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| Slime Mould Algorithm× | Aquila Optimizer× | |
|---|---|---|
| Fachgebiet | Optimierung | Optimierung |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2020 | 2021 |
| Urheber≠ | Shimin Li | Laith Abualigah |
| Typ | Nature-inspired metaheuristic algorithm | Nature-inspired metaheuristic algorithm |
| Wegweisende Quelle≠ | 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 ↗ | Abualigah, L., Yousri, D., Abd Elaziz, M., Ewees, A. A., Al-qaness, M. A., & Gandomi, A. H. (2021). Aquila optimizer: A novel meta-heuristic optimization algorithm. Computers and Industrial Engineering, 157, 107250. DOI ↗ |
| Aliasnamen | SMA | AO |
| Verwandt≠ | 5 | 3 |
| Zusammenfassung≠ | 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. | The Aquila Optimizer (AO) is a nature-inspired metaheuristic algorithm presented by Abualigah et al. in 2021, modeled after the hunting behavior and sensory abilities of golden eagles (aquila chrysaetos). The algorithm captures the exploration and exploitation phases of eagle hunting, including high-altitude soaring, exploration with high-precision vision, and rapid diving attacks. AO is designed to solve both constrained and unconstrained optimization problems. |
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