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| 고래 최적화 알고리즘 (Whale Optimization Algorithm, WOA)× | 모의 담금질× | |
|---|---|---|
| 분야 | 최적화 | 최적화 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 2016 | 1983 |
| 창시자≠ | Seyedali Mirjalili & Andrew Lewis | — |
| 유형≠ | Swarm-based metaheuristic | Probabilistic metaheuristic / local search |
| 원전≠ | Mirjalili, S. & Lewis, A. (2016). The Whale Optimization Algorithm. Advances in Engineering Software, 95, 51-67. DOI ↗ | Kirkpatrick, S., Gelatt, C.D. & Vecchi, M.P. (1983). Optimization by Simulated Annealing. Science, 220(4598), 671-680. DOI ↗ |
| 별칭 | WOA, Balina Optimizasyon Algoritması (WOA), bubble-net attacking method | Benzetimli Tavlama (Simulated Annealing), SA, probabilistic local search |
| 관련 | 5 | 5 |
| 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. | Simulated annealing is a probabilistic local-search metaheuristic introduced by Kirkpatrick, Gelatt, and Vecchi in 1983. It models the physical annealing process in metallurgy — where a material is heated and then slowly cooled to reach a low-energy crystalline state — and uses this analogy to escape local optima in combinatorial and continuous optimization problems. |
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