方法对比
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| 多目标元胞自动机× | 多目标遗传算法 (MOGA)× | |
|---|---|---|
| 领域 | 仿真 | 仿真 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1990s–2000s | 1984 |
| 提出者≠ | Various (Liu et al., White & Engelen, Clarke et al.) | Schaffer, J. D. (early MOGA); Goldberg, D. E. (GA foundations) |
| 类型≠ | Hybrid simulation-optimization | Population-based evolutionary optimizer |
| 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. DOI ↗ | Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Addison-Wesley. ISBN: 9780201157673 |
| 别名 | MOCA, Multi-objective CA, Multi-criteria cellular automata, MO-CA | MOGA, Multi-objective GA, Evolutionary multi-objective optimization, EMO |
| 相关≠ | 5 | 4 |
| 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. | 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. |
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