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Evolutionary design model of passive filter circuit for practical application

  • Jingsong HeEmail author
  • Jin Yin
Article
  • 26 Downloads

Abstract

Evolutionary circuit design is a promising way to study new circuit design methodologies, and the passive filter is the most basic circuit module widely existing in modern electronic systems. Focused on the basic and fatal criterion related to the filter circuit design, this paper presents a novel evolutionary design model of passive filter circuit. The proposed model includes a circuit representation method for passive filter circuit design based on circuit cells and the corresponding real encoding scheme, a fast fitness calculation method avoiding expensive SPICE simulations, and a simple and effective cell-based differential evolution algorithm. Experimental results show that the proposed model can quickly obtain filter circuits for challenging specifications. Under harsh design criteria, the design performance of the proposed model is not inferior to that of some advanced professional design techniques based on traditional design ideas.

Keywords

Evolutionary circuit design Analog circuit synthesis Differential evolution Neighborhood model 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China through Grant No. 61273315. The authors thank the anonymous reviewers for their comments, which have helped to improve the quality of this paper.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. 1.
    J.R. Koza, F.H. Bennett III, D. Andre et al., Automated WYWIWYG design of both the topology and component values of analog electrical circuits using genetic programming, in The First Annual Conference on Genetic Programming, Stanford University, CA, USA, 1996, ed. by J.R. Koza (MIT Press, Massachusetts, 1996), pp. 123–131Google Scholar
  2. 2.
    J.R. Koza, F.H. Benett III, D. Andre et al., Automated synthesis of analog electrical circuits by mean of genetic programming. IEEE Trans. Evol. Comput. 1(2), 109–128 (1997)CrossRefGoogle Scholar
  3. 3.
    J.B. Grimbleby, Automatic analogue network synthesis using genetic algorithms, in The First Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, Sheffield, UK (1995), pp. 53–58Google Scholar
  4. 4.
    J.D. Lohn, S.P. Colombano, A circuit representation technique for automated circuit design. IEEE Trans. Evol. Comput. 3(3), 205–219 (1999)CrossRefGoogle Scholar
  5. 5.
    D. Bose, S. Biswas, A.V. Vasilakos, S. Laha, Optimal filter design using an improved artificial bee colony algorithm. Inf. Sci. 281, 443–461 (2014)MathSciNetCrossRefGoogle Scholar
  6. 6.
    F. Viani, F. Robol, M. Salucci, R. Azaro, Automatic EMI filter design through particle swarm optimization. IEEE Trans. Electromagn. Compat. 59(4), 1079–1094 (2017)CrossRefGoogle Scholar
  7. 7.
    A.J. Hirst, Notes on the evolution of adaptive hardware, in Proceeding of Second International Conference on Adaptive Computing in Engineering Design and Control, ed. by I. Parmee (University of Plymouth Press, Plymouth, 1996), pp. 212–219Google Scholar
  8. 8.
    X. Yao, T. Higuchi, Promises and challenges of evolvable hardware. IEEE Trans. Syst. Man Cybern. Part C 29(1), 87–97 (1999)CrossRefGoogle Scholar
  9. 9.
    A. Thompson, P. Layzell, R.S. Zembulum, Explorations in design space: unconventional electronics design through artificial evolution. IEEE Trans. Evol. Comput. 3(3), 167–196 (1999)CrossRefGoogle Scholar
  10. 10.
    J. He, X. Wang, M. Zhang et al., New research on scalability of lossless image compression by GP engine, in Proceedings—NASA/DoD Conference on Evolvable Hardware, vol. 2005 (2005), pp. 160–164Google Scholar
  11. 11.
    J. He, X. Yao, Y. Chen, A novel and practicable on-chip adaptive lossless image compression scheme using intrinsic evolvable hardware. Connect. Sci. 19(4), 281–295 (2007)CrossRefGoogle Scholar
  12. 12.
    M. Liu, J. He, An evolutionary negative-correlation framework for robust analog-circuit design under uncertain faults. IEEE Trans. Evol. Comput. 17(5), 640–665 (2013)CrossRefGoogle Scholar
  13. 13.
    Z. Li, J. He, The extension of linear coding method for automated analog circuit design, in Advances in Swarm Intelligence. Lecture Notes in Computer Science, vol. 7929, LNCS, No. 2, ed. by Y. Tan, Y. Shi, H. Mo (Springer, Berlin, 2013), pp. 480–487CrossRefGoogle Scholar
  14. 14.
    X. Zhang, P. Xia, J. He, Distributed computation framework for circuit evolutionary design under CS network architecture, in 18th IEEE International Conference on Communication Technology Proceedings, Chongqing, China (2018), pp. 232–236Google Scholar
  15. 15.
    M. Yasunaga, I. Yoshihara, An evolutionary design methodology of printed circuit boards for high-speed VLSIs. Artif. Life Robot. 21(2), 171–176 (2016)CrossRefGoogle Scholar
  16. 16.
    M. Sikulova, G. Komjathy, L. Sekanina, Towards compositional coevolution in evolutionary circuit design, in 2014 IEEE International Conference on Evolvable Systems (2014), pp. 157–164Google Scholar
  17. 17.
    D. Grochol, L. Sekanina, M. Zadnik, J. Korenek, V. Kosar, Evolutionary circuit design for fast FPGA-based classification of network application protocols. Appl. Soft Comput. 38, 933–941 (2016)CrossRefGoogle Scholar
  18. 18.
    Z. Vasicek, L. Sekanina, Evolutionary design of complex approximate combinational circuits. Genet. Program. Evolvable Mach. 17(2), 169–192 (2016)CrossRefGoogle Scholar
  19. 19.
    I. Canturk, N. Kahraman, Comparative analog circuit design automation based on multi-objective evolutionary algorithms: an application on CMOS opamp, in 38th International Conference on Telecommunications and Signal Processing (2015)Google Scholar
  20. 20.
    V. Mrazek, Z. Vasicek, R. Hrbacek, Role of circuit representation in evolutionary design of energy-efficient approximate circuits. IET Comput. Digit. Tech. 12(4), 139–149 (2018)CrossRefGoogle Scholar
  21. 21.
    B.N. Thakkar, V.H. Nayak, Automatic design of low power CMOS buffer-chain circuit using differential evolutionary algorithm and particle swarm optimization, in 2017 International Conference on Algorithms, Methodology, Models and Applications in Emerging Technologies (2017), pp. 1–5Google Scholar
  22. 22.
    R. Storn, K. Price, Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(4), 341–359 (1997)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Y.-J. Gong, W.-N. Chen, Z.-H. Zhan et al., Distributed evolutionary algorithms and their models: a survey of the state-of-the-art. Appl. Soft Comput. 34, 286–300 (2015)CrossRefGoogle Scholar
  24. 24.
    C. Goh, Y. Li, GA automated design and synthesis of analog circuits with practical constraints, in 2001 IEEE Congress on Evolutionary Computation, vol. 1 (2001), pp. 170–177Google Scholar
  25. 25.
    A. Das, R. Vemuri, GAPSYS: a GA-based tool for automated passive analog circuit synthesis, in IEEE International Symposium on Circuits and Systems, 2007. ISCAS 2007 (2007), pp. 2702–2705Google Scholar
  26. 26.
    Y. Sapargaliyev, T. Kalganova, Constrained and unconstrained evolution of “lcr” low-pass filters with oscillating length representation, in IEEE Congress on Evolutionary Computation, 2006. CEC 2006 (2006), pp. 1529–1536Google Scholar
  27. 27.
    S.-J. Chang, H.-S. Hou, Y.-K. Su, Automated passive filter synthesis using a novel tree representation and genetic programming. IEEE Trans. Evol. Comput. 10(1), 93–100 (2006)CrossRefGoogle Scholar
  28. 28.
    Z. Gan, Z. Yang, G. Li, M. Jiang, Automatic synthesis of practical passive filters using clonal selection principle-based gene expression programming, in International Conference on Evolvable Systems (Springer, Berlin, 2007), pp. 89–99CrossRefGoogle Scholar
  29. 29.
    A.F. Sheta, Analogue filter design using differential evolution. Int. J. Bio-Inspired Comput. 2(3–4), 233–241 (2010)CrossRefGoogle Scholar
  30. 30.
    O. Verducci Jr, P.C. Crepaldi, L.B. Zoccal, T.C. Pimenta, Synthesis of passive filter using object oriented genetic algorithm, in 2014 26th International Conference on Microelectronics (ICM). IEEE (2014), pp. 72–75Google Scholar
  31. 31.
    O.J. Ushie, M.F. Abbod, J.C. Ogbulezie, The use of genetic programming to evolve passive filter circuits. Int. J. Eng. Technol. Innov. 7(4), 255–268 (2017)Google Scholar
  32. 32.
    M.D. Lutovac, D.V. Tošić, B.L. Evans, Filter Design for Signal Processing Using MATLAB and Mathematica (Prentice Hall, Upper Saddle River, 2001)Google Scholar
  33. 33.
    J. Brest, S. Greiner, B. Boskovic, M. Mernik, V. Zumer, Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Trans. Evol. Comput. 10(6), 646–657 (2006)CrossRefGoogle Scholar
  34. 34.
    M. Weber, F. Neri, V. Tirronen, A study on scale factor/crossover interaction in distributed differential evolution. Artif. Intell. Rev. 39(3), 195–224 (2013)CrossRefGoogle Scholar
  35. 35.
    C. Segura, C.A. Coello Coello, E. Segredo et al., On the adaptation of the mutation scale factor in differential evolution. Optim. Lett. 9(1), 189–198 (2015)MathSciNetCrossRefGoogle Scholar
  36. 36.
    X. Yu et al., Differential evolution mutation operators for constrained multi-objective optimization. Appl. Soft Comput. 67, 452–466 (2018)CrossRefGoogle Scholar
  37. 37.
    M. Dubreuil, C. Gagné, M. Parizeau, Analysis of a master–slave architecturefor distributed evolutionary computations. IEEE Trans. Syst. Man Cybern. B Cybern. 36(1), 229–235 (2006)CrossRefGoogle Scholar
  38. 38.
    F. Herrera, M. Lozano, Gradual distributed real-coded genetic algorithms. IEEE Trans. Evol. Comput. 4(1), 43–63 (2000)CrossRefGoogle Scholar
  39. 39.
    H. Pierreval, J.-L. Paris, Distributed evolutionary algorithms for simulation optimization. IEEE Trans. Syst. Man Cybern. A Syst. Hum. 30(1), 15–24 (2000)CrossRefGoogle Scholar
  40. 40.
    E. Alba, B. Dorronsoro, The exploration/exploitation tradeoff in dynamic cellular genetic algorithms. IEEE Trans. Evol. Comput. 9(2), 126–142 (2005)CrossRefGoogle Scholar
  41. 41.
    M. Giacobini, M. Tomassini, A.G. Tettamanzi, E. Alba, Selection intensity in cellular evolutionary algorithms for regular lattices. IEEE Trans. Evol. Comput. 9(5), 489–505 (2005)CrossRefGoogle Scholar
  42. 42.
    G. Folino, C. Pizzuti, G. Spezzano, Training distributed GP ensemble with aselective algorithm based on clustering and pruning for pattern classification. IEEE Trans. Evol. Comput. 12(4), 458–468 (2008)CrossRefGoogle Scholar
  43. 43.
    G. Roy, H. Lee, J.L. Welch, et al., A distributed pool architecture for genetic algorithms, in IEEE Congress on Evolutionary Computation (CEC) (2009), pp. 1177–1184Google Scholar
  44. 44.
    S. Ramírez-Gallego et al., A distributed evolutionary multivariate discretizer for big data processing on apache spark. Swarm Evol. Comput. 38, 240–250 (2017)CrossRefGoogle Scholar
  45. 45.
    Y.F. Ge et al., Distributed differential evolution based on adaptive mergence and split for large-scale optimization. IEEE Trans. Cybern. 48(7), 2166–2180 (2018)CrossRefGoogle Scholar
  46. 46.
    R.A. Vural, T. Yildirim, T. Kadioglu, A. Basargan, Performance evaluation of evolutionary algorithms for optimal filter design. IEEE Trans. Evol. Comput. 16(1), 135–147 (2012)CrossRefGoogle Scholar
  47. 47.
    Y. Kumar, G.K. Malik, Performance analysis of different filters for power line interface reduction in ECG signal. Int. J. Comput. Appl. 3(7), 1–6 (2010)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of MicroelectronicsUniversity of Science and Technology of ChinaHefeiChina

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