Quality Indicators for Preference-based Evolutionary Multi-objective Optimization Using a Reference Point: A Review and Analysis
Quality Indicators for Preference-based Evolutionary Multi-objective Optimization Using a Reference Point: A Review and Analysis
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DOI：
10.1109/tevc.2023.3319009
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发表时间：
2023-01
期刊：
影响因子：
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通讯作者：
Ryoji Tanabe;Ke Li
中科院分区：
文献类型：
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作者：
Ryoji Tanabe;Ke Li
已经提出了一些质量指标基准的基于偏好的进化多目标优化算法使用一个参考点。虽然对质量指标进行系统的审查和分析有助于制定基准和实际决策，但这两项工作都没有进行。在这种情况下，首先，本文回顾了现有的区域的利益和质量指标的偏好为基础的进化多目标优化使用的参考点。我们指出，每个质量指标是为不同的兴趣区域设计的。然后，本文研究了质量指标的性质。我们证明了一个成就标量化函数值并不总是与目标空间中从解到参考点的距离一致。我们观察到，根据参考点的位置和帕累托前沿的形状，感兴趣的区域可以是显著不同的。我们确定一些质量指标的不良属性。我们还表明，基于偏好的进化多目标优化算法的排名取决于质量指标的选择。
Some quality indicators have been proposed for benchmarking preference-based evolutionary multi-objective optimization algorithms using a reference point. Although a systematic review and analysis of the quality indicators are helpful for both benchmarking and practical decision-making, neither has been conducted. In this context, first, this paper reviews existing regions of interest and quality indicators for preference-based evolutionary multi-objective optimization using the reference point. We point out that each quality indicator was designed for a different region of interest. Then, this paper investigates the properties of the quality indicators. We demonstrate that an achievement scalarizing function value is not always consistent with the distance from a solution to the reference point in the objective space. We observe that the regions of interest can be significantly different depending on the position of the reference point and the shape of the Pareto front. We identify undesirable properties of some quality indicators. We also show that the ranking of preference-based evolutionary multi-objective optimization algorithms depends on the choice of quality indicators.