השוואת שיטות

	חיפוש קוקייה	אלגוריתם הגחלילית	Harmony Search
תחום	אופטימיזציה	אופטימיזציה	אופטימיזציה
משפחה	Process / pipeline	Process / pipeline	Process / pipeline
שנת המקור	2009	2008	2001
הוגה השיטה	—	Xin-She Yang	Zong Woo Geem, Joong Hoon Kim, G. V. Loganathan
סוג	Population-based metaheuristic / swarm intelligence	Swarm intelligence metaheuristic	Metaheuristic population-based optimization
מקור מכונן	Yang, X.S. & Deb, S. (2009). Cuckoo Search via Lévy Flights. 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), 210-214. IEEE.	Yang, X.S. (2010). Firefly Algorithm, Stochastic Test Functions and Design Optimisation. International Journal of Bio-Inspired Computation, 2(2), 78-84.	Geem, Z. W., Kim, J. H., & Loganathan, G. V. (2001). A New Heuristic Optimization Algorithm: Harmony Search. Simulation, 76(2), 60–68.
כינויים	Guguk Kuşu Araması (Cuckoo Search), CS algorithm, Cuckoo Search via Lévy Flights	FA, Firefly Optimization, Ateşböceği Algoritması (Firefly Algorithm)	HS algorithm, Harmoni Araması (Harmony Search), music-inspired optimization
קשורות	6	5	5
תקציר	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 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.	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.