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A novel hybrid of chimp with cuckoo search algorithm for the optimal designing of digital infinite impulse response filter using high-level synthesis

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Abstract

High-level synthesis of data paths in digital filters and model identification tasks are a most complicated optimization problem. Generally, the use of infinite impulse response (IIR) models for identification is chosen over their equivalent finite impulse response (FIR) models, because the former yield more perfect models of physical plants for real-world optimization problems. Additionally, infinite impulse response (IIR) model structures tend to make various multimodal error surfaces whose objective cost of the problems is significantly more difficult to minimize. For the solution of these types of issues, we needed more powerful methods that could resolve it perfectly. In this study, a novel hybrid variant of chimp and cuckoo search method has been developed for IIR related large-scale optimization issues, and it is known as chimp–cuckoo search algorithm (ChCS). The strength of the proposed method has been tested on the 23 standard functions and three types of infinite impulse response (IIR) models. Meanwhile, the numerical and statistical tabulated solutions of the ChCS method are also presented, showing the advantages of the proposed method over the competitors.

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  1. Department of Electronics & Communication Engg., Punjabi University, Patiala, Punjab, India

    Mandeep Kaur & Ranjit Kaur

  2. Department of Mathematics, Punjabi University, Patiala, Punjab, India

    Narinder Singh

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  1. Mandeep Kaur
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  2. Ranjit Kaur
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Correspondence to Narinder Singh.

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Appendix

Appendix

See Table 15.

Table 15 23 standard benchmark test functions
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Kaur, M., Kaur, R. & Singh, N. A novel hybrid of chimp with cuckoo search algorithm for the optimal designing of digital infinite impulse response filter using high-level synthesis. Soft Comput 26, 13843–13867 (2022). https://doi.org/10.1007/s00500-022-07410-3

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