Compară metode

	Cuckoo Search	Criteriul de Decizie Diferențială	Optimizatorul Lupilor Cenușii
Domeniu	Optimizare	Optimizare	Optimizare
Familie	Process / pipeline	Process / pipeline	Process / pipeline
Anul apariției	2009	1997	2014
Autorul original	—	Rainer Storn & Kenneth Price	Seyedali Mirjalili, Seyed Mohammad Mirjalili, Andrew Lewis
Tip	Population-based metaheuristic / swarm intelligence	Population-based stochastic metaheuristic	Swarm-intelligence metaheuristic
Sursa seminală	Yang, X.S. & Deb, S. (2009). Cuckoo Search via Lévy Flights. 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), 210-214. IEEE.	Storn, R. & Price, K. (1997). Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. Journal of Global Optimization, 11(4), 341–359.	Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46-61.
Denumiri alternative	Guguk Kuşu Araması (Cuckoo Search), CS algorithm, Cuckoo Search via Lévy Flights	DE algorithm, Diferansiyel Evrim (DE), DE optimization	GWO, Gri Kurt Optimizasyonu, Gri Kurt Optimizasyonu (GWO)
Înrudite	6	5	5
Rezumat	Cuckoo Search (CS) is a population-based metaheuristic optimization algorithm introduced by Xin-She Yang and Suash Deb in 2009. It models the obligate brood-parasitism of cuckoo birds — which lay eggs in other birds' nests — combined with Lévy flight random walks that enable long-range exploration of the search space. The algorithm has proven effective in structural engineering design, machine learning hyperparameter tuning, and other continuous black-box optimization problems.	Differential Evolution (DE), introduced by Rainer Storn and Kenneth Price in 1997, is a population-based stochastic optimisation algorithm designed for continuous parameter spaces. It generates candidate solutions by combining vector differences between existing population members, making it a powerful and parameter-lean alternative to Genetic Algorithms and Particle Swarm Optimisation when the search landscape is non-convex, multimodal, or poorly suited to gradient-based methods.	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.