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| Стохастична многокритериална оптимизация× | Стохастично динамично програмиране× | |
|---|---|---|
| Област | Симулационно моделиране | Симулационно моделиране |
| Семейство | Process / pipeline | Process / pipeline |
| Година на възникване≠ | 1990s–2000s | 1957 |
| Създател≠ | Various (Fonseca, Fleming, Deb, Zitzler, and others) | Bellman, R.; formalized for stochastic settings by Puterman, M. L. |
| Тип≠ | Stochastic metaheuristic optimization | Sequential optimization under uncertainty |
| Основополагащ източник≠ | Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396 | Bellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780486428093 |
| Други названия | SMOO, Stochastic MOO, Multi-objective optimization under uncertainty, Robust multi-objective optimization | SDP, Markov Decision Process, MDP, Stochastic DP |
| Свързани≠ | 5 | 6 |
| Резюме≠ | 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. | 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. |
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