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| 策略情景多目标优化× | 多目标遗传算法 (MOGA)× | |
|---|---|---|
| 领域 | 仿真 | 仿真 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1990s–2000s | 1984 |
| 提出者≠ | Evolved from multi-objective optimization and policy scenario analysis communities | Schaffer, J. D. (early MOGA); Goldberg, D. E. (GA foundations) |
| 类型≠ | Scenario-conditioned multi-objective search | Population-based evolutionary optimizer |
| 开创性文献≠ | Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons, Chichester. ISBN: 9780471873396 | Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Addison-Wesley. ISBN: 9780201157673 |
| 别名 | PS-MOO, Policy-Driven MOO, Scenario-Based Multi-Objective Optimization, Policy MOO | MOGA, Multi-objective GA, Evolutionary multi-objective optimization, EMO |
| 相关 | 4 | 4 |
| 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. | A Multi-Objective Genetic Algorithm (MOGA) is an evolutionary computation method that evolves a population of candidate solutions toward a Pareto-optimal front, simultaneously optimizing two or more conflicting objective functions. It avoids collapsing trade-offs into a single score, instead producing a set of non-dominated solutions for the decision-maker to choose among. |
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