مقایسهٔ روشها
روشهای انتخابی خود را کنار هم مرور کنید؛ ردیفهای متفاوت برجسته شدهاند.
| اتوماتای سلولی مبتنی بر عامل× | اتوماتای سلولی چندهدفه× | |
|---|---|---|
| حوزه | شبیهسازی | شبیهسازی |
| خانواده | Process / pipeline | Process / pipeline |
| سال پیدایش≠ | 1986–1996 | 1990s–2000s |
| پدیدآور≠ | Wolfram, S.; Epstein, J. M. & Axtell, R. | Various (Liu et al., White & Engelen, Clarke et al.) |
| نوع≠ | Hybrid spatial simulation | Hybrid simulation-optimization |
| منبع بنیادین≠ | Wolfram, S. (2002). A New Kind of Science. Wolfram Media, Champaign, IL. ISBN: 978-1579550080 | 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 ↗ |
| نامهای دیگر | ABCA, CA-ABM, Agent-CA, Hybrid Agent-Cellular Automaton | MOCA, Multi-objective CA, Multi-criteria cellular automata, MO-CA |
| مرتبط≠ | 6 | 5 |
| خلاصه≠ | Agent-Based Cellular Automata (ABCA) is a hybrid simulation framework that integrates the local transition rules of cellular automata with the autonomous behavioral logic of agent-based modeling. Cells in a spatial grid both evolve according to neighborhood rules and host agents that perceive, decide, and act, enabling the study of complex spatial phenomena such as land-use change, disease spread, crowd dynamics, and ecosystem evolution. | 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. |
| ScholarGateمجموعهداده ↗ |
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