An adaptive time-varying neural network for solving K optimal time-varying destroy locations query problem

Abstract

The time-varying and destroying are two real existing factors that are generally ignored in the existing k optimal locations query study, which makes the existing study unapplicable to real-world environments. This study presents a novel adaptive time-varying neural network (ATNN) to solve the k optimal locations query problem on time-varying destroy networks. ATNN is a structure-adaptive neural network that is composed of newly designed adaptive time-varying neurons; it does not require training and can automatically construct or adjust a topological structure according to different topologies and scales of time-varying and destroying networks. Adaptive time-varying neurons consist of five layers: wave receiving layer, state verification layer, state storage layer, wave generation layer, and wave sending layer, all those five layers implements the information exchange and processing between neurons as well as the representation of the time-varying and destroying properties of the network. The proposed algorithm has been theoretically proven through time-complexity analysis, correctness analysis, and a numerical example, and its performance has been further confirmed through experiments on public road network datasets.