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Two-Dimensional IIR Filter Design Using Simulated Annealing Based Particle Swarm Optimization

Author

Listed:
  • Supriya Dhabal
  • Palaniandavar Venkateswaran

Abstract

We present a novel hybrid algorithm based on particle swarm optimization (PSO) and simulated annealing (SA) for the design of two-dimensional recursive digital filters. The proposed method, known as SA-PSO, integrates the global search ability of PSO with the local search ability of SA and offsets the weakness of each other. The acceptance criterion of Metropolis is included in the basic algorithm of PSO to increase the swarm’s diversity by accepting sometimes weaker solutions also. The experimental results reveal that the performance of the optimal filter designed by the proposed SA-PSO method is improved. Further, the convergence behavior as well as optimization accuracy of proposed method has been improved significantly and computational time is also reduced. In addition, the proposed SA-PSO method also produces the best optimal solution with lower mean and variance which indicates that the algorithm can be used more efficiently in realizing two-dimensional digital filters.

Suggested Citation

  • Supriya Dhabal & Palaniandavar Venkateswaran, 2014. "Two-Dimensional IIR Filter Design Using Simulated Annealing Based Particle Swarm Optimization," Journal of Optimization, Hindawi, vol. 2014, pages 1-10, September.
    • Handle: RePEc:hin:jjopti:239721
    DOI: 10.1155/2014/239721
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    References listed on IDEAS

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    1. L. Ingber, 1996. "Adaptive simulated annealing (ASA): Lessons learned," Lester Ingber Papers 96as, Lester Ingber.
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    Cited by:

    1. Omar Avalos & Erik Cuevas & Jorge Gálvez & Essam H. Houssein & Kashif Hussain, 2020. "Comparison of Circular Symmetric Low-Pass Digital IIR Filter Design Using Evolutionary Computation Techniques," Mathematics, MDPI, vol. 8(8), pages 1-22, July.

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