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Improved Alopex-based evolutionary algorithm by Gaussian copula estimation of distribution algorithm and its application to the Butterworth filter design

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Listed:
  • Yihang Yang
  • Xiang Cheng
  • Junrui Cheng
  • Da Jiang
  • Shaojun Li

Abstract

The application of evolutionary algorithms (EAs) is becoming widespread in engineering optimisation problems because of their simplicity and effectiveness. The Alopex-based evolutionary algorithm (AEA) possesses the basic characteristics of heuristic search algorithms but is lacking in adequate information about the fitness landscape of the input domain, reducing the convergence speed. To improve the performance of AEA, the Gaussian copula estimation of distribution algorithm (EDA) is embedded into the original AEA in this paper. With the help of Gaussian copula EDA, precise probability models are built utilising the best solutions, which can increase the convergence speed, and at the same time, keep the population diversity as much as possible. The simulation results on the benchmark functions and the application to the Butterworth filter design demonstrate the efficiency and effectiveness of the proposed algorithm, compared with several other EAs.

Suggested Citation

  • Yihang Yang & Xiang Cheng & Junrui Cheng & Da Jiang & Shaojun Li, 2018. "Improved Alopex-based evolutionary algorithm by Gaussian copula estimation of distribution algorithm and its application to the Butterworth filter design," International Journal of Systems Science, Taylor & Francis Journals, vol. 49(1), pages 160-178, January.
    • Handle: RePEc:taf:tsysxx:v:49:y:2018:i:1:p:160-178
    DOI: 10.1080/00207721.2017.1390702
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    References listed on IDEAS

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    1. Robert T. Clemen & Terence Reilly, 1999. "Correlations and Copulas for Decision and Risk Analysis," Management Science, INFORMS, vol. 45(2), pages 208-224, February.
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