Methoden vergleichen

	Multi-Objective Cellular Automata	Multi-Objective Genetic Algorithm (MOGA)
Fachgebiet	Simulation	Simulation
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	1990s–2000s	1984
Urheber	Various (Liu et al., White & Engelen, Clarke et al.)	Schaffer, J. D. (early MOGA); Goldberg, D. E. (GA foundations)
Typ	Hybrid simulation-optimization	Population-based evolutionary optimizer
Wegweisende Quelle	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.	Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Addison-Wesley. ISBN: 9780201157673
Aliasnamen	MOCA, Multi-objective CA, Multi-criteria cellular automata, MO-CA	MOGA, Multi-objective GA, Evolutionary multi-objective optimization, EMO
Verwandt	5	4
Zusammenfassung	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.	A Multi-Objective Genetic Algorithm (MOGA) is an evolutionary computation method that evolves a population of candidate solutions toward a Pareto-optimal front, simultaneously optimizing two or more conflicting objective functions. It avoids collapsing trade-offs into a single score, instead producing a set of non-dominated solutions for the decision-maker to choose among.