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Design of optimal low-pass filter by a new Levy swallow swarm algorithm

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Abstract

The swallow swarm optimization (SS) is a challenging method of optimization, which has a quicker convergence speed, not getting caught in the local extreme points. However, the SS suffers from a few shortcomings—(1) the movement speed of particles is not controlled suitably during the search due to the requirement of an inertia weight and (2) the less flexibility of variables does not permit to maintain a balance between the local and the global searches. To solve these problems, a new Levy swallow swarm optimization (SSLY) algorithm with the exploitation capability is proposed. This article also provides an optimal design methodology for the low-pass filter using the suggested SSLY technique. A new objective function is introduced to achieve the maximally flat frequency response, which is another important contribution to the field. The firefly algorithm (FA), the sine cosine algorithm (SCA) and the standard global optimizers—real coded genetic algorithm (GA), conventional particle swarm optimization (PSO), cuckoo search (CS) and SS, are considered for a comparison. The proposed SSLY outperforms the FA, SCA, GA, PSO, CS and SS algorithms. Results authenticate suitability of the proposed algorithm for solving the filter design problems in the FIR domain.

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References

  • Ababneh JI, Bataineh MH (2008) Linear phase FIR filter design using particle swarm optimization and genetic algorithms. Digit Signal Process 18(4):657–668

    Article  Google Scholar 

  • Aggarwal A, Rawat TK, Upadhyay DK (2016) Design of optimal digital FIR filter using evolutionary and swarm optimization techniques. AEU-Int J Electron Commun 70(4):373–385

    Article  Google Scholar 

  • Ahmad SU, Andreas A (2006) Cascade-form-multiplierless FIR filters design using orthogonal genetic algorithm. In: IEEE international symposium on signal processing and information technology, pp 932–7

  • Ahmad SU, Antoniou A (2006) A genetic algorithm approach for fractional delay FIR filters. IEEE Int Symp Circuits Syst ISCAS 2006:2517–2520

    Google Scholar 

  • Chen T, Wang Q, Liu HL (2018) FIR digital filter design based on evolutionary multi-objective algorithm. In: 14th international conference on computational intelligence and security (CIS). IEEE, pp 349–352

  • Dash J, Dam B, Swain R (2020) Design and implementation of sharp edge FIR filters using hybrid differential evolution particle swarm optimization. AEU-Int J Electron Commun 114(153019):1–16

    Google Scholar 

  • Deng L, Sun H, Zhang L (2019) A new algorithm (ESA-DE) for designing FIR digital filters. In: International conference on wireless and satellite systems, pp 640–652

  • Dhabal S, Chakraborty N, Mukherjee A, Biswas J (2016) Design of higher order low pass filter using cuckoo search algorithm. In: International conference on communication and signal processing, pp 1036–1042

  • Dwivedi AK, Ghosh S, Londhe ND (2018) Review and analysis of evolutionary optimization-based techniques for FIR filter design. Circuits, Syst Signal Process 37(10):4409–4430

    Article  Google Scholar 

  • Fang W, Sun J, Xu W, Liu J (2006) FIR digital filters design based on quantum-behaved Particle Swarm Optimization. In: First international conference on innovative computing, information and control, ICICIC ‘06, vol 1, pp 615–619

  • Hussain ZM, Sadik AZ, O’Shea P (2011) Digital signal processing: an introduction with MATLAB applications. Springer, Berlin

    MATH  Google Scholar 

  • Ji D (2016) The application of artificial bee colony (ABC) algorithm in FIR filter design. In: 12th international conference on natural computation, fuzzy systems and knowledge discovery (ICNC-FSKD), pp 663–667

  • Jiménez-Galindo D, Casaseca-de-la-Higuera P, San-José-Revuelta LM (2019) A novel design method for digital FIR/IIR filters based on the shuffle frog-leaping algorithm. In: 2019 27th European signal processing conference (EUSIPCO), pp 1–5

  • Karaboga N (2009) A new design method based on artificial bee colony algorithm for digital IIR filters. J Frankl Inst 346(4):328–348

    Article  MathSciNet  Google Scholar 

  • Karaboga N, Cetinkaya B (2006) Design of digital FIR filters using differential evolution algorithm. Circuits Syst Signal Process 25(5):649–660

    Article  MathSciNet  Google Scholar 

  • Karaboga N, Cetinkaya MH (2011) An overly and efficient algorithm for adaptive filtering: artificial bee colony algorithm. Turk J Electr Eng Comp Sci 19(1):175–190

    Google Scholar 

  • Kennedy J, Eberhart R (1995) Particle swarm optimization. Proc IEEE Int Conf Neural Netw 4:1942–1948

    Article  Google Scholar 

  • Kwan HK (2017) Asymmetric filter design using evolutionary optimization. In: IEEE 30th Canadian conference on electrical and conference engineering, pp 1–4

  • Liang J, Kwan HK (2017) FIR filter design using multiobjective cuckoo search algorithm. In: IEEE 30th Canadian conference on electrical and computer engineering (CCECE). IEEE, pp 1–4

  • Mandal S, Ghoshal SP, Kar R, Mandal D (2011) Optimal linear phase FIR band pass filter design using craziness based Particle Swarm Optimization Algorithm. J Shanghai Iaotong Univ (Sci) 16(6):696–703

    Article  Google Scholar 

  • Mandal S, Ghosal SP, Kar R, Mandal D (2012) Design of optimal linear phase FIR high pass filter using craziness based particle swarm optimization. J King South Univ 24:83–92

    Google Scholar 

  • Mantegna RN (1994) Fast, accurate algorithm for numerical simulation of Levy stable stochastic processes. Phys Rev 49(5):4683–4977

    Google Scholar 

  • Mastorakis NE, Gonos IF, Swamy MNS (2003) Design of two-dimensional recursive filters using genetic algorithm. IEEE Trans Circuits Syst I Fundam Theory Appl 50:634–639

    Article  Google Scholar 

  • Mirjalili S (2016) SCA: a sine cosine algorithm for solving optimization problems. Knowl-Based Syst 96:120–133

    Article  Google Scholar 

  • Mukherjee A, Chakraborty N, Das BK (2017) Whale optimization algorithm: An implementation to low pass FIR filter. In: International conference on innovations in power and advanced computing technologies [i-PACT2017], pp 1–5

  • Neshat M, Sepidnam G, Sargolzaei M (2012) Swallow swarm optimization algorithm: a new method to optimization. Neural Comput Appl 2012(23):429–454

    Google Scholar 

  • Panda R, Agrawal S, Bhuyan S (2013) Edge magnitude based multilevel thresholding using Cuckoo search technique. Expert Syst Appl 40:7617–7628

    Article  Google Scholar 

  • Parks TW, Burrus CS (1987) Digital filter design. Wiley, New York

    MATH  Google Scholar 

  • Parks TW, McClellan JH (1972) Chebyshev approximation for non-recursive digital filters with linear phase. IEEE Trans Circuit Theory CT-19:189–194

    Article  Google Scholar 

  • Rana KPS, Kumar V, Nair SS (2016) Efficient FIR filter designs using constrained genetic algorithms based optimization. In: IEEE 2nd international conference on communication, control and intelligent systems (CCIS), pp 1–5

  • Rashedi E, Hossien N, Saryazdi S (2011) Filter modelling using gravitational search algorithm. Eng Appl Artif Intell 24(1):117–122

    Article  Google Scholar 

  • Ravi RV, Subramaniam K, Roshini TV, Muthusamy SPB, Venkatesan GP (2019) Optimization algorithms, an effective tool for the design of digital filters: a review. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-019-01431-x

  • Saha SK, Ghosal SP, Kar R, Mandal D (2013) Cat swarm optimization algorithm for optimal linear phase FIR design. ISA Trans 52:1–14

    Article  Google Scholar 

  • San-Jos´e-Revuelta M (2018) Design of optimal frequency-selective FIR filters using a memetic algorithm Luis. In: 26th European signal processing conference (EUSIPCO), pp 1172–1176

  • 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 

  • Sarangi SK, Panda R, Das PK, Abraham A (2018) Design of optimal high pass and band stop FIR filters using adaptive Cuckoo search algorithm. Eng Appl Artif Intell 70:67–80

    Article  Google Scholar 

  • Yang XS (2009) Firefly algorithms for multimodal optimization. In: Proceedings of the 5th international conference on stochastic algorithms foundations and applications, LNCS Springer, vol 5792, pp 169–178

  • Yang XS, Deb S (2009) Cuckoo search via levy flights. In: World congress on nature & biologically inspired computing, pp 210–214

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

    MATH  Google Scholar 

  • Zhang M, Kwan HK (2017) FIR filter design using multiobjective teaching-learning-based optimization. In: IEEE 30th Canadian conference on electrical and computer engineering (CCECE), pp 1–3

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Authors and Affiliations

  1. Department of Electronics and Instrumentation Engineering, I.T.E.R, SOA Deemed to be University, Bhubaneswar, India

    Shubhendu Kumar Sarangi

  2. Department of Electronics and Telecommunication Engineering, Veer Surendra Sai University of Technology, Burla, India

    Rutuparna Panda

  3. MIR Labs, Washington, USA

    Ajith Abraham

Authors
  1. Shubhendu Kumar Sarangi
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  2. Rutuparna Panda
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  3. Ajith Abraham
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Correspondence to Rutuparna Panda.

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Communicated by V. Loia.

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Sarangi, S.K., Panda, R. & Abraham, A. Design of optimal low-pass filter by a new Levy swallow swarm algorithm. Soft Comput 24, 18113–18128 (2020). https://doi.org/10.1007/s00500-020-05065-6

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