Compara mètodes

	Otimització Bayesiana per Colònies d'Escultures (BACO)	Simulated Annealing Bayesiana
Camp	Simulació	Simulació
Família	Process / pipeline	Process / pipeline
Any d'origen	1996 (ACO); Bayesian variant: 2000s	1984
Autor original	Dorigo, M. et al. (ACO); Bayesian extensions by multiple researchers in the 2000s–2010s	Geman, S. & Geman, D. (Bayesian framing); Kirkpatrick, S. et al. (SA foundation)
Tipus	Metaheuristic with Bayesian probabilistic learning	Probabilistic metaheuristic with Bayesian inference
Font seminal	Dorigo, M., Maniezzo, V., Colorni, A. (1996). Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B, 26(1), 29–41.	Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671–680.
Àlies	BACO, Bayesian ACO, Bayesian-guided ACO, Probabilistic ACO	BSA, Bayesian SA, Bayesian Stochastic Annealing, Bayesian Thermodynamic Optimization
Relacionats	5	5
Resum	Bayesian Ant Colony Optimization (BACO) is a hybrid metaheuristic that embeds Bayesian inference into the Ant Colony Optimization framework. By treating pheromone intensities or algorithm parameters as probability distributions updated with collected evidence, BACO improves convergence reliability and robustness compared to classical ACO on noisy or uncertain combinatorial 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.