用神经网络求解时间依赖网络最短路径问题的新算法(英文)

A New Algorithm for the Shortest Paths Computation by Neural Networks on Time-dependent Networks

时间依赖的网络与传统的网络模型相比更具有现实意义,具有广泛的应用领域.用实例证明了著名的Dijkstra算法在时间依赖的网络上不能有效地求解最短路径问题,给出了时间依赖的网络的定义和模型,给出一种实用反馈式神经网络来求解时间依赖的网络的最短路径问题.并用模拟实验验证了它在不同的网络更新时间区间上收敛速度的稳定性.结果是神经网络求解非NP 难解类优化问题的一种新尝试.

Time-dependent networks are more practical or immediate significance compared with traditional networks models. There are instances to prove the famous Dijkstra's algorithm cannot be effectively used to solve the shortest path problems. A new kind of neural networks for continuously computing the shortest path on a time-dependent network is presented and the stability of the network is proved. This makes a new study on solving the optimization, but not NP-hard problems by neural networks.