Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Алгоритм слизевиков× | Оптимизация с помощью ястребов Харриса× | |
|---|---|---|
| Область | Оптимизация | Оптимизация |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 2020 | 2019 |
| Автор метода≠ | Shimin Li | Ali Asghar Heidari |
| Тип | Nature-inspired metaheuristic algorithm | Nature-inspired metaheuristic algorithm |
| Основополагающий источник≠ | Li, S., Chen, H., Wang, M., Heidari, A. A., & Chakraborty, S. (2020). Slime mould algorithm: A new method for stochastic optimization. Future Generation Computer Systems, 111, 300-323. DOI ↗ | Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems, 97, 849-872. DOI ↗ |
| Другие названия | SMA | HHO |
| Связанные≠ | 5 | 4 |
| Сводка≠ | The Slime Mould Algorithm (SMA) is a nature-inspired metaheuristic optimization technique introduced by Li et al. in 2020. It mimics the behavior of slime moulds, which spread and contract to find optimal food sources. SMA addresses complex optimization problems by simulating the adaptive foraging and spatial distribution patterns of these organisms. | Harris Hawks Optimization (HHO) is a metaheuristic algorithm introduced by Heidari et al. in 2019, inspired by the hunting strategies of Harris's hawks. The algorithm models the cooperative hunting behavior and escape strategies of these raptors to solve complex optimization problems. HHO balances exploration through perching and exploitation through dynamic pursuit, making it effective for multimodal and high-dimensional optimization. |
| ScholarGateНабор данных ↗ |
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