Сравнение методов

	Байесовский табу-поиск	Байесовский отжиг (Bayesian Simulated Annealing, BSA)
Область	Имитационное моделирование	Имитационное моделирование
Семейство	Process / pipeline	Process / pipeline
Год появления	1989 (tabu search); hybrid formulations ~2005–2015	1984
Автор метода	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)
Тип	Hybrid metaheuristic — memory-based local search with Bayesian probabilistic guidance	Probabilistic metaheuristic with Bayesian inference
Основополагающий источник	Glover, F. (1989). Tabu search — Part I. ORSA Journal on Computing, 1(3), 190–206.	Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671–680.
Другие названия	BTS, Bayesian-guided tabu search, probabilistic tabu search, Bayes-TS	BSA, Bayesian SA, Bayesian Stochastic Annealing, Bayesian Thermodynamic Optimization
Связанные	6	5
Сводка	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.