Salīdzināt metodes

	Bayesian Tabu Search	Simulated Annealing
Nozare	Simulācija	Optimizācija
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	1989 (tabu search); hybrid formulations ~2005–2015	1983
Autors	Glover, F. (tabu search); Bayesian integration developed by multiple researchers in the 2000s–2010s	—
Tips	Hybrid metaheuristic — memory-based local search with Bayesian probabilistic guidance	Probabilistic metaheuristic / local search
Pirmavots	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.
Citi nosaukumi	BTS, Bayesian-guided tabu search, probabilistic tabu search, Bayes-TS	Benzetimli Tavlama (Simulated Annealing), SA, probabilistic local search
Saistītās	6	5
Kopsavilkums	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.