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| 결정론적 다목적 최적화× | 불확실성 하에서 다중 상충 목표를 최적화하는 확률적 다목표 최적화× | |
|---|---|---|
| 분야 | 시뮬레이션 | 시뮬레이션 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 1951–1999 | 1990s–2000s |
| 창시자≠ | Kuhn, H. W., Tucker, A. W. (Pareto optimality formalized); Miettinen, K. (systematic deterministic framework) | Various (Fonseca, Fleming, Deb, Zitzler, and others) |
| 유형≠ | Optimization framework — deterministic Pareto and scalarization methods | Stochastic metaheuristic optimization |
| 원전 | Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 978-0-471-87339-6 | Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396 |
| 별칭 | Deterministic MOO, Classical Multi-Objective Optimization, Non-Stochastic MOO, Deterministic Pareto Optimization | SMOO, Stochastic MOO, Multi-objective optimization under uncertainty, Robust multi-objective optimization |
| 관련≠ | 3 | 5 |
| 요약≠ | Deterministic Multi-Objective Optimization (Deterministic MOO) is a family of classical optimization approaches that simultaneously minimize or maximize multiple conflicting objective functions over a deterministic feasible set. It produces a Pareto front — the set of non-dominated solutions — from which a decision-maker selects the preferred trade-off. Unlike stochastic variants, all objective evaluations and constraints are fixed and noise-free. | 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. |
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