Performance Metrics for Multiobjective Optimization Under Noise
本文指出常用性能指标未考虑噪声导致的决策者选择误差，并提出了两个改进指标，适用于评估多目标优化算法在噪声下的表现。
This article discusses the challenge when evaluating multiobjective optimization algorithms under noise. It argues that it is important to take into account possible selection errors by a decision maker, due to inaccurate estimates of a solution’s true objective values. It demonstrates that commonly used performance metrics do not properly account for such errors, and proposes two alternative performance metrics that do account for such errors by adapting the popular R2 and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathrm { IGD}}^{+}$ </tex-math></inline-formula> metrics.