Comparar métodos

	Optimización por Colonia de Hormigas Basada en Agentes	Modelado Basado en Agentes (MBA)
Campo	Simulación	Simulación
Familia	Process / pipeline	Process / pipeline
Año de origen	1992-2004	1970s–1990s (formalized as a field)
Autor original	Dorigo, M. and colleagues; agent-based framing developed in swarm intelligence community	Thomas Schelling and Robert Axelrod (foundational contributions, 1970s–1990s)
Tipo	Metaheuristic optimization — agent-based swarm simulation	Computational simulation method
Fuente seminal	Dorigo, M., Stutzle, T. (2004). Ant Colony Optimization. MIT Press, Cambridge, MA. ISBN: 9780262042192	Axelrod, R. (1997). The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration. Princeton University Press.
Alias	AB-ACO, Agent-Based ACO, Multi-Agent Ant Colony Optimization, MAACO	ABM, Ajan Tabanlı Modelleme (ABM), multi-agent simulation, individual-based modeling
Relacionados	5	5
Resumen	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.	Agent-based modeling (ABM) is a computational simulation method, formalized through the work of Thomas Schelling and Robert Axelrod in the 1970s–1990s, that simulates the behavior of complex systems by specifying and running autonomous agents — individuals, firms, cells, or any bounded entity — whose local interactions with each other and with their environment collectively produce global, system-level patterns that could not be predicted from any single agent's rules alone.