Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Алгоритм арифметической оптимизации× | Оптимизация роем частиц (PSO)× | |
|---|---|---|
| Область | Оптимизация | Оптимизация |
| Семейство≠ | Machine learning | Process / pipeline |
| Год появления≠ | 2020 | 1995 |
| Автор метода≠ | Laith Abualigah | — |
| Тип≠ | Mathematical metaheuristic algorithm | Population-based metaheuristic / swarm intelligence |
| Основополагающий источник≠ | 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 ↗ | Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948. DOI ↗ |
| Другие названия≠ | AOA | PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO) |
| Связанные≠ | 5 | 6 |
| Сводка≠ | 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. | Particle Swarm Optimization (PSO) is a population-based metaheuristic algorithm introduced by Kennedy and Eberhart in 1995, inspired by the collective movement of bird flocks and fish schools. Each candidate solution — called a particle — moves through the search space by updating its velocity and position based on its own best experience and the best experience of the entire swarm, enabling fast convergence across continuous optimization problems. |
| ScholarGateНабор данных ↗ |
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