论文标题
解决方案子集选择用于进化多目标优化的最终决策
Solution Subset Selection for Final Decision Making in Evolutionary Multi-Objective Optimization
论文作者
论文摘要
通常，多目标优化问题没有一个最佳解决方案，而是一组帕累托最佳解决方案，该解决方案在目标空间中形成了帕累托前部。已经提出了各种进化算法，以使用预先指定的溶液来近似帕累托前沿。数以百计的解决方案是通过单次运行获得的。从获得的解决方案中选择单个最终解决方案是由人类决策者完成的。但是，在许多情况下，决策者不想检查数百种解决方案。因此，需要选择所获得的溶液的一小部分。在本文中，我们从最终决策的角度讨论了子集选择。首先，我们简要说明现有的子集选择研究。接下来，我们为子集选择制定了预期损耗函数。我们还表明，配方函数与IGD Plus指示器相同。然后，我们报告了实验结果，其中将提出的方法与其他基于指标的子集选择方法进行了比较。
In general, a multi-objective optimization problem does not have a single optimal solution but a set of Pareto optimal solutions, which forms the Pareto front in the objective space. Various evolutionary algorithms have been proposed to approximate the Pareto front using a pre-specified number of solutions. Hundreds of solutions are obtained by their single run. The selection of a single final solution from the obtained solutions is assumed to be done by a human decision maker. However, in many cases, the decision maker does not want to examine hundreds of solutions. Thus, it is needed to select a small subset of the obtained solutions. In this paper, we discuss subset selection from a viewpoint of the final decision making. First we briefly explain existing subset selection studies. Next we formulate an expected loss function for subset selection. We also show that the formulated function is the same as the IGD plus indicator. Then we report experimental results where the proposed approach is compared with other indicator-based subset selection methods.
