Compară metode

	Optimizarea prin roi de particule (PSO)	Optimizatorul Lupilor Cenușii
Domeniu	Optimizare	Optimizare
Familie	Process / pipeline	Process / pipeline
Anul apariției	1995	2014
Autorul original	—	Seyedali Mirjalili, Seyed Mohammad Mirjalili, Andrew Lewis
Tip	Population-based metaheuristic / swarm intelligence	Swarm-intelligence metaheuristic
Sursa seminală	Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948.	Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46-61.
Denumiri alternative	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)	GWO, Gri Kurt Optimizasyonu, Gri Kurt Optimizasyonu (GWO)
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.	The Grey Wolf Optimizer (GWO) is a swarm-intelligence metaheuristic introduced by Mirjalili, Mirjalili, and Lewis in 2014 that models the social hierarchy and cooperative hunting behaviour of grey wolves. A population of candidate solutions is divided into four leadership ranks — alpha, beta, delta, and omega — and the three best solutions at each iteration guide the entire swarm toward increasingly better regions of the search space.