A Comprehensive Survey of Aquila Optimizer: Theory, Variants, Hybridization, and Applications
Yazarlar (9)
Sylia Mekhmoukh Taleb Université De Boumerdes, Cezayir
Dr. Öğr. Üyesi Elham Tahsın Yasın YASIN Selçuk Üniversitesi, Türkiye
Amylia Ait Saadi Laboratoire D'ıngénierie Des Systèmes De Versailles, Fransa
Arş. Gör. Musa Dogan Selçuk Üniversitesi, Türkiye
Selma Yahia Université De Boumerdes, Cezayir
Yassine Meraihi Université De Boumerdes, Cezayir
Murat Koklu Selçuk Üniversitesi, Türkiye
Seyedali Mirjalili Torrens University Australia, Avustralya
Amar Ramdane-Cherif Laboratoire D'ıngénierie Des Systèmes De Versailles, Fransa
| Makale Türü |
Diğer (Teknik, not, yorum, vaka takdimi, editöre mektup, özet, kitap krıtiği,
araştırma notu, bilirkişi raporu ve benzeri)
(SCI, SSCI, AHCI, SCI-Exp dergilerinde yayınlanan teknik not,
editöre mektup, tartışma, vaka takdimi ve özet türünden makale)
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| Dergi Adı | Archives of Computational Methods in Engineering (Q1) | ||
| Dergi ISSN | 1134-3060 Dergi Bilgileri (2025) | ||
| Makale Dili | İngilizce | Basım Tarihi | 12-2025 |
| Kabul Tarihi | 26-03-2025 | Yayınlanma Tarihi | 07-05-2025 |
| Cilt / Sayı / Sayfa | 32 / 8 / 4643–4689 | DOI | 10.1007/s11831-025-10281-0 |
| Makale Linki | https://link.springer.com/10.1007/s11831-025-10281-0 | ||
| UAK Araştırma Alanları |
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| Özet |
| 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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BM Sürdürülebilir Kalkınma Amaçları
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