Methoden vergleichen

	Agentenbasierte zelluläre Automaten	Multi-Objective Cellular Automata
Fachgebiet	Simulation	Simulation
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	1986–1996	1990s–2000s
Urheber	Wolfram, S.; Epstein, J. M. & Axtell, R.	Various (Liu et al., White & Engelen, Clarke et al.)
Typ	Hybrid spatial simulation	Hybrid simulation-optimization
Wegweisende Quelle	Wolfram, S. (2002). A New Kind of Science. Wolfram Media, Champaign, IL. ISBN: 978-1579550080	Liu, X., Liang, X., Li, X., Xu, X., Ou, J., Chen, Y., Li, S., Wang, S., Pei, F. (2017). A future land use simulation model (FLUS) for simulating multiple land use scenarios by coupling human and natural effects. Landscape and Urban Planning, 168, 94-116.
Aliasnamen	ABCA, CA-ABM, Agent-CA, Hybrid Agent-Cellular Automaton	MOCA, Multi-objective CA, Multi-criteria cellular automata, MO-CA
Verwandt	6	5
Zusammenfassung	Agent-Based Cellular Automata (ABCA) is a hybrid simulation framework that integrates the local transition rules of cellular automata with the autonomous behavioral logic of agent-based modeling. Cells in a spatial grid both evolve according to neighborhood rules and host agents that perceive, decide, and act, enabling the study of complex spatial phenomena such as land-use change, disease spread, crowd dynamics, and ecosystem evolution.	Multi-Objective Cellular Automata (MOCA) couples the bottom-up spatial dynamics of cellular automata with multi-objective optimization to simultaneously pursue competing goals — such as maximizing urban compactness while minimizing ecosystem loss. Each grid cell updates its state based on transition rules that are calibrated or steered to satisfy a Pareto-optimal trade-off among two or more objectives, making the method widely used in land-use change simulation, urban growth modeling, and spatial planning under conflicting demands.