Comparer des méthodes

	Optimisation par Colonies de Fourmis Bayésiennes	Optimisation bayésienne par essaims particulaires
Domaine	Simulation	Simulation
Famille	Process / pipeline	Process / pipeline
Année d'origine	1996 (ACO); Bayesian variant: 2000s	2003
Auteur d'origine	Dorigo, M. et al. (ACO); Bayesian extensions by multiple researchers in the 2000s–2010s	Higashi, N., Iba, H. (extending Kennedy and Eberhart's PSO)
Type	Metaheuristic with Bayesian probabilistic learning	Hybrid metaheuristic — Bayesian probabilistic swarm search
Source fondatrice	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.	Higashi, N., Iba, H. (2003). Particle swarm optimization with Gaussian mutation. Proceedings of the 2003 IEEE Swarm Intelligence Symposium, Indianapolis, IN, USA, pp. 72-79.
Alias	BACO, Bayesian ACO, Bayesian-guided ACO, Probabilistic ACO	Bayesian PSO, BPSO, Probabilistic Swarm Optimization, Prior-guided PSO
Apparentées	5	6
Résumé	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 Particle Swarm Optimization (Bayesian PSO) integrates Bayesian probabilistic reasoning into the standard particle swarm framework. Particles update their velocities and positions guided not only by personal and global best positions but also by a Bayesian posterior that encodes prior knowledge about the solution space, enabling more directed and statistically principled exploration of complex optimization landscapes.