The result of a multiobjective or a many-objective optimization problem is a large set of non-dominated solutions. Once the Pareto Front (or a good approximation of it) has been found, then providing the decision maker with a smaller set of “interesting solutions” is a key step. Here, the focus is on how to select such a set of solutions of interest which, in contrast to previous approaches that relied on geometrical features, is carried out considering the decision maker’s preferences. The proposed a posteriori approach consists in assigning an interval of potential scores to every solution, where such scores depend on the decision maker’s preferences. The solutions are then compared and filtered according to their corresponding intervals, using a recently proposed possibility degree formula. Three examples, with two, three and many objectives are used to show the benefits of the proposal.

多目标或多目标优化问题的结果是大量的非支配解。一旦找到了帕累托阵线（或近似的帕累托阵线），那么为决策者提供一整套较小的“有趣的解决方案”就是关键一步。在此，重点在于如何选择这样一组感兴趣的解决方案，与依赖于几何特征的先前方法相比，该解决方案是在考虑决策者的偏好的情况下进行的。建议的后验方法包括为每个解决方案分配一个潜在得分间隔，其中这些得分取决于决策者的偏好。然后使用最近提出的可能性度公式，根据相应的时间间隔对解决方案进行比较和过滤。用三个，两个，三个和许多目标的例子来说明该提案的好处。