방법 비교

	고래 최적화 알고리즘 (Whale Optimization Algorithm, WOA)	입자 군집 최적화 (PSO)
분야	최적화	최적화
계열	Process / pipeline	Process / pipeline
기원 연도	2016	1995
창시자	Seyedali Mirjalili & Andrew Lewis	—
유형	Swarm-based metaheuristic	Population-based metaheuristic / swarm intelligence
원전	Mirjalili, S. & Lewis, A. (2016). The Whale Optimization Algorithm. Advances in Engineering Software, 95, 51-67.	Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948.
별칭	WOA, Balina Optimizasyon Algoritması (WOA), bubble-net attacking method	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)
관련	5	6
요약	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.	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.