论文标题
本地搜索的多对象化：多目标梯度下降带来的单目标优化益处
Multiobjectivization of Local Search: Single-Objective Optimization Benefits From Multi-Objective Gradient Descent
论文作者
论文摘要
多模式是优化的最大困难之一，因为本地Optima通常阻止算法取得进展。这不仅挑战可能卡住的当地策略。它还阻碍了元高 - 诸如融合到全球最优的进化算法之类的元赫尔术。在本文中，我们提出了一个新的梯度下降概念，该概念能够逃脱本地陷阱。它依赖于原始问题的多对象化，并应用了最近提出的且在这里进行了稍微修改的多目标本地搜索机构Mogsa。我们使用复杂的可视化技术来解决多目标问题，以证明我们的想法的工作原理。因此，这项工作突出了新见解从多目标转移到单目标域，并提供了第一个视觉证据，表明多对投影可以将单目标局部优化链接在多模式景观中。
Multimodality is one of the biggest difficulties for optimization as local optima are often preventing algorithms from making progress. This does not only challenge local strategies that can get stuck. It also hinders meta-heuristics like evolutionary algorithms in convergence to the global optimum. In this paper we present a new concept of gradient descent, which is able to escape local traps. It relies on multiobjectivization of the original problem and applies the recently proposed and here slightly modified multi-objective local search mechanism MOGSA. We use a sophisticated visualization technique for multi-objective problems to prove the working principle of our idea. As such, this work highlights the transfer of new insights from the multi-objective to the single-objective domain and provides first visual evidence that multiobjectivization can link single-objective local optima in multimodal landscapes.
