Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Programação Dinâmica Estocástica× | Otimização Estocástica Multi-Objetivo× | |
|---|---|---|
| Área | Simulação | Simulação |
| Família | Process / pipeline | Process / pipeline |
| Ano de origem≠ | 1957 | 1990s–2000s |
| Autor original≠ | Bellman, R.; formalized for stochastic settings by Puterman, M. L. | Various (Fonseca, Fleming, Deb, Zitzler, and others) |
| Tipo≠ | Sequential optimization under uncertainty | Stochastic metaheuristic optimization |
| Fonte seminal≠ | Bellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780486428093 | Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396 |
| Outros nomes | SDP, Markov Decision Process, MDP, Stochastic DP | SMOO, Stochastic MOO, Multi-objective optimization under uncertainty, Robust multi-objective optimization |
| Relacionados≠ | 6 | 5 |
| Resumo≠ | Stochastic Dynamic Programming (SDP) is a mathematical optimization framework for sequential decision problems where outcomes are partly random. It extends Bellman's principle of optimality to stochastic environments, representing problems as Markov Decision Processes (MDPs) and computing optimal policies by solving recursive value equations over states and time periods. | 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. |
| ScholarGateConjunto de dados ↗ |
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