Methoden vergleichen

	Agentenbasierte NSGA-II	Agentenbasiertes Modellieren (ABM)
Fachgebiet	Simulation	Simulation
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	2000s–2010s	1970s–1990s (formalized as a field)
Urheber	Deb et al. (NSGA-II, 2002); integrated with agent-based modeling frameworks in the 2000s–2010s	Thomas Schelling and Robert Axelrod (foundational contributions, 1970s–1990s)
Typ	Simulation-embedded evolutionary multi-objective optimizer	Computational simulation method
Wegweisende Quelle	Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182-197.	Axelrod, R. (1997). The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration. Princeton University Press.
Aliasnamen	AB-NSGA-II, ABM-NSGA2, agent-driven NSGA-II, simulation-based NSGA-II	ABM, Ajan Tabanlı Modelleme (ABM), multi-agent simulation, individual-based modeling
Verwandt	4	5
Zusammenfassung	Agent-based NSGA-II embeds the NSGA-II evolutionary algorithm inside an agent-based simulation loop so that objective values for each candidate solution are determined by running a full agent simulation rather than by evaluating a closed-form function. This coupling enables multi-objective optimization over systems whose performance emerges from the micro-level interactions of autonomous agents rather than from analytically tractable equations.	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.