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Augmented arithmetic optimization algorithm using opposite-based learning and lévy flight distribution for global optimization and data clustering

Author

Listed:
  • Laith Abualigah

    (Al-Ahliyya Amman University
    Middle East University)

  • Mohamed Abd Elaziz

    (Galala University
    Ajman University
    Zagazig University
    Tomsk Polytechnic University)

  • Dalia Yousri

    (Fayoum University)

  • Mohammed A. A. Al-qaness

    (Zhejiang Normal University
    Sana’a University)

  • Ahmed A. Ewees

    (University of Bisha
    Damietta University)

  • Raed Abu Zitar

    (Sorbonne University-Abu Dhabi)

Abstract

This paper proposes a new data clustering method using the advantages of metaheuristic (MH) optimization algorithms. A novel MH optimization algorithm, called arithmetic optimization algorithm (AOA), was proposed to address complex optimization tasks. Math operations inspire the AOA, and it showed significant performance in dealing with different optimization problems. However, the traditional AOA faces some limitations in its search process. Thus, we develop a new variant of the AOA, namely, Augmented AOA (AAOA), integrated with the opposition-based learning (OLB) and Lévy flight (LF) distribution. The main idea of applying OLB and LF is to improve the traditional AOA exploration and exploitation trends in order to find the best clusters. To evaluate the AAOA, we implemented extensive experiments using twenty-three well-known benchmark functions and eight data clustering datasets. We also evaluated the proposed AAOA with extensive comparisons to different optimization algorithms. The outcomes verified the superiority of the AAOA over the traditional AOA and several MH optimization algorithms. Overall, the applications of the LF and OLB have a significant impact on the performance of the conventional AOA.

Suggested Citation

  • Laith Abualigah & Mohamed Abd Elaziz & Dalia Yousri & Mohammed A. A. Al-qaness & Ahmed A. Ewees & Raed Abu Zitar, 2023. "Augmented arithmetic optimization algorithm using opposite-based learning and lévy flight distribution for global optimization and data clustering," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3523-3561, December.
  • Handle: RePEc:spr:joinma:v:34:y:2023:i:8:d:10.1007_s10845-022-02016-w
    DOI: 10.1007/s10845-022-02016-w
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    References listed on IDEAS

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    1. Elaziz, Mohamed Abd & Ewees, Ahmed A. & Ibrahim, Rehab Ali & Lu, Songfeng, 2020. "Opposition-based moth-flame optimization improved by differential evolution for feature selection," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 168(C), pages 48-75.
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