Compară metode

	Automate Celulare Multi-Obiectiv	Optimizare Multi-Obiectiv
Domeniu	Simulare	Simulare
Familie	Process / pipeline	Process / pipeline
Anul apariției	1990s–2000s	1896 (concept); 1989–2002 (evolutionary algorithms era)
Autorul original	Various (Liu et al., White & Engelen, Clarke et al.)	Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al.
Tip	Hybrid simulation-optimization	Optimization framework
Sursa seminală	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.	Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396
Denumiri alternative	MOCA, Multi-objective CA, Multi-criteria cellular automata, MO-CA	MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization
Înrudite	5	3
Rezumat	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.