ScholarGate
Assistent

Sammenlign metoder

Gennemgå dine valgte metoder side om side; rækker, der afviger, er fremhævet.

Multi-Objective Celleautomater×Cellulære Automater×
FagområdeSimuleringSimulering
FamilieProcess / pipelineProcess / pipeline
Oprindelsesår1990s–2000s1940s–1950s (formalized); 1970 (Conway's Game of Life); 2002 (Wolfram's systematic classification)
OphavspersonVarious (Liu et al., White & Engelen, Clarke et al.)John von Neumann and Stanislaw Ulam (1940s–1950s); popularized by John Conway (1970) and Stephen Wolfram (1980s–2002)
TypeHybrid simulation-optimizationGrid-based computational simulation model
Oprindelig kildeLiu, 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 ↗Wolfram, S. (2002). A New Kind of Science. Wolfram Media. ISBN: 978-1579550080
AliasserMOCA, Multi-objective CA, Multi-criteria cellular automata, MO-CACA, Hücresel Otomat (Cellular Automata), lattice model, grid-based simulation
Relaterede55
ResuméMulti-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.Cellular automata (CA) is a grid-based computational simulation model, first formalized by John von Neumann and Stanislaw Ulam in the 1940s–1950s and brought to wide attention by John Conway's Game of Life (1970) and Stephen Wolfram's systematic classification (2002), in which a lattice of cells — each holding a finite discrete state — evolves in discrete time steps according to local neighborhood interaction rules, causing complex global patterns to emerge from simple local specifications.
ScholarGateDatasæt
  1. v1
  2. 2 Kilder
  3. PUBLISHED
  1. v1
  2. 2 Kilder
  3. PUBLISHED

Gå til søgning Hent slides

ScholarGateSammenlign metoder: Multi-objective cellular automata · Cellular Automata. Hentet 2026-06-17 fra https://scholargate.app/da/compare