State Variable Filter Design Using Improvised Particle Swarm Optimization Algorithm

  • Aakash Indoria
  • Varatharajan Varrun
  • Akshay
  • Murali Krishna Reddy
  • Tejaswi Sathyasai
  • Baskaran Anand
  • Nirmala M. Devi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 325)

Abstract

State variable filter design using particle swarm optimization algorithm proves to be better when compared to the conventional design method. It gives several solutions to the component values which are useful in designing the state variable filter. The automatic termination technique gives the best possible solution in lesser time. This technique has several advantages in terms of a quicker convergence rate and efficient computation toward the suitable output, where an added advantage gives the user a control over the output’s precision. The performance parameter here can be defined as the trade-off between the convergence time and accuracy of the resulting solution, which is determined by the precision value. The results also indicate that the solution with a predefined precision level can be obtained with the minimum number of iterations in minimum time.

Keywords

Particle swarm optimization algorithm Real-world optimization problems Complex optimization problems PSO algorithm 

References

  1. 1.
    J. Kennedy, R.C. Eberhart, Particle swarm optimization, in IEEE International Conference on Neural Network, Perth, Australia, vol. 4 (1995), pp. 1942–1948Google Scholar
  2. 2.
    J. Kennedy, R.C. Eberhart, Y.H. Shi, Swarm Intelligence (Morgan Kaufmann, San Mateo, 2001)Google Scholar
  3. 3.
    X.D. Li, A.P. Engelbrecht, Particle swarm optimization: an introduction and its recent developments, in Proceedings of Genetic Evolutionary Computation Conference (2007), pp. 3391–3414Google Scholar
  4. 4.
    R.A. Krohling, L. dos Santos Coelho, Coevolutionary particle swarm optimization using Gaussian distribution for solving constrained optimization problems. IEEE Trans. Syst. Man Cybern. B Cybern. 36(6), 1407–1416 (2006)Google Scholar
  5. 5.
    S.-Y. Ho, H.-S. Lin, W.-H. Liauh, S.-J. Ho, OPSO-orthogonal particle swarm optimization and its application to task assignment problems. IEEE Trans. Syst. Man Cybern. A Syst. Hum. 38(2), 288–298 (2008)Google Scholar
  6. 6.
    B. Liu, L. Wang, Y.H. Jin, An effective PSO-based memetic algorithm for flow shop scheduling. IEEE Trans. Syst. Man Cybern. B Cybern. 37(1), 18–27 (2007)Google Scholar
  7. 7.
    J.J. Liang, A.K. Qin, P.N. Suganthan, S. Baskar, Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans. Evol. Comput. 10(3), 281–295 (2006)CrossRefGoogle Scholar
  8. 8.
    Y. Shi, R.C. Eberhart, A modified particle swarm optimizer, in Proceedings of IEEE World Congress on Computational Intelligence (1998), pp. 69–73Google Scholar
  9. 9.
    Z.-H. Zhan, J. Zhang, Adaptive particle swarm optimization, in IEEE Trans. Syst. Man Cybern. Part B Cybern. 39(6) (2009)Google Scholar
  10. 10.
    T.-Y. Chen, T.-M. Chi, On the improvements of the particle swarm optimization algorithm. Adv. Eng. Softw. 41, 229–239 (2010)Google Scholar
  11. 11.
    I. Aakash, V. Varrun, Akshay, M.K. Reddy, T. Sathyasai, B. Anand, N.M. Devi, Improvisation of particle swarm optimization algorithm, in International Conference on Signal Processing and Integrated Networks (2014)Google Scholar
  12. 12.
    R.A. Vural, T. Yildirim T, State variable filter design using particle swarm optimization. in Symbolic and Numerical Methods, Modeling and Applications to Circuit Design (SM2ACD) (2010), pp. 1–4Google Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  • Aakash Indoria
    • 1
  • Varatharajan Varrun
    • 1
  • Akshay
    • 1
  • Murali Krishna Reddy
    • 1
  • Tejaswi Sathyasai
    • 1
  • Baskaran Anand
    • 1
  • Nirmala M. Devi
    • 1
  1. 1.Department of Electronics and Communication Engineering, Amrita School of EngineeringAmrita Vishwa VidyapeethamCoimbatoreIndia

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