Porovnat metody

	Algoritmus světlušek	Cuckoo Search	Genetický algoritmus
Obor	Optimalizace	Optimalizace	Optimalizace
Rodina	Process / pipeline	Process / pipeline	Process / pipeline
Rok vzniku	2008	2009	1975
Tvůrce	Xin-She Yang	—	John Henry Holland
Typ	Swarm intelligence metaheuristic	Population-based metaheuristic / swarm intelligence	Population-based metaheuristic
Původní zdroj	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.	Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press.
Další názvy	FA, Firefly Optimization, Ateşböceği Algoritması (Firefly Algorithm)	Guguk Kuşu Araması (Cuckoo Search), CS algorithm, Cuckoo Search via Lévy Flights	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon
Příbuzné	5	6	5
Shrnutí	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.	A genetic algorithm (GA) is a population-based metaheuristic optimization method introduced by John Henry Holland (1975) that mimics the principles of natural selection. It maintains a population of candidate solutions and iteratively improves them through selection, crossover, and mutation operators, making it especially powerful on discontinuous, non-convex, and multi-modal search spaces where classical gradient-based methods fail.