方法对比

	多目标元胞自动机	元胞自动机
领域	仿真	仿真
方法族	Process / pipeline	Process / pipeline
起源年份	1990s–2000s	1940s–1950s (formalized); 1970 (Conway's Game of Life); 2002 (Wolfram's systematic classification)
提出者	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)
类型	Hybrid simulation-optimization	Grid-based computational simulation model
开创性文献	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.	Wolfram, S. (2002). A New Kind of Science. Wolfram Media. ISBN: 978-1579550080
别名	MOCA, Multi-objective CA, Multi-criteria cellular automata, MO-CA	CA, Hücresel Otomat (Cellular Automata), lattice model, grid-based simulation
相关	5	5
摘要	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.