A Dynamic Neural Network for Solving Time-varying Shortest Path with Hop-constraint

Abstract

This paper proposes a dynamic neural network (DNN) to solve the time-varying shortest path problem with hop-constraint (HC-TSPP). The purpose of HC-TSPP is to find a path with the shortest transmission time and the restricted number of arcs. The proposed DNN is a novel neural network based on dynamic neurons. All neurons on DNN are computing in parallel, and each dynamic neuron is composed of seven parts: input, wave receiver, filter, status memorizer, wave generator, wave sender, and output. Wave is the carrier of neuron communication, and each wave is composed of three parts. The shortest path report is based on the first wave that reaches the destination node and satisfies the hop constraint. The example and experimental results based on the Internet dataset show that the proposed algorithm can arrive at the global optimal solution and outperform the existing algorithm (viz. Dijkstra algorithm).