen, B. Luk, Digital IIR filter design using particle swarm optimization. Int. J. Model. Identification Control 9(4), 327–335 (2010)CrossRefGoogle Scholar
  2. 2.
    J.S. Dhillon, J.S. Dhillon, D.P. Kothari, Economic-emission load dispatch using binary successive approximation-based evolutionary search. IET Gener. Transm. Distrib. 3(1), 1–16 (2009)CrossRefGoogle Scholar
  3. 3.
    J. Hua, W. Kuang, Z. Gao, L. Meng, Z. Xu, Image denoising using 2-D FIR filters designed with DEPSO. Multimed. Tools Appl. 69, 157–169 (2014)CrossRefGoogle Scholar
  4. 4.
    N. Karabog, Digital IIR filter design using differential evolution algorithm. EURASIP J. Appl. Sig. Process. 8, 1269–1276 (2005)Google Scholar
  5. 5.
    N. Karaboga, M.B. Cetinkaya, A novel and efficient algorithm for adaptive filtering: artificial bee colony algorithm. Turk. J. Electr. Eng. Comput. Sci. 19(1), 175–190 (2011)Google Scholar
  6. 6.
    N. Karaboga, M.B. Cetinkaya, Design of digital FIR filters using differential evolution algorithm. Circuits Syst. Sig. Process. 25, 649–660 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    N. Karaboga, M.B. Cetinkaya, Design of minimum phase digital IIR filters by using genetic algorithm. Eng. Intell. Syst. Electr. Eng. Commun. 16, 1–8 (2008)Google Scholar
  8. 8.
    K. Kaur, J.S. Dhillon, Design of digital IIR filters using integrated cat swarm optimization and differential evolution. Int. J. Comput. Appl. 99(4), 28–43 (2014)Google Scholar
  9. 9.
    R. Kaur, M.S. Patterh, J.S. Dhillon, Real coded genetic algorithm for design of digital IIR filter with conflicting objective. Appl. Math. Inf. Sci. 08(5), 2635–2644 (2014)CrossRefGoogle Scholar
  10. 10.
    J. Kennedy, R. Eberhart, Particle Swarm Optimization. IEEE International Conference on Neural Networks, Piscataway, New Jersey, USA. 04, 1942–1948 (1995)Google Scholar
  11. 11.
    J.Y. Lin, Y.P. Chen, Analysis on the collaboration between global search and local search in memetic computation. IEEE Trans. Evolut. Comput. 15(5), 608–623 (2011)CrossRefGoogle Scholar
  12. 12.
    J.G. Proakis, D.G. Manolakis, Digital Signal Processing, Principles Algorithms and Applications (Pearson Education Inc., South Asia, 2013)Google Scholar
  13. 13.
    S.K. Saha, R. Kar, D. Mandal, S.P. Ghoshal, Design and simulation of FIR band pass and band stop filters using gravitational search algorithm. Memet. Comput. 5(4), 311–321 (2013)CrossRefGoogle Scholar
  14. 14.
    B.A. Shenoi, Introduction to Digital Signal Processing and Filter Design (Wiley, Hoboken, 2006)Google Scholar
  15. 15.
    D.S. Sidhu, J.S. Dhillon, D. Kaur, Design of digital IIR filter with conflicting objectives using hybrid gravitational search algorithm. Math. Probl. Eng. doi: 10.1155/2015/282809 (2015)
  16. 16.
    D.S. Sidhu, J.S. Dhillon, D. Kaur, Hybrid heuristic search method for design of digital IIR filter with conflicting objectives. Soft Comput. 21(12), 3461–3476 (2017)CrossRefzbMATHGoogle Scholar
  17. 17.
    A. Silva, A. Neves, E. Costa, An empirical comparison of particle swarm optimization and predator prey optimization. Proceeding: Irish International Conference on Artificial Intelligence and Cognitive Science. 24(64), 793–797 (2002)Google Scholar
  18. 18.
    B. Singh, J.S. Dhillon, Y.S. Brar, A hybrid differential evolution method for the design of IIR filter. ACEEE Int. J. Signal Image Process. 04(1), 1–10 (2013)CrossRefGoogle Scholar
  19. 19.
    B. Singh, J.S. Dhillon, Y.S. Brar, Predator prey optimization method for the design of IIR filter. WSEAS Trans. Signal Process. 9, 51–62 (2013)Google Scholar
  20. 20.
    C.W. Tsai, C.H. Huang, C.L. Lin, Structure-specified IIR filter and control design using real structured genetic algorithm. Appl. Soft Comput. 9, 1285–1295 (2009)CrossRefGoogle Scholar
  21. 21.
    J.T. Tsai, J.H. Chou, T.K. Liu, Optimal design of digital filters by using hybrid Taguchi genetic algorithm. IEEE Trans. Ind. Electron. 53(3), 867–879 (2006)CrossRefGoogle Scholar
  22. 22.
    Vasundhara, D. Mandal, R. Kar, S.P. Ghoshal, Digital FIR filter design using fitness based hybrid adaptive differential evolution with particle swarm optimization. Nat. Comput. 55–64 (2014)Google Scholar
  23. 23.
    Y. Wang, B. Li, Y. Chen, Digital IIR filter design using multi-objective optimization evolutionary algorithm. Appl. Soft Comput. 11, 1851–1857 (2011)CrossRefGoogle Scholar
  24. 24.
    Y. Yu, Y. Xinjie, Cooperative coevolutionary genetic algorithm for digital IIR filter design. IEEE Trans. Ind. Electron. 54(3), 1311–1318 (2007)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Government Polytechnic CollegeBathindaIndia
  2. 2.Department of Electrical and Instrumentation EngineeringSant Longowal Institute of Engineering and TechnologyLongowalIndia

Personalised recommendations