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

Laith Abualigah (), Mohamed Abd Elaziz, Dalia Yousri, Mohammed A. A. Al-qaness, Ahmed A. Ewees and Raed Abu Zitar
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Laith Abualigah: Al-Ahliyya Amman University
Mohamed Abd Elaziz: Galala University
Dalia Yousri: Fayoum University
Mohammed A. A. Al-qaness: Zhejiang Normal University
Ahmed A. Ewees: University of Bisha
Raed Abu Zitar: Sorbonne University-Abu Dhabi

Journal of Intelligent Manufacturing, 2023, vol. 34, issue 8, No 15, 3523-3561

Abstract: 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.

Keywords: Data clustering; Global optimization; Arithmetic optimization algorithm (AOA); Lévy flight (LF); Opposition-based learning (OBL) (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10845-022-02016-w

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