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| 베이지안 타부 탐색× | 베이지안 시뮬레이티드 어닐링× | |
|---|---|---|
| 분야 | 시뮬레이션 | 시뮬레이션 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 1989 (tabu search); hybrid formulations ~2005–2015 | 1984 |
| 창시자≠ | Glover, F. (tabu search); Bayesian integration developed by multiple researchers in the 2000s–2010s | Geman, S. & Geman, D. (Bayesian framing); Kirkpatrick, S. et al. (SA foundation) |
| 유형≠ | Hybrid metaheuristic — memory-based local search with Bayesian probabilistic guidance | Probabilistic metaheuristic with Bayesian inference |
| 원전≠ | Glover, F. (1989). Tabu search — Part I. ORSA Journal on Computing, 1(3), 190–206. DOI ↗ | Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671–680. DOI ↗ |
| 별칭 | BTS, Bayesian-guided tabu search, probabilistic tabu search, Bayes-TS | BSA, Bayesian SA, Bayesian Stochastic Annealing, Bayesian Thermodynamic Optimization |
| 관련≠ | 6 | 5 |
| 요약≠ | Bayesian Tabu Search (BTS) is a hybrid metaheuristic that couples the memory-based forbidden-move mechanism of classic Tabu Search with a Bayesian probabilistic model. The Bayesian component learns from past evaluations to score candidate moves, focusing the search on promising regions while the tabu list prevents cycling. This combination reduces wasted function evaluations in expensive combinatorial and continuous optimization problems. | Bayesian Simulated Annealing (BSA) integrates Bayesian prior knowledge about the objective landscape into the simulated annealing search process. By encoding beliefs about promising regions as prior distributions and updating them as the search progresses, BSA focuses computational effort on high-probability areas of the solution space, accelerating convergence and improving solution quality compared to uninformed SA. |
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