A Comprehensive Survey of Aquila Optimizer: Theory, Variants, Hybridization, and Applications A Comprehensive Survey of Aquila Optimizer: Theory, Variants, Hybridization, and Applications

The Aquila Optimizer (AO) algorithm is a well-known Swarm-based nature-inspired optimization algorithm inspired by Aquila’s behavior in hunting and catching prey. Since its development by Abualigah et al. (Comput Methods Appl Mech Eng 376:113609, 2021), AO has gained significant interest among resea...

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Vydané v:Archives of computational methods in engineering Ročník 32; číslo 8; s. 4643 - 4689
Hlavní autori: Taleb, Sylia Mekhmoukh, Yasin, Elham Tahsin, Saadi, Amylia Ait, Dogan, Musa, Yahia, Selma, Meraihi, Yassine, Koklu, Murat, Mirjalili, Seyedali, Ramdane-Cherif, Amar
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Dordrecht Springer Netherlands 01.12.2025
Springer Nature B.V
Springer Verlag
Predmet:
Algorithms
Artificial Intelligence
Chemical reactions
Computer Science
Engineering
Evolutionary computation
Genetic algorithms
Heuristic
Heuristic methods
Image processing
Mathematical and Computational Engineering
Optimization
Optimization algorithms
Power dispatch
Review
Unmanned aerial vehicles
Vehicle routing
Wireless sensor networks
ISSN:1134-3060, 1886-1784
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Shrnutí:The Aquila Optimizer (AO) algorithm is a well-known Swarm-based nature-inspired optimization algorithm inspired by Aquila’s behavior in hunting and catching prey. Since its development by Abualigah et al. (Comput Methods Appl Mech Eng 376:113609, 2021), AO has gained significant interest among researchers. It has been widely applied across various fields to solve optimization problems, owing to its simplicity, ease of implementation, and reasonable execution time. The main purpose of this paper is to provide a comprehensive survey of the AO algorithm and its improved variants (multi-objective, modified, and hybridized). It also illustrates the various applications of the AO algorithm in several domains of problems such as image processing, feature selection, economic load dispatch, wireless sensor networks, photovoltaic power systems, Unmanned Aerial Vehicles (UAVs) path planning, optimal parameter control, and vehicle routing problems. Furthermore, the results of the AO algorithm are compared with some well-known optimization meta-heuristics published in the literature, such as Differential Evolution (DF), Firefly Algorithm (FA), Bat Algorithm (BA), Grey Wolf Optimization (GWO), Moth Flame Optimization (MFO), and Multi-Verse Optimizer (MVO). Finally, the paper concludes with some future research directions for the AO algorithm.
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ISSN:1134-3060
1886-1784
DOI:10.1007/s11831-025-10281-0