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| Стохастична многокритериална оптимизация× | Многоцелева оптимизация× | |
|---|---|---|
| Област | Симулационно моделиране | Симулационно моделиране |
| Семейство | Process / pipeline | Process / pipeline |
| Година на възникване≠ | 1990s–2000s | 1896 (concept); 1989–2002 (evolutionary algorithms era) |
| Създател≠ | Various (Fonseca, Fleming, Deb, Zitzler, and others) | Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al. |
| Тип≠ | Stochastic metaheuristic optimization | Optimization framework |
| Основополагащ източник | Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396 | Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396 |
| Други названия | SMOO, Stochastic MOO, Multi-objective optimization under uncertainty, Robust multi-objective optimization | MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization |
| Свързани≠ | 5 | 3 |
| 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. | 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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