基于定制深度学习网络的旅行军官问题近似优化解
Approximating Optimisation Solutions for Travelling Officer Problem with Customised Deep Learning Network
摘要：
深度学习已经扩展到许多新的领域，并取得了巨大的成功，尽管一些传统的定向问题，如旅行推销员问题（TSP）及其变体，通常并不使用这些技术来解决。深度神经网络（DNN）由于其强大的函数逼近能力和快速的前馈计算，是解决这些问题的潜在有前途和未开发的解决方案。在本文中，我们概述了一种将定向运动问题转化为分类问题的方法，并设计了一个定制的多层深度学习网络来近似传统的优化解决方案。我们在真实世界的停车违规数据集上测试了网络的性能，并进行了一项通用研究，以经验为基础展示了影响网络性能的关键架构组件。
英文摘要：
Deep learning has been extended to a number of new domains with critical success, though some traditional orienteering problems such as the Travelling Salesman Problem (TSP) and its variants are not commonly solved using such techniques. Deep neural networks (DNNs) are a potentially promising and under-explored solution to solve these problems due to their powerful function approximation abilities, and their fast feed-forward computation. In this paper, we outline a method for converting an orienteering problem into a classification problem, and design a customised multi-layer deep learning network to approximate traditional optimisation solutions to this problem. We test the performance of the network on a real-world parking violation dataset, and conduct a generic study that empirically shows the critical architectural components that affect network performance for this problem.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
