论文标题
关于解决单目标问题的多物体化益处的实证研究
Empirical Study on the Benefits of Multiobjectivization for Solving Single-Objective Problems
论文作者
论文摘要
在处理连续的单目标问题时，多模式为全球优化带来了最大的困难之一。当地的Optima通常会阻止算法取得进步，从而构成严重威胁。在本文中，我们分析了单目标优化如何通过考虑额外的目标来从多物体注射化中受益。通过基于多目标梯度的复杂可视化技术的使用，并研究了出现的多目标景观的特性。我们将凭经验表明，多目标优化器MOGSA能够利用这些属性来克服本地陷阱。 MOGSA的性能在可可平台提供的几个功能的测试床上进行了评估。将结果与局部优化器Nelder-Mead进行比较。
When dealing with continuous single-objective problems, multimodality poses one of the biggest difficulties for global optimization. Local optima are often preventing algorithms from making progress and thus pose a severe threat. In this paper we analyze how single-objective optimization can benefit from multiobjectivization by considering an additional objective. With the use of a sophisticated visualization technique based on the multi-objective gradients, the properties of the arising multi-objective landscapes are illustrated and examined. We will empirically show that the multi-objective optimizer MOGSA is able to exploit these properties to overcome local traps. The performance of MOGSA is assessed on a testbed of several functions provided by the COCO platform. The results are compared to the local optimizer Nelder-Mead.
