Сравнение методов

	Дифференциальная эволюция	Алгоритм светлячков	Harmony Search
Область	Оптимизация	Оптимизация	Оптимизация
Семейство	Process / pipeline	Process / pipeline	Process / pipeline
Год появления	1997	2008	2001
Автор метода	Rainer Storn & Kenneth Price	Xin-She Yang	Zong Woo Geem, Joong Hoon Kim, G. V. Loganathan
Тип	Population-based stochastic metaheuristic	Swarm intelligence metaheuristic	Metaheuristic population-based optimization
Основополагающий источник	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.	Geem, Z. W., Kim, J. H., & Loganathan, G. V. (2001). A New Heuristic Optimization Algorithm: Harmony Search. Simulation, 76(2), 60–68.
Другие названия	DE algorithm, Diferansiyel Evrim (DE), DE optimization	FA, Firefly Optimization, Ateşböceği Algoritması (Firefly Algorithm)	HS algorithm, Harmoni Araması (Harmony Search), music-inspired optimization
Связанные	5	5	5
Сводка	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.	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.