Comparer des méthodes

	Bayesian Tabu Search	Recuit simulé
Domaine	Simulation	Optimisation
Famille	Process / pipeline	Process / pipeline
Année d'origine	1989 (tabu search); hybrid formulations ~2005–2015	1983
Auteur d'origine	Glover, F. (tabu search); Bayesian integration developed by multiple researchers in the 2000s–2010s	—
Type	Hybrid metaheuristic — memory-based local search with Bayesian probabilistic guidance	Probabilistic metaheuristic / local search
Source fondatrice	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.
Alias	BTS, Bayesian-guided tabu search, probabilistic tabu search, Bayes-TS	Benzetimli Tavlama (Simulated Annealing), SA, probabilistic local search
Apparentées	6	5
Résumé	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.	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.