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	Bayesian Tabu Search	बेयसियन सिम्युलेटेड एनीलिंग
क्षेत्र	अनुकरण	अनुकरण
परिवार	Process / pipeline	Process / pipeline
उद्भव वर्ष	1989 (tabu search); hybrid formulations ~2005–2015	1984
प्रवर्तक	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)
प्रकार	Hybrid metaheuristic — memory-based local search with Bayesian probabilistic guidance	Probabilistic metaheuristic with Bayesian inference
मौलिक स्रोत	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.
उपनाम	BTS, Bayesian-guided tabu search, probabilistic tabu search, Bayes-TS	BSA, Bayesian SA, Bayesian Stochastic Annealing, Bayesian Thermodynamic Optimization
संबंधित	6	5
सारांश	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.