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| 策略情景多目标优化× | 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 multi-objective optimization and policy scenario analysis communities | Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al. |
| 类型≠ | Scenario-conditioned multi-objective search | Optimization framework |
| 开创性文献≠ | Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons, Chichester. ISBN: 9780471873396 | Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396 |
| 别名 | PS-MOO, Policy-Driven MOO, Scenario-Based Multi-Objective Optimization, Policy MOO | MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization |
| 相关≠ | 4 | 3 |
| 摘要≠ | Policy Scenario Multi-Objective Optimization (PS-MOO) integrates explicit policy scenario construction with multi-objective optimization to identify Pareto-optimal policy options across plausible future states. Decision-makers evaluate trade-offs between competing objectives — such as economic efficiency, equity, and environmental impact — for each distinct policy scenario, then compare Pareto fronts to select robust or scenario-contingent strategies. | 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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