Σύγκριση μεθόδων

	Αλγόριθμος Βελτιστοποίησης Φάλαινας (WOA)	Βελτιστοποιητής Γκρίζου Λύκου
Πεδίο	Βελτιστοποίηση	Βελτιστοποίηση
Οικογένεια	Process / pipeline	Process / pipeline
Έτος προέλευσης	2016	2014
Δημιουργός	Seyedali Mirjalili & Andrew Lewis	Seyedali Mirjalili, Seyed Mohammad Mirjalili, Andrew Lewis
Τύπος	Swarm-based metaheuristic	Swarm-intelligence metaheuristic
Θεμελιώδης πηγή	Mirjalili, S. & Lewis, A. (2016). The Whale Optimization Algorithm. Advances in Engineering Software, 95, 51-67.	Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46-61.
Εναλλακτικές ονομασίες	WOA, Balina Optimizasyon Algoritması (WOA), bubble-net attacking method	GWO, Gri Kurt Optimizasyonu, Gri Kurt Optimizasyonu (GWO)
Συναφείς	5	5
Σύνοψη	The Whale Optimization Algorithm (WOA) is a swarm-based metaheuristic introduced by Mirjalili and Lewis in 2016. It models the bubble-net hunting strategy of humpback whales, in which a group of whales spirals around prey while gradually tightening the encirclement. The algorithm balances global exploration and local exploitation through a small set of parameters and has become widely used in continuous engineering optimisation 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.