Σύγκριση μεθόδων

	Βελτιστοποιητής Γκρίζου Λύκου	Γενετικός Αλγόριθμος
Πεδίο	Βελτιστοποίηση	Βελτιστοποίηση
Οικογένεια	Process / pipeline	Process / pipeline
Έτος προέλευσης	2014	1975
Δημιουργός	Seyedali Mirjalili, Seyed Mohammad Mirjalili, Andrew Lewis	John Henry Holland
Τύπος	Swarm-intelligence metaheuristic	Population-based metaheuristic
Θεμελιώδης πηγή	Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46-61.	Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press.
Εναλλακτικές ονομασίες	GWO, Gri Kurt Optimizasyonu, Gri Kurt Optimizasyonu (GWO)	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon
Συναφείς	5	5
Σύνοψη	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.	A genetic algorithm (GA) is a population-based metaheuristic optimization method introduced by John Henry Holland (1975) that mimics the principles of natural selection. It maintains a population of candidate solutions and iteratively improves them through selection, crossover, and mutation operators, making it especially powerful on discontinuous, non-convex, and multi-modal search spaces where classical gradient-based methods fail.