The Aquila Optimizer (AO) is a newly proposed, highly capable metaheuristic algorithm based on the hunting and search behavior of the Aquila bird. However, the AO faces some challenges when dealing with high-dimensional optimization problems due to its narrow exploration capabilities and a tendency to converge prematurely to local optima, which can decrease its performance in complex scenarios. This paper presents a modified form of the previously proposed AO, the Locality Opposition-Based Learning Aquila Optimizer (LOBLAO), aimed at resolving such issues and improving the performance of tasks related to global optimization and data clustering in particular. The proposed LOBLAO incorporates two key advancements: the Opposition-Based Learning (OBL) strategy, which enhances solution diversity and balances exploration and exploitation, and the Mutation Search Strategy (MSS), which mitigates the risk of local optima and ensures robust exploration of the search space. Comprehensive experiments on benchmark test functions and data clustering problems demonstrate the efficacy of LOBLAO. The results reveal that LOBLAO outperforms the original AO and several state-of-the-art optimization algorithms, showcasing superior performance in tackling high-dimensional datasets. In particular, LOBLAO achieved the best average ranking of 1.625 across multiple clustering problems, underscoring its robustness and versatility. These findings highlight the significant potential of LOBLAO to solve diverse and challenging optimization problems, establishing it as a valuable tool for researchers and practitioners.

Centre for Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab 140401 India

Computer Science Department Al al Bayt University Mafraq 25113 Jordan

Computer Technologies Engineering Mazaya University College Nasiriyah Iraq

CSMIS Department Oman College of Management and Technology 320 Barka Oman

Department of Computer Science and Engineering College of Applied Studies and Community Service King Saud University 11362 Riyadh Saudi Arabia

Department of Computer Science Khazar University Baku Azerbaijan

Department of Mathematics Facility of Science The Hashemite University P O Box 330127 Zarqa 13133 Jordan

Faculty of Educational Sciences Al Ahliyya Amman University Amman 19328 Jordan

Faculty of Electrical Engineering and Computer Science VŠB Technical University of Ostrava 70800 Poruba Ostrava Czech Republic

Faculty of Engineering and Computing Liwa College Abu Dhabi United Arab Emirates

Faculty of Engineering and Information Technology University of Technology Sydney Ultimo NSW 2007 Australia

Faculty of Information Technology Jadara University Irbid 21110 Jordan

School of Engineering and Technology Sunway University Malaysia Petaling Jaya 27500 Malaysia

University Research and Innovation Center Óbuda University 1034 Budapest Hungary

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