Sammenlign metoder

	Bayesian Tabu Search	Bayesian Simulated Annealing
Fagområde	Simulering	Simulering
Familie	Process / pipeline	Process / pipeline
Oprindelsesår	1989 (tabu search); hybrid formulations ~2005–2015	1984
Ophavsperson	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)
Type	Hybrid metaheuristic — memory-based local search with Bayesian probabilistic guidance	Probabilistic metaheuristic with Bayesian inference
Oprindelig kilde	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.
Aliasser	BTS, Bayesian-guided tabu search, probabilistic tabu search, Bayes-TS	BSA, Bayesian SA, Bayesian Stochastic Annealing, Bayesian Thermodynamic Optimization
Relaterede	6	5
Resumé	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.