مقایسهٔ روشها
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| الگوریتم بهینهسازی حسابی× | بهینهسازی ازدحام ذرات (PSO)× | |
|---|---|---|
| حوزه | بهینهسازی | بهینهسازی |
| خانواده≠ | Machine learning | Process / pipeline |
| سال پیدایش≠ | 2020 | 1995 |
| پدیدآور≠ | Laith Abualigah | — |
| نوع≠ | Mathematical metaheuristic algorithm | Population-based metaheuristic / swarm intelligence |
| منبع بنیادین≠ | 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 ↗ |
| نامهای دیگر≠ | AOA | PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO) |
| مرتبط≠ | 5 | 6 |
| خلاصه≠ | 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. |
| ScholarGateمجموعهداده ↗ |
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