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

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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.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant No. 62150410434) and in part by LIESMARS Special Research Funding

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Authors and Affiliations

  1. Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 19328, Jordan

    Laith Abualigah

  2. Faculty of Information Technology, Middle East University, Amman, 11831, Jordan

    Laith Abualigah

  3. Faculty of Computer Science and Engineering, Galala University, Suez, 435611, Egypt

    Mohamed Abd Elaziz

  4. Artificial Intelligence Research Center (AIRC), Ajman University, Ajman, 346, UAE

    Mohamed Abd Elaziz

  5. Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, 44519, Egypt

    Mohamed Abd Elaziz

  6. School of Computer Science and Robotics, Tomsk Polytechnic University, Tomsk, 634050, Russia

    Mohamed Abd Elaziz

  7. Department of Electrical Engineering, Faculty of Engineering, Fayoum University, Fayoum, Egypt

    Dalia Yousri

  8. College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua, 321004, China

    Mohammed A. A. Al-qaness

  9. Faculty of Engineering, Sana’a University, Sana’a, 12544, Yemen

    Mohammed A. A. Al-qaness

  10. Department of Information Systems, College of Computing and Information Technology, University of Bisha, Bisha, 61922, Saudi Arabia

    Ahmed A. Ewees

  11. Department of Computer, Damietta University, Damietta, 34517, Egypt

    Ahmed A. Ewees

  12. Sorbonne Center of Artificial Intelligence, Sorbonne University-Abu Dhabi, Abu Dhabi, 38044, UAE

    Raed Abu Zitar

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  1. Laith Abualigah
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  2. Mohamed Abd Elaziz
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  4. Mohammed A. A. Al-qaness
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Abualigah, L., Elaziz, M.A., Yousri, D. et al. Augmented arithmetic optimization algorithm using opposite-based learning and lévy flight distribution for global optimization and data clustering. J Intell Manuf 34, 3523–3561 (2023). https://doi.org/10.1007/s10845-022-02016-w

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