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

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 s...

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Vydáno v:Journal of intelligent manufacturing Ročník 34; číslo 8; s. 3523 - 3561
Hlavní autoři: Abualigah, Laith, Elaziz, Mohamed Abd, Yousri, Dalia, Al-qaness, Mohammed A. A., Ewees, Ahmed A., Zitar, Raed Abu
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.12.2023
Springer Nature B.V
Springer Verlag (Germany)
Témata:
Advanced manufacturing technologies
Algorithms
Arithmetic
Artificial Intelligence
Business and Management
Clustering
Computer Science
Control
Global optimization
Heuristic methods
Machine learning
Machines
Manufacturing
Mechatronics
Optimization algorithms
Processes
Production
Robotics
Search process
Task complexity
Arithmetic optimization algorithm (AOA)
Opposition-based learning (OBL)
Global optimization
Lévy flight (LF)
Data clustering
ISSN:0956-5515, 1572-8145
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Shrnutí: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.
Bibliografie:ObjectType-Article-1
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ISSN:0956-5515
1572-8145
DOI:10.1007/s10845-022-02016-w