Порівняння методів

	Оптимізатор сірого вовка	Оптимізація роєм частинок (PSO)
Галузь	Оптимізація	Оптимізація
Родина	Process / pipeline	Process / pipeline
Рік появи	2014	1995
Автор методу	Seyedali Mirjalili, Seyed Mohammad Mirjalili, Andrew Lewis	—
Тип	Swarm-intelligence metaheuristic	Population-based metaheuristic / swarm intelligence
Основоположне джерело	Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46-61.	Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948.
Інші назви	GWO, Gri Kurt Optimizasyonu, Gri Kurt Optimizasyonu (GWO)	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)
Пов'язані	5	6
Підсумок	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.	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.