Process / pipelineSimulation / optimization
鲁棒多目标优化——寻找在不确定性下稳定的帕累托最优解
鲁棒多目标优化（RMOO）是一个框架，用于寻找能够同时优化多个相互冲突的目标，同时对决策变量或问题参数的扰动保持不敏感的解。与经典的多目标优化（MOO）不同，RMOO将不确定性显式地纳入优化循环，生成一个鲁棒的帕累托前沿，其成员不仅在标称设计点表现良好，而且在各种合理的操作条件下也表现良好。
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来源
- Deb, K., & Gupta, H. (2006). Introducing robustness in multi-objective optimization. Evolutionary Computation, 14(4), 463–494. DOI: 10.1162/evco.2006.14.4.463 ↗
- Robust optimization. Wikipedia. link ↗
如何引用本页
ScholarGate. (2026, June 3). Robust Multi-Objective Optimization (RMOO) — optimizing multiple conflicting objectives under uncertainty. ScholarGate. https://scholargate.app/zh/simulation/robust-multi-objective-optimization
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