Vertaile menetelmiä
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| Multi-objective cellular automata× | Monitavoiteoptimointi – ristiriitaisten tavoitteiden samanaikainen optimointi× | |
|---|---|---|
| Tieteenala | Simulointi | Simulointi |
| Menetelmäperhe | Process / pipeline | Process / pipeline |
| Syntyvuosi≠ | 1990s–2000s | 1896 (concept); 1989–2002 (evolutionary algorithms era) |
| Kehittäjä≠ | Various (Liu et al., White & Engelen, Clarke et al.) | Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al. |
| Tyyppi≠ | Hybrid simulation-optimization | Optimization framework |
| Alkuperäislähde≠ | 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 ↗ | Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396 |
| Rinnakkaisnimet | MOCA, Multi-objective CA, Multi-criteria cellular automata, MO-CA | MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization |
| Liittyvät≠ | 5 | 3 |
| Tiivistelmä≠ | 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. | Multi-Objective Optimization (MOO) is a mathematical and computational framework for finding solutions that simultaneously optimize two or more conflicting objective functions. Rather than collapsing all goals into a single scalar, MOO produces a set of trade-off solutions — the Pareto front — from which a decision-maker selects according to preference. It is widely used in engineering design, operations research, logistics, economics, and policy analysis. |
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