方法对比

	萤火虫算法	差分进化	遗传算法
领域	优化	优化	优化
方法族	Process / pipeline	Process / pipeline	Process / pipeline
起源年份	2008	1997	1975
提出者	Xin-She Yang	Rainer Storn & Kenneth Price	John Henry Holland
类型	Swarm intelligence metaheuristic	Population-based stochastic metaheuristic	Population-based metaheuristic
开创性文献	Yang, X.S. (2010). Firefly Algorithm, Stochastic Test Functions and Design Optimisation. International Journal of Bio-Inspired Computation, 2(2), 78-84.	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.	Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press.
别名	FA, Firefly Optimization, Ateşböceği Algoritması (Firefly Algorithm)	DE algorithm, Diferansiyel Evrim (DE), DE optimization	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon
相关	5	5	5
摘要	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.	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.	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.