Falcón-Cardona J G, Emmerich M T M, Coello C A Coello
Computer Science Department, CINVESTAV-IPN, Mexico City, 07360, Mexico
LIACS, Leiden University, Leiden, 2333, The Netherlands
Evol Comput. 2022 Sep 1;30(3):381-408. doi: 10.1162/evco_a_00307.
The most relevant property that a quality indicator (QI) is expected to have is Pareto compliance, which means that every time an approximation set strictly dominates another in a Pareto sense, the indicator must reflect this. The hypervolume indicator and its variants are the only unary QIs known to be Pareto-compliant but there are many commonly used weakly Pareto-compliant indicators such as R2, IGD+, and ε+. Currently, an open research area is related to finding new Pareto-compliant indicators whose preferences are different from those of the hypervolume indicator. In this article, we propose a theoretical basis to combine existing weakly Pareto-compliant indicators with at least one being Pareto-compliant, such that the resulting combined indicator is Pareto-compliant as well. Most importantly, we show that the combination of Pareto-compliant QIs with weakly Pareto-compliant indicators leads to indicators that inherit properties of the weakly compliant indicators in terms of optimal point distributions. The consequences of these new combined indicators are threefold: (1) to increase the variety of available Pareto-compliant QIs by correcting weakly Pareto-compliant indicators, (2) to introduce a general framework for the combination of QIs, and (3) to generate new selection mechanisms for multiobjective evolutionary algorithms where it is possible to achieve/adjust desired distributions on the Pareto front.
质量指标(QI)预期应具备的最相关属性是符合帕累托原则,这意味着每当一个近似集在帕累托意义上严格优于另一个时,该指标必须反映这一点。超体积指标及其变体是已知的仅有的符合帕累托原则的一元质量指标,但有许多常用的弱帕累托符合指标,如R2、IGD +和ε +。目前,一个开放的研究领域是寻找偏好与超体积指标不同的新的符合帕累托原则的指标。在本文中,我们提出了一个理论基础,将现有的弱帕累托符合指标与至少一个符合帕累托原则的指标相结合,使得得到的组合指标也符合帕累托原则。最重要的是,我们表明符合帕累托原则的质量指标与弱帕累托符合指标的组合会产生在最优点分布方面继承弱符合指标属性的指标。这些新的组合指标的影响有三个方面:(1)通过修正弱帕累托符合指标来增加可用的符合帕累托原则的质量指标的种类,(2)引入质量指标组合的通用框架,(3)为多目标进化算法生成新的选择机制,在帕累托前沿有可能实现/调整期望的分布。