Methoden vergleichen

	Agentenbasierte Ameisenkolonie-Optimierung	Agentenbasiertes Modellieren (ABM)
Fachgebiet	Simulation	Simulation
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	1992-2004	1970s–1990s (formalized as a field)
Urheber	Dorigo, M. and colleagues; agent-based framing developed in swarm intelligence community	Thomas Schelling and Robert Axelrod (foundational contributions, 1970s–1990s)
Typ	Metaheuristic optimization — agent-based swarm simulation	Computational simulation method
Wegweisende Quelle	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.
Aliasnamen	AB-ACO, Agent-Based ACO, Multi-Agent Ant Colony Optimization, MAACO	ABM, Ajan Tabanlı Modelleme (ABM), multi-agent simulation, individual-based modeling
Verwandt	5	5
Zusammenfassung	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.