So sánh phương pháp

	Thuật toán tìm kiếm Chim Cúc cu	Tối ưu hóa Lượng tử	Grey Wolf Optimizer
Lĩnh vực	Tối ưu hóa	Tối ưu hóa	Tối ưu hóa
Họ	Process / pipeline	Process / pipeline	Process / pipeline
Năm ra đời	2009	1997	2014
Người khởi xướng	—	Rainer Storn & Kenneth Price	Seyedali Mirjalili, Seyed Mohammad Mirjalili, Andrew Lewis
Loại	Population-based metaheuristic / swarm intelligence	Population-based stochastic metaheuristic	Swarm-intelligence metaheuristic
Công trình gốc	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.
Tên gọi khác	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)
Liên quan	6	5	5
Tóm tắt	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.