Sammenlign metoder

	Firefly Algoritmen	Genetisk Algoritme	Harmony Search
Fagområde	Optimering	Optimering	Optimering
Familie	Process / pipeline	Process / pipeline	Process / pipeline
Oprindelsesår	2008	1975	2001
Ophavsperson	Xin-She Yang	John Henry Holland	Zong Woo Geem, Joong Hoon Kim, G. V. Loganathan
Type	Swarm intelligence metaheuristic	Population-based metaheuristic	Metaheuristic population-based optimization
Oprindelig kilde	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.
Aliasser	FA, Firefly Optimization, Ateşböceği Algoritması (Firefly Algorithm)	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon	HS algorithm, Harmoni Araması (Harmony Search), music-inspired optimization
Relaterede	5	5	5
Resumé	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.