Skip to main content
SpringerLink
Log in

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

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

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.

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

Similar content being viewed by others

References

  • Abedinpourshotorban H, Shamsuddin SM, Beheshti Z, Jawawi DNA (2016) Electromagnetic field optimization: a physics-inspired metaheuristic optimization algorithm. Swarm Evolut Comput 26:8–22

    Article  Google Scholar 

  • 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):2213–2257

    Article  MATH  Google Scholar 

  • 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 633–640

  • Bindima T, Elias E (2016) Design of efficient circularly symmetric two-dimensional variable digital fir filters. J Adv Res 7(3):336–347

    Article  Google Scholar 

  • Biswas S, Mandal KK, Chakraborty N (2013) Constriction factor based particle swarm optimization for analyzing tuned reactive power dispatch. Front Energy 7(2):174–181

    Article  Google Scholar 

  • 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):429–446

    Article  Google Scholar 

  • Chandrasekaran K, Simon SP (2012) Multi-objective scheduling problem: hybrid approach using fuzzy assisted cuckoo search algorithm. Swarm Evolut Comput 5:1–16

    Article  Google Scholar 

  • Dhabal S, Venkateswaran P (2017a) An efficient gbest-guided cuckoo search algorithm for higher order two channel filter bank design. Swarm Evolut Comput 33:68–84

    Article  Google Scholar 

  • 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):471–493

    Article  MATH  Google Scholar 

  • 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 0936–0941

  • Draa A (2015) On the performances of the flower pollination algorithm—qualitative and quantitative analyses. Appl Soft Comput 34:349–371

    Article  Google Scholar 

  • Gao W, Liu S, Huang L (2012) A global best artificial bee colony algorithm for global optimization. J Comput Appl Math 236(11):2741–2753

    Article  MathSciNet  MATH  Google Scholar 

  • Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39(3):459–471

    Article  MathSciNet  MATH  Google Scholar 

  • Karaboga D, Basturk B (2008) On the performance of artificial bee colony (ABC) algorithm. Appl Soft Comput 8(1):687–697

    Article  Google Scholar 

  • 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 2–4

  • 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):994–998

    Google Scholar 

  • 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, Singapore

  • 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):746–754

    Article  MathSciNet  Google Scholar 

  • 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):814–826

    Article  MathSciNet  MATH  Google Scholar 

  • 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 21–24

  • 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):35–46

    Article  MathSciNet  Google Scholar 

  • Mallipeddi R, Suganthan PN, Pan QK, Tasgetiren MF (2011) Differential evolution algorithm with ensemble of parameters and mutation strategies. Appl Soft Comput 11(2):1679–1696

    Article  Google Scholar 

  • 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):344–351

    Google Scholar 

  • 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):32–40

    Google Scholar 

  • 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):41–50

    Google Scholar 

  • McClellan JH (1973) The design of two-dimensional filters by transformations. In: Proceedings of 7th annual conference on information sciences and systems, pp 247–251

  • Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67

    Article  Google Scholar 

  • Nickabadi A, Ebadzadeh MM, Safabakhsh R (2011) A novel particle swarm optimization algorithm with adaptive inertia weight. Appl Soft Comput 11(4):3658–3670

    Article  Google Scholar 

  • 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):546–555

    Article  MATH  Google Scholar 

  • Pei SC, Shyu JJ (1995) Design of two-dimensional FIR digital filters by McClellan transformation and least-squares contour mapping. Signal Process 44(1):19–26

    Article  MATH  Google Scholar 

  • 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):303–315

    Article  Google Scholar 

  • Saeedi J, Faez K (2012) Infrared and visible image fusion using fuzzy logic and population-based optimization. Appl Soft Comput 12(3):1041–1054

    Article  Google Scholar 

  • 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 34:109–121

    Article  Google Scholar 

  • Shao P, Wu Z, Tran DC (2017) FIR digital filter design using improved particle swarm optimization based on refraction principle. Soft Comput 21(10):2631–2642

    Article  Google Scholar 

  • 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):574–582

    Article  MathSciNet  Google Scholar 

  • Sidhu DS, Dhillon JS, Kaur D (2017) Hybrid heuristic search method for design of digital IIR filter with conflicting objectives. Soft Comput 21(12):3461–3476

    Article  MATH  Google Scholar 

  • Soleimani A (2015) Combine particle swarm optimization algorithm and canonical sign digit to design finite impulse response filter. Soft Comput 19(2):407–419

    Article  MathSciNet  Google Scholar 

  • Tseng C, Lee SL (2013) Designs of two-dimensional linear phase FIR filters using fractional derivative constraints. Signal Process 93(5):1141–1151

    Article  Google Scholar 

  • 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):111–125

    Article  Google Scholar 

  • Tzeng ST (2007) Design of 2-D FIR digital filters with specified magnitude and group delay responses by GA approach. Signal Process 87(9):2036–2044

    Article  MATH  Google Scholar 

  • 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):2864–2873

    Article  MathSciNet  Google Scholar 

  • 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):167–171

    Google Scholar 

  • Yang XS (2012) Flower pollination algorithm for global optimization. Unconv Comput Nat Comput Lect Notes Comput Sci 7445:240–249

    MATH  Google Scholar 

  • Yang XS, Deb S (2010) Engineering optimisation by cuckoo search. Int J Math Modell Numer Optim 1(4):330–343

    MATH  Google Scholar 

  • Yang XS, Deb S (2013) Multiobjective cuckoo search for design optimization. Comput Oper Res 40(6):1616–1624

    Article  MathSciNet  MATH  Google Scholar 

  • Yang XS, Karamanoglu M, He X (2014) Flower pollination algorithm: a novel approach for multiobjective optimization. Eng Optim 46(9):1222–1237

    Article  MathSciNet  Google Scholar 

  • 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):180–185

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Netaji Subhash Engineering College, Kolkata, West Bengal, 700152, India

    Supriya Dhabal

  2. Department of Electronics and Tele-Communication Engineering, Jadavpur University, Kolkata, West Bengal, 700032, India

    Palaniandavar Venkateswaran

Authors
  1. Supriya Dhabal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Palaniandavar Venkateswaran
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Supriya Dhabal.

Ethics declarations

Conflict of interest

All authors declare that they have no conflict of interest.

Additional information

Communicated by V. Loia.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dhabal, S., Venkateswaran, P. An improved global-best-driven flower pollination algorithm for optimal design of two-dimensional FIR filter. Soft Comput 23, 8855–8872 (2019). https://doi.org/10.1007/s00500-018-3484-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-018-3484-3

Keywords

Search

Navigation