Сравнение методов

	Оптимизация роем частиц (PSO)	Дифференциальная эволюция
Область	Оптимизация	Оптимизация
Семейство	Process / pipeline	Process / pipeline
Год появления	1995	1997
Автор метода	—	Rainer Storn & Kenneth Price
Тип	Population-based metaheuristic / swarm intelligence	Population-based stochastic metaheuristic
Основополагающий источник	Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948.	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.
Другие названия	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)	DE algorithm, Diferansiyel Evrim (DE), DE optimization
Связанные	6	5
Сводка	Particle Swarm Optimization (PSO) is a population-based metaheuristic algorithm introduced by Kennedy and Eberhart in 1995, inspired by the collective movement of bird flocks and fish schools. Each candidate solution — called a particle — moves through the search space by updating its velocity and position based on its own best experience and the best experience of the entire swarm, enabling fast convergence across continuous optimization problems.	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.