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Multi-Objective Celleautomater — Rumlig simulering styret af flere konkurrerende mål

Multi-Objective Cellular Automata (MOCA) kobler den bottom-up rumlige dynamik af celleautomater med multi-objektiv optimering for samtidigt at forfølge konkurrerende mål — såsom maksimering af bymæssig kompakhed, mens økosystemtab minimeres. Hver gittercelle opdaterer sin tilstand baseret på overgangsregler, der er kalibreret eller styret til at tilfredsstille en Pareto-optimal afvejning mellem to eller flere mål, hvilket gør metoden udbredt inden for arealanvendelsesændringssimulering, byvækstmodellering og rumlig planlægning under modstridende krav.

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  1. 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. DOI: 10.1016/j.landurbplan.2017.09.019
  2. Jantz, C. A., Goetz, S. J., Shelley, M. K. (2004). Using the SLEUTH urban growth model to simulate the impacts of future policy scenarios on urban land use in the Baltimore-Washington metropolitan area. Environment and Planning B: Planning and Design, 31(2), 251-271. DOI: 10.1068/b2983

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ScholarGate. (2026, June 3). Multi-Objective Cellular Automata — Simulation-based spatial optimization with multiple competing objectives. ScholarGate. https://scholargate.app/da/simulation/multi-objective-cellular-automata

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ScholarGateMulti-objective cellular automata (Multi-Objective Cellular Automata — Simulation-based spatial optimization with multiple competing objectives). Hentet 2026-06-15 fra https://scholargate.app/da/simulation/multi-objective-cellular-automata · Datasæt: https://doi.org/10.5281/zenodo.20539026