Process / pipelineSimulation / optimization
随机NSGA-II — 不确定性下的演化多目标优化
随机NSGA-II扩展了NSGA-II演化算法，以处理噪声、不确定或概率性的目标函数。通过对多个评估中的随机目标进行平均或抽样，它能识别对不确定性具有鲁棒性的帕累托最优解，使其适用于工程设计、供应链和政策优化等实际变异性至关重要的领域。
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Method map
The neighbourhood of related methods — select a node to explore.
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 ↗
- Hughes, E. J. (2001). Evolutionary multi-objective ranking with uncertainty and noise. In Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization (EMO 2001), Lecture Notes in Computer Science, vol. 1993, pp. 329–343. Springer. DOI: 10.1007/3-540-44719-9_23 ↗
如何引用本页
ScholarGate. (2026, June 3). Stochastic Non-dominated Sorting Genetic Algorithm II. ScholarGate. https://scholargate.app/zh/simulation/stochastic-nsga-ii
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- 多目标遗传算法 (MOGA)仿真↔ compare
- Robust NSGA-II仿真↔ compare
- 随机遗传算法仿真↔ compare
- 随机多目标优化仿真↔ compare
- 随机粒子群优化仿真↔ compare