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| Honigdachs-Algorithmus× | Grey Wolf Optimizer× | |
|---|---|---|
| Fachgebiet | Optimierung | Optimierung |
| Familie≠ | Machine learning | Process / pipeline |
| Entstehungsjahr≠ | 2023 | 2014 |
| Urheber≠ | Fatma A. Hashim | Seyedali Mirjalili, Seyed Mohammad Mirjalili, Andrew Lewis |
| Typ≠ | Nature-inspired metaheuristic algorithm | Swarm-intelligence metaheuristic |
| 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 ↗ | Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46-61. DOI ↗ |
| Aliasnamen≠ | HBA | GWO, Gri Kurt Optimizasyonu, Gri Kurt Optimizasyonu (GWO) |
| 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 Grey Wolf Optimizer (GWO) is a swarm-intelligence metaheuristic introduced by Mirjalili, Mirjalili, and Lewis in 2014 that models the social hierarchy and cooperative hunting behaviour of grey wolves. A population of candidate solutions is divided into four leadership ranks — alpha, beta, delta, and omega — and the three best solutions at each iteration guide the entire swarm toward increasingly better regions of the search space. |
| ScholarGateDatensatz ↗ |
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