In this study, we propose a time-varying neural network (TVNN) for solving the time-varying minimum spanning tree problem with constraints (CTMST), which is a variant of the time-varying network minimum spanning tree problem (TNMSTP), a well-known NP-hard problem. Unlike traditional algorithms that use heuristic search, the proposed TVNN is based on time-varying neurons and can achieve parallel computing without any training requirements. Time-varying neurons are novel computational neurons designed in this work. They consist of six parts: input, wave receiver, neuron state, wave generator, wave sender, and output. The parallel computing strategy and self-feedback mechanism of the proposed algorithm greatly improve the response speed and solution accuracy on large-scale time-varying networks. The analysis of time complexity and experimental results on the New York City dataset show that the performance of the proposed algorithm is significantly improved compared with the existing methods.

在这项研究中，我们提出了一种时变神经网络 (TVNN) 来解决带约束的时变最小生成树问题 (CTMST)，它是时变网络最小生成树问题 (TNMSTP) 的变体，众所周知的 NP-hard 问题。与使用启发式搜索的传统算法不同，本文提出的 TVNN 基于时变神经元，无需任何训练即可实现并行计算。时变神经元是在这项工作中设计的新型计算神经元。它们由六个部分组成：输入、波接收器、神经元状态、波发生器、波发送器和输出。该算法的并行计算策略和自反馈机制大大提高了对大规模时变网络的响应速度和求解精度。