Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Otimização Estocástica Multi-Objetivo× | Simulação de Monte Carlo× | |
|---|---|---|
| Área≠ | Simulação | Tomada de decisão |
| Família≠ | Process / pipeline | MCDM |
| Ano de origem≠ | 1990s–2000s | 1949 |
| Autor original≠ | Various (Fonseca, Fleming, Deb, Zitzler, and others) | Metropolis, N., Ulam, S. |
| Tipo≠ | Stochastic metaheuristic optimization | Robustness wrapper — Monte Carlo uncertainty propagation |
| Fonte seminal≠ | Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396 | Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI ↗ |
| Outros nomes≠ | SMOO, Stochastic MOO, Multi-objective optimization under uncertainty, Robust multi-objective optimization | — |
| Relacionados≠ | 5 | 0 |
| Resumo≠ | Stochastic Multi-Objective Optimization (SMOO) is a class of methods that simultaneously optimizes two or more conflicting objectives when parameters, costs, or constraints are uncertain or random. Rather than a single optimal solution, it produces a Pareto front of non-dominated solutions, each representing a different balance among objectives under the modeled uncertainty. | MONTE-CARLO-SIMULATION (Monte Carlo Simulation — Stochastic uncertainty propagation through MCDM model) is a ranking multi-criteria decision-making (MCDM) method introduced by Metropolis, N., Ulam, S. in 1949. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result. |
| ScholarGateConjunto de dados ↗ |
|
|