Process / pipelineSimulation / optimization
Robust NSGA-II — 不确定性下的多目标优化
Robust NSGA-II 将经典的 NSGA-II 进化算法扩展至考虑参数不确定性，寻找即使在输入参数偏离其标称值时仍能保持高性能的帕累托最优权衡解。它不以单点优化目标值，而是评估每个候选解在不确定性实现范围或分布下的表现，并同时选择帕累托优势和鲁棒性。
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来源
- Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182-197. DOI: 10.1109/4235.996017 ↗
- 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 ↗
如何引用本页
ScholarGate. (2026, June 3). Robust Non-dominated Sorting Genetic Algorithm II. ScholarGate. https://scholargate.app/zh/simulation/robust-nsga-ii
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