标杆管理
公制(单位)
数学优化
帕累托原理
多目标优化
集合(抽象数据类型)
计算机科学
离散化
解决方案集
数学
运营管理
数学分析
业务
经济
营销
程序设计语言
作者
Lennart Schäpermeier,Pascal Kerschke
摘要
Abstract In multi-objective optimization, set-based quality indicators are a cornerstone of benchmarking and performance assessment. They capture the quality of a set of tradeoff solutions by reducing it to a scalar number. One of the most commonly used setbased metrics is the R2 indicator, which describes the expected utility of a solution set to a decision-maker under a distribution of utility functions. Typically, this indicator is applied by discretizing the latter distribution, yielding a weakly Pareto-compliant indicator. In consequence, adding a nondominated or dominating solution to a solution set may — but does not have to — improve the indicator's value. In this paper, we reinvestigate the R2 indicator under the premise that we have a continuous, uniform distribution of (Tchebycheff) utility functions. We analyze its properties in detail, demonstrating that this continuous variant is indeed Pareto-compliant — that is, any beneficial solution will improve the metric's value. Additionally, we provide efficient computational procedures that (a) compute this metric for bi-objective problems in O(NlogN), and (b) can perform incremental updates to the indicator whenever solutions are added to (or removed from) the current set of solutions, without needing to recompute the indicator for the entire set. As a result, this work contributes to the state-of-the-art Pareto-compliant unary performance metrics, such as the hypervolume indicator, offering an efficient and promising alternative.
科研通智能强力驱动
Strongly Powered by AbleSci AI