Порівняння методів

	Оптимізація роєм частинок (PSO)	Оптимізатор сірого вовка
Галузь	Оптимізація	Оптимізація
Родина	Process / pipeline	Process / pipeline
Рік появи	1995	2014
Автор методу	—	Seyedali Mirjalili, Seyed Mohammad Mirjalili, Andrew Lewis
Тип	Population-based metaheuristic / swarm intelligence	Swarm-intelligence metaheuristic
Основоположне джерело	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.
Інші назви	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)	GWO, Gri Kurt Optimizasyonu, Gri Kurt Optimizasyonu (GWO)
Пов'язані	6	5
Підсумок	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.