Сравнение методов

	Байесовская оптимизация роем муравьев	Муравьиные алгоритмы
Область	Имитационное моделирование	Оптимизация
Семейство	Process / pipeline	Process / pipeline
Год появления	1996 (ACO); Bayesian variant: 2000s	1992 (foundational thesis); 1997 (Ant Colony System formalization)
Автор метода	Dorigo, M. et al. (ACO); Bayesian extensions by multiple researchers in the 2000s–2010s	—
Тип	Metaheuristic with Bayesian probabilistic learning	Metaheuristic — swarm intelligence
Основополагающий источник	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.	Dorigo, M. & Gambardella, L.M. (1997). Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem. IEEE Transactions on Evolutionary Computation, 1(1), 53-66.
Другие названия	BACO, Bayesian ACO, Bayesian-guided ACO, Probabilistic ACO	ACO, Karınca Kolonisi Optimizasyonu (ACO), ant colony system
Связанные	5	5
Сводка	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.	Ant Colony Optimization (ACO) is a metaheuristic algorithm introduced by Marco Dorigo and colleagues in the early 1990s that solves combinatorial optimisation problems by simulating the collective foraging behaviour of ants. Real ants lay pheromone trails on paths and preferentially follow stronger trails; ACO turns this positive-feedback mechanism into a search procedure that finds high-quality solutions to graph-structured problems such as the Travelling Salesman Problem, vehicle routing, and scheduling.