手法を比較
選択した手法を並べて確認できます。異なる行はハイライト表示されます。
| ルンゲ＝クッタ最適化手法× | 算術最適化アルゴリズム× | |
|---|---|---|
| 分野 | 最適化 | 最適化 |
| 系統 | Machine learning | Machine learning |
| 提唱年≠ | 2023 | 2020 |
| 提唱者≠ | Ayushi Khatri | Laith Abualigah |
| 種類 | Mathematical metaheuristic algorithm | Mathematical metaheuristic algorithm |
| 原典≠ | Khatri, A., Kumar, A., & Gaba, G. K. (2023). Runge Kutta optimizer: An efficient approach for solving optimization tasks. Computers and Industrial Engineering, 180, 109201. link ↗ | Abualigah, L., Yousri, D., Abd Elaziz, M., Ewees, A. A., Al-qaness, M. A., & Gandomi, A. H. (2021). Arithmetic optimization algorithm: A new metaheuristic algorithm for solving optimization problems. Applied Mathematics and Computation, 392, 125450. link ↗ |
| 別名 | RKO | AOA |
| 関連 | 5 | 5 |
| 概要≠ | The Runge Kutta Optimizer (RKO) is a metaheuristic algorithm introduced by Khatri et al. in 2023 that leverages numerical integration principles from the Runge-Kutta method. Instead of biological inspiration, RKO grounds optimization in mathematical principles of differential equations and numerical integration. The algorithm treats the optimization landscape as a dynamic system and uses multi-stage integration steps to evolve solutions toward optima. | The Arithmetic Optimization Algorithm (AOA) is a metaheuristic optimization approach introduced by Abualigah et al. in 2020 that leverages mathematical operators (multiplication, division, addition, subtraction) as the inspiration for search strategies. Unlike nature-inspired algorithms, AOA uses the inherent properties of arithmetic operations to balance exploration and exploitation, making it particularly effective for mathematical optimization problems. |
| ScholarGateデータセット ↗ |
|
|