Krahasoni metodat

	Kërkimi i Qyqes	Evolucioni Diferencial	Optimizuesi ujku gri
Fusha	Optimizimi	Optimizimi	Optimizimi
Familja	Process / pipeline	Process / pipeline	Process / pipeline
Viti i origjinës	2009	1997	2014
Krijuesi	—	Rainer Storn & Kenneth Price	Seyedali Mirjalili, Seyed Mohammad Mirjalili, Andrew Lewis
Lloji	Population-based metaheuristic / swarm intelligence	Population-based stochastic metaheuristic	Swarm-intelligence metaheuristic
Burimi themelues	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.
Emërtime të tjera	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)
Të lidhura	6	5	5
Përmbledhja	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.