Methoden vergleichen

	Agentenbasierte NSGA-II	Agent-based multi-objective optimization
Fachgebiet	Simulation	Simulation
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	2000s–2010s	1990s–2000s
Urheber	Deb et al. (NSGA-II, 2002); integrated with agent-based modeling frameworks in the 2000s–2010s	Bonabeau, Dorigo, Theraulaz; Coello Coello et al.
Typ	Simulation-embedded evolutionary multi-objective optimizer	Simulation-driven multi-objective search
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.	Bonabeau, E., Dorigo, M., & Theraulaz, G. (2002). Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press. ISBN: 9780195131598
Aliasnamen	AB-NSGA-II, ABM-NSGA2, agent-driven NSGA-II, simulation-based NSGA-II	ABMOO, agent-driven MOO, multi-objective ABM optimization, ABMO
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 multi-objective optimization (ABMOO) embeds autonomous agents inside a simulation environment and evolves their behavior or parameters to simultaneously optimize two or more conflicting objectives, yielding a Pareto-efficient frontier of solutions rather than a single optimum. It is suited to complex adaptive systems where objectives emerge from micro-level interactions rather than closed-form equations.