Methoden vergleichen

	Agent-based multi-objective optimization	Agentenbasiertes Modellieren (ABM)
Fachgebiet	Simulation	Simulation
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	1990s–2000s	1970s–1990s (formalized as a field)
Urheber	Bonabeau, Dorigo, Theraulaz; Coello Coello et al.	Thomas Schelling and Robert Axelrod (foundational contributions, 1970s–1990s)
Typ	Simulation-driven multi-objective search	Computational simulation method
Wegweisende Quelle	Bonabeau, E., Dorigo, M., & Theraulaz, G. (2002). Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press. ISBN: 9780195131598	Axelrod, R. (1997). The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration. Princeton University Press.
Aliasnamen	ABMOO, agent-driven MOO, multi-objective ABM optimization, ABMO	ABM, Ajan Tabanlı Modelleme (ABM), multi-agent simulation, individual-based modeling
Verwandt	5	5
Zusammenfassung	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.	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.