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Soft Computing

, Volume 23, Issue 18, pp 8855–8872 | Cite as

An improved global-best-driven flower pollination algorithm for optimal design of two-dimensional FIR filter

  • Supriya DhabalEmail author
  • Palaniandavar Venkateswaran
Methodologies and Application
  • 155 Downloads

Abstract

The design of two-dimensional (2D) digital filter is a higher-order, nonlinear, and multi-modal optimization problem. This paper presents an improved Global-best-driven Flower Pollination Algorithm, named as GFPA, for the design of 2D Finite Impulse Response (FIR) filters. Two methods have been proposed—the first method minimizes the weighted square error via GFPA and the second method finds the coefficients of one-dimensional FIR filter by GFPA before McClellan transformation. The performance of proposed algorithm has been compared with state-of-the-art algorithms and the simulation results show significant improvements. For the design of a \(15\times 15\) circular symmetric filter, an average reduction of 55% in fitness function evaluation and 72% in execution time is observed. Further, the experiment on CEC 2014 benchmark functions demonstrates better optimal solution than existing algorithms.

Keywords

2D FIR filter Optimization Mini-max design Flower pollination algorithm 

Notes

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict of interest.

References

  1. Abedinpourshotorban H, Shamsuddin SM, Beheshti Z, Jawawi DNA (2016) Electromagnetic field optimization: a physics-inspired metaheuristic optimization algorithm. Swarm Evolut Comput 26:8–22CrossRefGoogle Scholar
  2. Aggarwal A, Kumar M, Rawat TK, Upadhyay DK (2016) Optimal design of 2D FIR filters with quadrantally symmetric properties using fractional derivative constraints. Circ Syst Signal Process 35(6):2213–2257zbMATHCrossRefGoogle Scholar
  3. Bansal JC, Singh PK, Saraswat M, Verma A, Jadon SS, Abraham A (2011) Inertia weight strategies in particle swarm optimization. In: Third world congress on nature and biologically inspired computing, pp 633–640Google Scholar
  4. Bindima T, Elias E (2016) Design of efficient circularly symmetric two-dimensional variable digital fir filters. J Adv Res 7(3):336–347CrossRefGoogle Scholar
  5. Biswas S, Mandal KK, Chakraborty N (2013) Constriction factor based particle swarm optimization for analyzing tuned reactive power dispatch. Front Energy 7(2):174–181CrossRefGoogle Scholar
  6. Boudjelaba K, Ros F, Chikouche D (2014) Adaptive genetic algorithm-based approach to improve the synthesis of two-dimensional finite impulse response filters. IET Signal Process 8(5):429–446CrossRefGoogle Scholar
  7. Chandrasekaran K, Simon SP (2012) Multi-objective scheduling problem: hybrid approach using fuzzy assisted cuckoo search algorithm. Swarm Evolut Comput 5:1–16CrossRefGoogle Scholar
  8. Dhabal S, Venkateswaran P (2017a) An efficient gbest-guided cuckoo search algorithm for higher order two channel filter bank design. Swarm Evolut Comput 33:68–84CrossRefGoogle Scholar
  9. Dhabal S, Venkateswaran P (2017b) A novel accelerated artificial bee colony algorithm for optimal design of two dimensional FIR filter. Multidimens Syst Signal Process 28(2):471–493zbMATHCrossRefGoogle Scholar
  10. Dhabal S, Chakraborty N, Mukherjee A, Biswas J (2016) Design of higher order FIR low pass filter using cuckoo search algorithm. In: Proceedings of international conference on communication and signal processing (ICCSP), IEEE, pp 0936–0941Google Scholar
  11. Draa A (2015) On the performances of the flower pollination algorithm—qualitative and quantitative analyses. Appl Soft Comput 34:349–371CrossRefGoogle Scholar
  12. Gao W, Liu S, Huang L (2012) A global best artificial bee colony algorithm for global optimization. J Comput Appl Math 236(11):2741–2753MathSciNetzbMATHCrossRefGoogle Scholar
  13. Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39(3):459–471MathSciNetzbMATHCrossRefGoogle Scholar
  14. Karaboga D, Basturk B (2008) On the performance of artificial bee colony (ABC) algorithm. Appl Soft Comput 8(1):687–697CrossRefGoogle Scholar
  15. Kockanat S, Karaboga N, Koza T (2012) Image denoising with 2-D fir filter by using artificial bee colony algorithm. In: Proceedings of international symposium on innovations in intelligent systems and applications (INISTA), pp 2–4Google Scholar
  16. Lai PX, Cheng Y (2007) A sequential constrained least-square approach to minimax design of 2-D FIR filters. IEEE Trans Circ Syst II 54(11):994–998Google Scholar
  17. Liang JJ, Qu BY, Suganthan PN (2013) Problem definitions and evaluation criteria for the cec 2014 special session and competition on single objective real-parameter numerical optimization. Technical Report 201311, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou, China and Technical Report, Nanyang Technological University, SingaporeGoogle Scholar
  18. Liu JC, Tai YL (2011) Design of 2-D wideband circularly symmetric FIR filters by multiplierless high-order transformation. IEEE Trans Circ Syst I, Reg Pap 58(4):746–754MathSciNetCrossRefGoogle Scholar
  19. Lu WS (2002) A unified approach for the design of 2-D digital filters via semidefinite programming. IEEE Trans Circ Syst I Fund Theory Appl 49(6):814–826MathSciNetzbMATHCrossRefGoogle Scholar
  20. Lu WS, Hinamoto T (2006) A second-order cone programming approach for minimax design of 2-D FIR filters with low group delay. In: Proceedings of IEEE international symposium on circuits and systems, pp 21–24Google Scholar
  21. Lu WS, Wang HP, Antoniou A (1990) Design of two-dimensional FIR digital filters by using the singular-value decomposition. IEEE Trans Circ Syst 37(1):35–46MathSciNetCrossRefGoogle Scholar
  22. Mallipeddi R, Suganthan PN, Pan QK, Tasgetiren MF (2011) Differential evolution algorithm with ensemble of parameters and mutation strategies. Appl Soft Comput 11(2):1679–1696CrossRefGoogle Scholar
  23. Manuel M, Elias E (2012a) Design of sharp 2D multiplier-less circularly symmetric FIR filter using harmony search algorithm and frequency transformation. J Signal Inf Process 3(3):344–351Google Scholar
  24. Manuel M, Elias E (2012b) A novel approach for the design of 2D sharp circularly symmetric FIR filters. Glob J Res Eng Electr Electron Eng 12(6):32–40Google Scholar
  25. Manuel M, Krishnan R, Elias E (2012) Design of multiplierless 2-D sharp wideband filters using FRM and GSA. Glob J Res Eng 12(5):41–50Google Scholar
  26. McClellan JH (1973) The design of two-dimensional filters by transformations. In: Proceedings of 7th annual conference on information sciences and systems, pp 247–251Google Scholar
  27. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67CrossRefGoogle Scholar
  28. Nickabadi A, Ebadzadeh MM, Safabakhsh R (2011) A novel particle swarm optimization algorithm with adaptive inertia weight. Appl Soft Comput 11(4):3658–3670CrossRefGoogle Scholar
  29. Pei SC, Shyu JJ (1993) Design of 2-D FIR digital filters by McClellan transformation and least squares eigencontour mapping. IEEE Trans Circ Syst II Analog Digit SIgnal Process 40(9):546–555zbMATHCrossRefGoogle Scholar
  30. Pei SC, Shyu JJ (1995) Design of two-dimensional FIR digital filters by McClellan transformation and least-squares contour mapping. Signal Process 44(1):19–26zbMATHCrossRefGoogle Scholar
  31. Rao RV, Savsani VJ, Vakharia DP (2011) Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 43(3):303–315CrossRefGoogle Scholar
  32. Saeedi J, Faez K (2012) Infrared and visible image fusion using fuzzy logic and population-based optimization. Appl Soft Comput 12(3):1041–1054CrossRefGoogle Scholar
  33. Sarangi SK, Panda R, Dash M (2014) Design of 1-D and 2-D recursive filters using crossover bacterial foraging and cuckoo search techniques. Eng Appl Artif Intell 34:109–121CrossRefGoogle Scholar
  34. Shao P, Wu Z, Tran DC (2017) FIR digital filter design using improved particle swarm optimization based on refraction principle. Soft Comput 21(10):2631–2642CrossRefGoogle Scholar
  35. Shyu JJ, Pei SC, Huang YD (2009) Design of variable two-dimensional FIR digital filters by McClellan transformation. IEEE Trans Circ Syst I Reg Pap 56(3):574–582MathSciNetCrossRefGoogle Scholar
  36. Sidhu DS, Dhillon JS, Kaur D (2017) Hybrid heuristic search method for design of digital IIR filter with conflicting objectives. Soft Comput 21(12):3461–3476zbMATHCrossRefGoogle Scholar
  37. Soleimani A (2015) Combine particle swarm optimization algorithm and canonical sign digit to design finite impulse response filter. Soft Comput 19(2):407–419MathSciNetCrossRefGoogle Scholar
  38. Tseng C, Lee SL (2013) Designs of two-dimensional linear phase FIR filters using fractional derivative constraints. Signal Process 93(5):1141–1151CrossRefGoogle Scholar
  39. Tseng C, Lee SL (2014) Designs of fractional derivative constrained 1-D and 2-D FIR filters in the complex domain. Signal Process 95(5):111–125CrossRefGoogle Scholar
  40. Tzeng ST (2007) Design of 2-D FIR digital filters with specified magnitude and group delay responses by GA approach. Signal Process 87(9):2036–2044zbMATHCrossRefGoogle Scholar
  41. Wang H (2015) A new separable two-dimensional finite impulse response filter design with sparse coefficients. IEEE Trans Circ Syst I Reg Pap 62(12):2864–2873MathSciNetCrossRefGoogle Scholar
  42. Wang Y, Yue J, Su Y, Liu H (2013) Design of two-dimensional zero phase FIR digital filter by mcclellan transformation and interval global optimization. IEEE Trans Circ Syst II Express Briefs 60(3):167–171Google Scholar
  43. Yang XS (2012) Flower pollination algorithm for global optimization. Unconv Comput Nat Comput Lect Notes Comput Sci 7445:240–249zbMATHGoogle Scholar
  44. Yang XS, Deb S (2010) Engineering optimisation by cuckoo search. Int J Math Modell Numer Optim 1(4):330–343zbMATHGoogle Scholar
  45. Yang XS, Deb S (2013) Multiobjective cuckoo search for design optimization. Comput Oper Res 40(6):1616–1624MathSciNetzbMATHCrossRefGoogle Scholar
  46. Yang XS, Karamanoglu M, He X (2014) Flower pollination algorithm: a novel approach for multiobjective optimization. Eng Optim 46(9):1222–1237MathSciNetCrossRefGoogle Scholar
  47. Zhao R, Lai X (2013) Fast two-dimensional weighted least squares techniques for the design of two-dimensional finite impulse response filters. IJ Control Theory Appl 11(2):180–185MathSciNetzbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringNetaji Subhash Engineering CollegeKolkataIndia
  2. 2.Department of Electronics and Tele-Communication EngineeringJadavpur UniversityKolkataIndia

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