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<p>The Archimedes Optimization Algorithm (AOA) is a recent physics-based metaheuristic inspired by Archimedes’ principle. Since its introduction by Hashim et al. in 2021, it has gained significant attention and has been applied to various real-world optimization problems. Its popularity stems from its simple structure, adaptability, ease of implementation, and satisfactory convergence. This paper presents a comprehensive review of the AOA algorithm, including its modified, multi-objective, and hybrid variants, and examines its applications in several domains such as classification, feature selection, parameter tuning, scheduling, photovoltaic systems, wireless networks, and clustering. The performance of the AOA algorithm is evaluated against some well-known metaheuristic algorithms, including Genetic Algorithm (GA), Differential Evolution (DE), Tabu Search (TS), Firefly Algorithm (FA), Bat Algorithm (BA), Whale Optimization Algorithm (WOA), Grey Wolf Optimizer (GWO), Sine Cosine Algorithm (SCA), and Marine Predators Algorithm (MPA), and results demonstrates its superiority in most cases (72,22%) with stable dispersion in box-plot analyses. Finally, future research directions for improving the effectiveness and applicability of the AOA algorithm are outlined.</p>

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A Comprehensive Review of Archimedes Optimization Algorithm with its Theory, Variants, Hybridization, and Applications

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月21日

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  • Yassine Meraihi,
  • Sylia Mekhmoukh Taleb,
  • Bikram Pratim Bhuyan,
  • Abdelbaki Benayad,
  • Galina Ivanova,
  • Musa Dogan,
  • Elham Tahsin Yasin,
  • Murat Koklu,
  • Amar Ramdane-Cherif,
  • Seyedali Mirjalili

摘要

The Archimedes Optimization Algorithm (AOA) is a recent physics-based metaheuristic inspired by Archimedes’ principle. Since its introduction by Hashim et al. in 2021, it has gained significant attention and has been applied to various real-world optimization problems. Its popularity stems from its simple structure, adaptability, ease of implementation, and satisfactory convergence. This paper presents a comprehensive review of the AOA algorithm, including its modified, multi-objective, and hybrid variants, and examines its applications in several domains such as classification, feature selection, parameter tuning, scheduling, photovoltaic systems, wireless networks, and clustering. The performance of the AOA algorithm is evaluated against some well-known metaheuristic algorithms, including Genetic Algorithm (GA), Differential Evolution (DE), Tabu Search (TS), Firefly Algorithm (FA), Bat Algorithm (BA), Whale Optimization Algorithm (WOA), Grey Wolf Optimizer (GWO), Sine Cosine Algorithm (SCA), and Marine Predators Algorithm (MPA), and results demonstrates its superiority in most cases (72,22%) with stable dispersion in box-plot analyses. Finally, future research directions for improving the effectiveness and applicability of the AOA algorithm are outlined.