Sammenlign metoder

	Differential Evolution	Firefly Algoritmen
Fagområde	Optimering	Optimering
Familie	Process / pipeline	Process / pipeline
Oprindelsesår	1997	2008
Ophavsperson	Rainer Storn & Kenneth Price	Xin-She Yang
Type	Population-based stochastic metaheuristic	Swarm intelligence metaheuristic
Oprindelig kilde	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.
Aliasser	DE algorithm, Diferansiyel Evrim (DE), DE optimization	FA, Firefly Optimization, Ateşböceği Algoritması (Firefly Algorithm)
Relaterede	5	5
Resumé	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.