Сравнение методов

	Агентно-ориентированная муравьиная оптимизация	Многокритериальная оптимизация методами роя муравьев (MOACO)
Область	Имитационное моделирование	Имитационное моделирование
Семейство	Process / pipeline	Process / pipeline
Год появления	1992-2004	1999
Автор метода	Dorigo, M. and colleagues; agent-based framing developed in swarm intelligence community	Gambardella, Taillard & Agazzi; Dorigo & Stützle
Тип	Metaheuristic optimization — agent-based swarm simulation	Population-based metaheuristic
Основополагающий источник	Dorigo, M., Stutzle, T. (2004). Ant Colony Optimization. MIT Press, Cambridge, MA. ISBN: 9780262042192	Gambardella, L. M., Taillard, E., & Agazzi, G. (1999). MACS-VRPTW: A multiple ant colony system for vehicle routing problems with time windows. In D. Corne, M. Dorigo, & F. Glover (Eds.), New Ideas in Optimization (pp. 63–76). McGraw-Hill.
Другие названия	AB-ACO, Agent-Based ACO, Multi-Agent Ant Colony Optimization, MAACO	MOACO, Multi-Objective ACO, Pareto Ant Colony Optimization, Multi-objective ACO
Связанные	5	4
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.	Multi-Objective Ant Colony Optimization (MOACO) is a swarm-intelligence metaheuristic that extends the classic Ant Colony Optimization framework to simultaneously optimize two or more conflicting objectives. Artificial ants construct candidate solutions guided by pheromone trails and heuristic information, progressively building an archive of Pareto-optimal solutions rather than converging to a single best answer.