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An Improved Global-Best-Guided Cuckoo Search Algorithm for Multiplierless Design of Two-Dimensional IIR Filters

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Abstract

Cuckoo search algorithm (CSA) is relatively a new optimization technique with less control parameters and strong exploration ability. Due to the random search associated with CSA, it requires large number of functional evaluations for obtaining optimal solution. An improved algorithm, named as improved global-best-guided CSA, is presented here based on the best solution of previous iteration for the optimal design of multiplierless two-dimensional recursive digital filters. The most important feature of the proposed algorithm is that it is completely self-adaptive with no tuning parameters, whereas in CSA the replacement factor needs to be adjusted. The proposed algorithm exhibits 52% improvement in fitness function evaluation (for p = 2) and the execution time is reduced by 56% in comparison with the existing algorithms. Further, the proposed algorithm has been tested for several benchmark problems and found to exhibit significant performance improvement.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Netaji Subhash Engineering College, Kolkata, West Bengal, 700152, India

    Supriya Dhabal

  2. Department of Electronics and Tele-Communication Engineering, Jadavpur University, Kolkata, West Bengal, 700032, India

    Palaniandavar Venkateswaran

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  1. Supriya Dhabal
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  2. Palaniandavar Venkateswaran
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Correspondence to Supriya Dhabal.

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Dhabal, S., Venkateswaran, P. An Improved Global-Best-Guided Cuckoo Search Algorithm for Multiplierless Design of Two-Dimensional IIR Filters. Circuits Syst Signal Process 38, 805–826 (2019). https://doi.org/10.1007/s00034-018-0886-5

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  • DOI: https://doi.org/10.1007/s00034-018-0886-5

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