论文标题
通过学习到级的交互式进化多目标优化
Interactive Evolutionary Multi-Objective Optimization via Learning-to-Rank
论文作者
论文摘要
在实用的多准则决策中，如果要求决策者（DM）在涵盖整个帕累托最佳阵线的一套权衡替代方案中进行选择，这很麻烦。这是常规进化多目标优化（EMO）的悖论，始终旨在在收敛和多样性之间取得良好的平衡。从本质上讲，多目标优化的最终目标是帮助决策者（DM）确定感兴趣的解决方案（SOI）实现多个冲突标准之间令人满意的权衡。考虑到这一点，本文开发了一个框架，用于设计基于偏好的Emo算法以交互式找到SOI。它的核心思想是让人参与emo循环。每隔几次迭代后，就邀请DM引起她对几个现任候选人的反馈。通过收集此类信息，她的偏好是通过学习到秩的神经网络逐步学习的，然后应用于指导基线表情算法。请注意，此框架是如此一般，以至于可以以插件方式应用任何现有的EMO算法。 $ 48 $基准测试问题具有多达10个目标的实验完全证明了我们提出的算法找到SOI的有效性。
In practical multi-criterion decision-making, it is cumbersome if a decision maker (DM) is asked to choose among a set of trade-off alternatives covering the whole Pareto-optimal front. This is a paradox in conventional evolutionary multi-objective optimization (EMO) that always aim to achieve a well balance between convergence and diversity. In essence, the ultimate goal of multi-objective optimization is to help a decision maker (DM) identify solution(s) of interest (SOI) achieving satisfactory trade-offs among multiple conflicting criteria. Bearing this in mind, this paper develops a framework for designing preference-based EMO algorithms to find SOI in an interactive manner. Its core idea is to involve human in the loop of EMO. After every several iterations, the DM is invited to elicit her feedback with regard to a couple of incumbent candidates. By collecting such information, her preference is progressively learned by a learning-to-rank neural network and then applied to guide the baseline EMO algorithm. Note that this framework is so general that any existing EMO algorithm can be applied in a plug-in manner. Experiments on $48$ benchmark test problems with up to 10 objectives fully demonstrate the effectiveness of our proposed algorithms for finding SOI.
