方法对比
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| 多目标敏感性分析× | 多目标优化× | |
|---|---|---|
| 领域 | 仿真 | 仿真 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1990s–2000s | 1896 (concept); 1989–2002 (evolutionary algorithms era) |
| 提出者≠ | Evolved from classical sensitivity analysis (Saltelli et al.) combined with multi-objective optimization (Pareto, 1896) | Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al. |
| 类型≠ | Analytical technique — parametric sensitivity across multiple objectives | Optimization framework |
| 开创性文献≠ | Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. Wiley, Chichester. ISBN: 9780470059975 | Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396 |
| 别名 | MOSA, Multi-criteria sensitivity analysis, Pareto sensitivity analysis, Multi-objective SA | MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization |
| 相关≠ | 4 | 3 |
| 摘要≠ | Multi-Objective Sensitivity Analysis (MOSA) examines how changes in model parameters, weights, or assumptions affect an entire set of competing objectives simultaneously. Rather than asking how a single output shifts, MOSA tracks changes in the Pareto front or trade-off surface, revealing which parameters most destabilize multi-objective solutions and where decision-maker choices are robust versus fragile. | 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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