Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Multi-Objective Agent-Based Modeling× | Multi-Objective Optimization× | |
|---|---|---|
| Vakgebied | Simulatie | Simulatie |
| Familie | Process / pipeline | Process / pipeline |
| Jaar van ontstaan≠ | 2001-2006 | 1896 (concept); 1989–2002 (evolutionary algorithms era) |
| Grondlegger≠ | Deb, K.; Tesfatsion, L. et al. | Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al. |
| Type≠ | Simulation-optimization hybrid | Optimization framework |
| Oorspronkelijke bron≠ | Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons, Chichester. ISBN: 9780471873396 | Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396 |
| Aliassen | MO-ABM, Multi-objective ABM, Pareto-based agent-based modeling, Multi-objective agent simulation | MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization |
| Verwant≠ | 4 | 3 |
| Samenvatting≠ | Multi-Objective Agent-Based Modeling (MO-ABM) couples agent-based simulation with multi-objective optimization to simultaneously optimize several conflicting performance criteria across complex adaptive systems. Autonomous agents interact according to behavioral rules while an optimizer searches for parameter configurations that achieve Pareto-optimal trade-offs among competing system-level goals. | 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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