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R2 v2:符合帕雷托的R2指标,用于在双目标优化中更好地进行基准测试
Lennart Schäpermeier1, Pascal Kerschke2
1Big Data Analytics in Transportation, TU Dresden, 01062 Dresden, Germany, ScaDS.AI Dresden/Leipzig, 01062 Dresden, Germany lennart.schaepermeier@tu-dresden.de.
Evolutionary computation
|October 9, 2025
概括
本研究介绍了R2指标的连续变体,用于多目标优化. 这一新指标符合帕雷托标准并具有计算效率,为评估解决方案集质量提供了改进的替代方案.
科学领域:
- 多目标优化多目标优化
- 决策分析 决策分析
- 计算智能是一种计算智能.
背景情况:
- 基于集的质量指标对于评估多目标优化解决方案至关重要.
- 传统的R2指标虽然很常见,但由于分布分类,它只能很弱地符合帕雷托标准.
- 这种限制意味着添加更好的解决方案可能并不总是提高R2得分.
研究的目的:
- 通过连续,均的实用函数分布,重新研究R2指标.
- 开发一个严格符合帕雷托标准的R2指标版本.
- 为这个改进的指标提供高效的计算方法.
主要方法:
- 在切比切夫实用函数的连续均分布下分析R2指标属性.
- 开发一个O ((NlogN) 算法,用于计算双目标问题的指标.
- 实施增量更新程序来添加/删除解决方案.
主要成果:
- 连续R2指标被证明是严格符合帕雷托标准的.
- 为双目标问题建立了高效的计算程序.
- 增量更新在解决方案集发生变化时显著降低了重新计算成本.
结论:
- 连续的R2指标提供了一个理论上可靠且实际上高效的性能指标.
- 它作为一个有前途的替代方案,以现有的帕雷托符合指标,如超大容量指标.
- 这项工作推进了多目标优化中的绩效评估领域.
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