Compara mètodes

	Cuckoo Search	Differential Evolution	Firefly Algorithm	Algorisme genètic	Harmony Search
Camp	Optimització	Optimització	Optimització	Optimització	Optimització
Família	Process / pipeline	Process / pipeline	Process / pipeline	Process / pipeline	Process / pipeline
Any d'origen	2009	1997	2008	1975	2001
Autor original	—	Rainer Storn & Kenneth Price	Xin-She Yang	John Henry Holland	Zong Woo Geem, Joong Hoon Kim, G. V. Loganathan
Tipus	Population-based metaheuristic / swarm intelligence	Population-based stochastic metaheuristic	Swarm intelligence metaheuristic	Population-based metaheuristic	Metaheuristic population-based optimization
Font seminal	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.	Yang, X.S. (2010). Firefly Algorithm, Stochastic Test Functions and Design Optimisation. International Journal of Bio-Inspired Computation, 2(2), 78-84.	Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press.	Geem, Z. W., Kim, J. H., & Loganathan, G. V. (2001). A New Heuristic Optimization Algorithm: Harmony Search. Simulation, 76(2), 60–68.
Àlies	Guguk Kuşu Araması (Cuckoo Search), CS algorithm, Cuckoo Search via Lévy Flights	DE algorithm, Diferansiyel Evrim (DE), DE optimization	FA, Firefly Optimization, Ateşböceği Algoritması (Firefly Algorithm)	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon	HS algorithm, Harmoni Araması (Harmony Search), music-inspired optimization
Relacionats	6	5	5	5	5
Resum	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 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.	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.	Harmony Search (HS) is a population-based metaheuristic optimization algorithm introduced by Geem, Kim, and Loganathan in 2001. It mimics the improvisation process of jazz musicians seeking a perfect state of harmony, using three operators — memory consideration, pitch adjustment, and random selection — to generate candidate solutions. The algorithm applies to both continuous and discrete variables and has found wide use in engineering design, water distribution network optimization, and combinatorial problems.