论文标题
带有三个解决方案集的进化多目标优化算法框架
Evolutionary Multi-Objective Optimization Algorithm Framework with Three Solution Sets
论文作者
论文摘要
在进化多目标优化（EMO）社区中假定，决策者是由由Emo算法获得的非主导解决方案集选择的最终解决方案。要呈现给决策者的解决方案数量可能完全不同。在某些情况下，决策者可能只想检查一些最终解决方案的代表性解决方案。在其他情况下，可能需要大量非主导的解决方案来可视化帕累托阵线。在本文中，我们建议将一般的emo框架与三个溶液集使用相对于所需数量的解决方案处理各种情况。三个解决方案集是EMO算法的主要人群，一个用于存储有希望的解决方案的外部档案，以及呈现给决策者的最终解决方案集。最终解决方案集从存档中选择。因此，只要存档尺寸不小于所需的解决方案数量，人口大小和存档大小就可以任意指定。最终人口不一定是一个很好的解决方案，因为它没有提交决策者。通过计算实验，我们显示了该框架比标准最终人口和最终档案框架的优势。我们还讨论了如何选择最终解决方案集以及如何解释选择的原因，这是迈向可解释的EMO框架的尝试。
It is assumed in the evolutionary multi-objective optimization (EMO) community that a final solution is selected by a decision maker from a non-dominated solution set obtained by an EMO algorithm. The number of solutions to be presented to the decision maker can be totally different. In some cases, the decision maker may want to examine only a few representative solutions from which a final solution is selected. In other cases, a large number of non-dominated solutions may be needed to visualize the Pareto front. In this paper, we suggest the use of a general EMO framework with three solution sets to handle various situations with respect to the required number of solutions. The three solution sets are the main population of an EMO algorithm, an external archive to store promising solutions, and a final solution set which is presented to the decision maker. The final solution set is selected from the archive. Thus the population size and the archive size can be arbitrarily specified as long as the archive size is not smaller than the required number of solutions. The final population is not necessarily to be a good solution set since it is not presented to the decision maker. Through computational experiments, we show the advantages of this framework over the standard final population and final archive frameworks. We also discuss how to select a final solution set and how to explain the reason for the selection, which is the first attempt towards an explainable EMO framework.
