Сравнение методов

	Агентно-ориентированная муравьиная оптимизация	Муравьиные алгоритмы
Область	Имитационное моделирование	Оптимизация
Семейство	Process / pipeline	Process / pipeline
Год появления	1992-2004	1992 (foundational thesis); 1997 (Ant Colony System formalization)
Автор метода	Dorigo, M. and colleagues; agent-based framing developed in swarm intelligence community	—
Тип	Metaheuristic optimization — agent-based swarm simulation	Metaheuristic — swarm intelligence
Основополагающий источник	Dorigo, M., Stutzle, T. (2004). Ant Colony Optimization. MIT Press, Cambridge, MA. ISBN: 9780262042192	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	AB-ACO, Agent-Based ACO, Multi-Agent Ant Colony Optimization, MAACO	ACO, Karınca Kolonisi Optimizasyonu (ACO), ant colony system
Связанные	5	5
Сводка	Agent-Based Ant Colony Optimization (AB-ACO) models individual ants as autonomous agents that probabilistically construct solutions by following and depositing pheromone trails on a search graph. By coupling agent-level behavioral rules with a shared pheromone environment, the collective system converges on high-quality solutions to hard combinatorial and simulation-embedded optimization problems without central coordination.	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.