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A hybrid moth flame optimization and variable neighbourhood search technique for optimal design of IIR filters

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Abstract

In this manuscript, a hybrid optimization technique, which integrates moth flame optimization (MFO) technique and variable neighbourhood search (VNS) heuristic, has been proposed to search the optimal coefficients of infinite impulse response (IIR) filter. The search process of MFO technique is based on the navigation method of the moths. The moth updates its position around the flame. In order to improve the search ability and convergence precision of MFO technique, the VNS heuristic has been integrated with it. In VNS heuristic, a random solution is generated around the neighbourhood of the best MFO solution. The random solution is updated by local search ‘Powell’s pattern search’ (PPS) method. The PPS method has excellent exploitation capability, which avoids any possible stagnation at local optimal solution. The proposed optimization technique has been applied on the benchmark functions and for the optimal design of five low-pass and six high-pass IIR filters. For low-pass filter (LPF) design problems 1–5, the proposed optimization technique is able to minimize the objective function by at least 50.78%, 205.72%, 122.36%, 20.48% and 28.76% more as compared to the results obtained by other state-of-the-art techniques, respectively. Hence, optimal IIR filter designed by the proposed optimization technique is able to achieve better desirable attributes, i.e. passband error, stopband error, square error, and stopband attenuation as compared to other state-of-the-art techniques.

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References

  1. Shynk JJ (1989) Adaptive IIR filtering. IEEE ASSP Mag 6:4–21

    Article  Google Scholar 

  2. Stearns SD (1981) Error surface of recursive adaptive filters. IEEE Trans Acoustics Speech Signal Process 29:763–766

    Article  MATH  Google Scholar 

  3. Radenkovic M, Bose T (2001) Adaptive IIR filtering of nonstationary signals. Signal Process 81:183–195

    Article  MATH  Google Scholar 

  4. Lutovac MD, Tosic DV, Evans BL (2001) Filter design for signal processing using Matlab and Mathematica. Prentice-Hall, Upper Saddle River, NJ

    Google Scholar 

  5. Chen S, Istepanian RH, Luk BL (2001) Digital IIR filter design using adaptive simulated annealing. Digit Signal Process 11:241–251

    Article  Google Scholar 

  6. Ng SC, Leung SH, Chung CY, Luk A, Lau WH (1996) The genetic search approach: a new learning algorithm for IIR filtering. IEEE Signal Process Mag 13:38–46

    Article  Google Scholar 

  7. Karaboga N, Cetinkaya B (2006) Design of digital FIR filters by using differential evolution algorithms. Circuits Syst Signal Process J 25:649–660

    Article  MATH  Google Scholar 

  8. Karaboga N (2009) A design method based on artificial bee colony algorithm for digital IIR filters. J Franklin I(346):328–348

    Article  MathSciNet  MATH  Google Scholar 

  9. Sarangi SK, Rutuparna P, Manoranjan D (2014) Design of 1-D and 2-D recursive filters using crossover bacterial foraging and cuckoo search techniques. Eng Appl Artif Intel 34:109–121

    Article  Google Scholar 

  10. Agrawal N, Kumar A, Bajaj V (2018) Design of digital IIR filter with low quantization error using hybrid optimization technique. Soft Comput 22:2953–2971

    Article  Google Scholar 

  11. Zou DX, Deb S, Wang GG (2018) Solving IIR system identification by a variant of particle swarm optimization. Neural Comput Appl 30:685–698

    Article  Google Scholar 

  12. Peng H, Wang J (2017) A hybrid approach based on tissue P systems and artificial bee colony for IIR system identification. Neural Comput Applic 28:2675–2685

    Article  Google Scholar 

  13. Mohammadi A, Zahiri SH (2018) Inclined planes system optimization algorithm for IIR system identification. Int J Mach Learn Cyber 9:541–558

    Article  Google Scholar 

  14. Wang J, Shi P, Peng H (2016) Membrane computing model for IIR filter design. Inform Sci 329:164–176

    Article  Google Scholar 

  15. Upadhyay P, Kar R, Mandal D, Ghoshal SP (2016) A new design method based on firefly algorithm for IIR system identification problem. JKSUES 28:174–198

    Google Scholar 

  16. Upadhyay P, Kar R, Mandal D, Ghoshal SP (2014) Craziness based particle swarm optimization algorithm for IIR system identification problem. AEÜ- Int J Electron Commun 68(5):369–378

    Article  Google Scholar 

  17. Sanghvi RC, Soni HB (2016) Multi-objective IIR filter design using Non-dominated sorting genetic algorithm-II. Ind J Sci Technol 9(47):1–7

    Google Scholar 

  18. Sarangi A, Sarangi SK, Panigrahi SP (2016) An approach to identification of unknown IIR system using crossover cat swarm optimization. Perspectives Sci 8:301–303

    Article  Google Scholar 

  19. Dhaliwal KK, Dhillon JS. (2016) On the design and optimization of digital IIR filter using oppositional artificial bee colony algorithm, In: IEEE Students Conf. on Electrical, Electronics and Computer Science.

  20. Sharifi MA, Mojallali H (2015) A modified imperialist competitive algorithm for digital IIR filter design. Optik 126:2979–2984

    Article  Google Scholar 

  21. Mirjalili S (2015) Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowl-Based Syst 89:228–249

    Article  Google Scholar 

  22. Das A, Mandal D, Ghoshal SP, Kar R (2018) Concentric circular antenna array synthesis for side lobe suppression using moth flame optimization. AEÜ- Int J Electron Commun 86:177–184

    Article  Google Scholar 

  23. Allam D, Yousri DA, Eteiba MB (2016) Parameter extraction of the three diode model for the multi-crystalline solar cell/module using moth-flame optimization algorithm. Energy Convers Manag 123:535–548

    Article  Google Scholar 

  24. Wang G-G (2018) Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems. Memet Comput 10:151–164

    Article  Google Scholar 

  25. Luo Q, Yang X, Zhou Y (2019) Nature-inspired approach: an enhanced moth swarm algorithm for global optimization. Math Comput Simul 159:57–92

    Article  MathSciNet  MATH  Google Scholar 

  26. Kaur K, Singh U, Salgotra R (2020) An enhanced moth flame optimization. Neural Comput Applic 32:2315–2349

    Article  Google Scholar 

  27. Shehab M, Abualigah L, Al Hamad H, Alabool H, Alshinwan M, Khasawneh AM (2020) Moth-flame optimization algorithm: variants and applications. Neural Comput Applic 32:9859–9884

    Article  Google Scholar 

  28. Li R, Hu S, Wang Y, Yin M (2017) A local search algorithm with tabu strategy and perturbation mechanism for generalized vertex cover problem. Neural Comput Applic 28:1775–1785

    Article  Google Scholar 

  29. Zhou Y, Wang Y, Gao J, Luo N, Wang J (2018) An efficient local search for partial vertex cover problem. Neural Comput Applic 30:2245–2256

    Article  Google Scholar 

  30. Mladenović N, Hansen P (1997) Variable neighborhood search. Comput Ops Res 24(11):1097–1100

    Article  MathSciNet  MATH  Google Scholar 

  31. Mladenović N, Petrović J, Kovačević-Vujčić V, Čangalović M (2003) Solving spread spectrum radar polyphase code design problem by tabu search and variable neighbourhood search. Eur J Oper Res 151:389–399

    Article  MathSciNet  MATH  Google Scholar 

  32. Kalayci CB, Kaya C (2016) An ant colony system empowered variable neighbourhood search algorithm for the vehicle routing problem with simultaneous pickup and delivery. Expert Syst Appl 66:163–175

    Article  Google Scholar 

  33. Sicilia JA, Quemada C, Royo B, Escuín D (2016) An optimization algorithm for solving the rich vehicle routing problem based on variable neighbourhood search and tabu search metaheuristic. J Comput Appl Math 291:468–477

    Article  MathSciNet  MATH  Google Scholar 

  34. Mladenović N, Todosijević R, Urošević D (2016) Less is more: Basic variable neighbourhood search for minimum differential dispersion problem. Inf Sci 326:160–171

    Article  Google Scholar 

  35. Zhao F, Liu Y, Zhang Y, Ma W, Zhang C (2017) A hybrid harmony search algorithm with efficient job sequence scheme and variable neighbourhood search for the permutation flow shop scheduling problems. Eng Appl Artif Intell 65:178–199

    Article  Google Scholar 

  36. Marinakis Y, Migdalas A, Sifaleras A (2017) A hybrid particle swarm optimization-variable neighbourhood search algorithm for constrained shortest path problems. Eur J Oper Res 261:819–834

    Article  MATH  Google Scholar 

  37. Li X, Gao L, Pan Q, Wan L, Chao K-M (2019) An effective hybrid genetic algorithm and variable neighbourhood search for integrated process planning and scheduling in a packing machine workshop. IEEE Trans Syst Man Cybern Syst 49(10):1933–1945

    Article  Google Scholar 

  38. Narang N, Dhillon JS, Kothari DP (2012) Multiobjective fixed head hydrothermal scheduling using integrated predator-prey optimization and Powell search method. Energy 47:237–252

    Article  Google Scholar 

  39. Singh N, Dhillon JS, Kothari DP (2018) Multi-objective thermal power load dispatch using chaotic differential evolutionary algorithm and Powell’s method. Soft Comput 22:2159–2174

    Article  Google Scholar 

  40. Saha SK, Kar R, Mandal D, Ghoshal SP (2011) IIR filter design with craziness based particle swarm optimization technique. Int J Elect Compu Ener Elect Comm Eng 5(12):1810–1817

    Google Scholar 

  41. Gaston KJ, Bennie J, Davies TW, Hopkins J (2013) The ecological impacts of night time light pollution: a mechanistic appraisal. Biol Rev 88:912–917

    Article  Google Scholar 

  42. DraŽić M, Lavor C, Maculan N, Mladenović N (2008) A continuous variable neighbourhood search heuristic for finding the three-dimensional structure of a molecule. Eur J Oper Res 185:1265–1273

    Article  MATH  Google Scholar 

  43. Kumar M, Dhillon JS (2019) A conglomerated ion-motion and crisscross search optimizer for electric power load dispatch. Appl Soft Comput 83:1–21

    Article  Google Scholar 

  44. Storn R, Price K (1997) Differential evolution-a simple and efficient heuristic for global optimization over continuous space. J Glob Optim 4(11):341–359

    Article  MathSciNet  MATH  Google Scholar 

  45. Venkata R, 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 

  46. Venkata Rao R, Patel V (2013) An improved teaching-learning-based optimization algorithm for solving unconstrained optimization problems. Sharif Uni Technol 20(3):710–720

    Google Scholar 

  47. Mohanty B, Panda S, Hota PK (2014) Controller parameters tuning of differential evolution algorithm and its application to load frequency control of multi-source power system. Elect Pow Ener Syst 54:77–85

    Article  Google Scholar 

  48. Segundo EHV, Mariania VC, Coelhob LS (2019) Design of heat exchangers using falcon optimization algorithm. Appl Therm Engg 156:119–144

    Article  Google Scholar 

  49. Saha SK, Kar R, Mandal D, Ghoshal SP. (2012) Digital stable IIR low pass filter optimization using PSO-CFIWA, In: 1st Int conf recent advances in information technology; 196–201.

  50. Montgomery D (2012) Design and analysis of Experiments. Wiley, UK

    Google Scholar 

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  1. Department of Electronics and Communication Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, 147004, India

    Teena Mittal

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  1. Teena Mittal
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Mittal, T. A hybrid moth flame optimization and variable neighbourhood search technique for optimal design of IIR filters. Neural Comput & Applic 34, 689–704 (2022). https://doi.org/10.1007/s00521-021-06379-8

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