Compară metode

	Optimizarea prin roi de particule (PSO)	Criteriul de Decizie Diferențială
Domeniu	Optimizare	Optimizare
Familie	Process / pipeline	Process / pipeline
Anul apariției	1995	1997
Autorul original	—	Rainer Storn & Kenneth Price
Tip	Population-based metaheuristic / swarm intelligence	Population-based stochastic metaheuristic
Sursa seminală	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.
Denumiri alternative	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)	DE algorithm, Diferansiyel Evrim (DE), DE optimization
Înrudite	6	5
Rezumat	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.