Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Optimizarea prin manguste pitice× | Optimizatorul Lupilor Cenușii× | |
|---|---|---|
| Domeniu | Optimizare | Optimizare |
| Familie≠ | Machine learning | Process / pipeline |
| Anul apariției≠ | 2022 | 2014 |
| Autorul original≠ | Joseph O. Agushaka | Seyedali Mirjalili, Seyed Mohammad Mirjalili, Andrew Lewis |
| Tip≠ | Nature-inspired metaheuristic algorithm | Swarm-intelligence metaheuristic |
| Sursa seminală≠ | Agushaka, J. O., Ezugwu, A. E., & Abualigah, L. (2022). Dwarf mongoose optimization algorithm. Computer Methods in Applied Mechanics and Engineering, 391, 114570. DOI ↗ | Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46-61. DOI ↗ |
| Denumiri alternative≠ | DMO | GWO, Gri Kurt Optimizasyonu, Gri Kurt Optimizasyonu (GWO) |
| Înrudite≠ | 4 | 5 |
| Rezumat≠ | The Dwarf Mongoose Optimization (DMO) algorithm is a nature-inspired metaheuristic introduced by Agushaka et al. in 2022, based on the behavioral patterns of dwarf mongoose colonies. Dwarf mongooses exhibit sophisticated group dynamics including sentry behavior (surveillance and exploration), pup care (mentoring), and cooperative hunting. The algorithm translates these social behaviors into optimization mechanisms that balance exploration and exploitation effectively. | 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. |
| ScholarGateSet de date ↗ |
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