Porovnať metódy
Prezrite si vybrané metódy vedľa seba; riadky, ktoré sa líšia, sú zvýraznené.
| Viacobjektové celulárne automaty× | Bunkové automaty× | |
|---|---|---|
| Odbor | Simulácia | Simulácia |
| Rodina | Process / pipeline | Process / pipeline |
| Rok vzniku≠ | 1990s–2000s | 1940s–1950s (formalized); 1970 (Conway's Game of Life); 2002 (Wolfram's systematic classification) |
| Tvorca≠ | Various (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) |
| Typ≠ | Hybrid simulation-optimization | Grid-based computational simulation model |
| Pôvodný zdroj≠ | 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 ↗ | Wolfram, S. (2002). A New Kind of Science. Wolfram Media. ISBN: 978-1579550080 |
| Ďalšie názvy | MOCA, Multi-objective CA, Multi-criteria cellular automata, MO-CA | CA, Hücresel Otomat (Cellular Automata), lattice model, grid-based simulation |
| Príbuzné | 5 | 5 |
| Zhrnutie≠ | 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. | 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. |
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