Salīdzināt metodes

	Firefly Algorithm	Dzeguzes meklēšana	Grey Wolf Optimizer
Nozare	Optimizācija	Optimizācija	Optimizācija
Saime	Process / pipeline	Process / pipeline	Process / pipeline
Izcelsmes gads	2008	2009	2014
Autors	Xin-She Yang	—	Seyedali Mirjalili, Seyed Mohammad Mirjalili, Andrew Lewis
Tips	Swarm intelligence metaheuristic	Population-based metaheuristic / swarm intelligence	Swarm-intelligence metaheuristic
Pirmavots	Yang, X.S. (2010). Firefly Algorithm, Stochastic Test Functions and Design Optimisation. International Journal of Bio-Inspired Computation, 2(2), 78-84.	Yang, X.S. & Deb, S. (2009). Cuckoo Search via Lévy Flights. 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), 210-214. IEEE.	Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46-61.
Citi nosaukumi	FA, Firefly Optimization, Ateşböceği Algoritması (Firefly Algorithm)	Guguk Kuşu Araması (Cuckoo Search), CS algorithm, Cuckoo Search via Lévy Flights	GWO, Gri Kurt Optimizasyonu, Gri Kurt Optimizasyonu (GWO)
Saistītās	5	6	5
Kopsavilkums	The Firefly Algorithm (FA), introduced by Xin-She Yang in 2008 and formally published in 2010, is a nature-inspired swarm metaheuristic that models the bioluminescent attraction behaviour of fireflies. Each candidate solution is a firefly whose brightness represents its objective-function value; dimmer fireflies move toward brighter ones with an attraction force that decays with distance, driving the swarm toward optima without gradient information.	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.	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.