Compara mètodes

	Optimitzaçó de mangosta nana	Grey Wolf Optimizer
Camp	Optimització	Optimització
Família	Machine learning	Process / pipeline
Any d'origen	2022	2014
Autor original	Joseph O. Agushaka	Seyedali Mirjalili, Seyed Mohammad Mirjalili, Andrew Lewis
Tipus	Nature-inspired metaheuristic algorithm	Swarm-intelligence metaheuristic
Font seminal	Agushaka, J. O., Ezugwu, A. E., & Abualigah, L. (2022). Dwarf mongoose optimization algorithm. Computer Methods in Applied Mechanics and Engineering, 391, 114570.	Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46-61.
Àlies	DMO	GWO, Gri Kurt Optimizasyonu, Gri Kurt Optimizasyonu (GWO)
Relacionats	4	5
Resum	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.