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| Honigdachs-Algorithmus× | Slime Mould Algorithm× | |
|---|---|---|
| Fachgebiet | Optimierung | Optimierung |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2023 | 2020 |
| Urheber≠ | Fatma A. Hashim | Shimin Li |
| Typ | Nature-inspired metaheuristic algorithm | Nature-inspired metaheuristic algorithm |
| Wegweisende Quelle≠ | Hashim, F. A., Hussain, K., & Houssein, E. H. (2023). Honey badger algorithm: A new meta-heuristic optimization algorithm. Neural Computing and Applications, 35(17), 12265-12287. link ↗ | 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 ↗ |
| Aliasnamen | HBA | SMA |
| Verwandt | 5 | 5 |
| Zusammenfassung≠ | The Honey Badger Algorithm (HBA) is a nature-inspired metaheuristic optimization algorithm presented by Hashim et al. in 2023, modeled on the hunting behavior and intelligent strategies of honey badgers (Mellivora capensis). Honey badgers are known for their remarkable problem-solving abilities, fearlessness, and persistent pursuit of prey and food sources despite significant obstacles. HBA captures these behavioral traits to create an effective optimization framework. | 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. |
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