A wave delay neural network for solving label-constrained shortest route query on time-varying communication networks

The focus of the research is on the label-constrained time-varying shortest route query problem on time-varying communication networks. To the best of our knowledge, research on this issue is still relatively limited, and similar studies have the drawbacks of low solution accuracy and slow computati...

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Vydané v:PeerJ. Computer science Ročník 10; s. e2116
Hlavní autori: Han, Bing, Fu, Qiang, Zhang, Xinliang
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States PeerJ. Ltd 12.06.2024
PeerJ Inc
Predmet:
Algorithms
Analysis
Artificial Intelligence
Auto-wave neuron
Computer Networks and Communications
Data Mining and Machine Learning
Label-constrained time-varying shortest route query
Neural Networks
Neurons
Time-varying network
Wave delay neural network
Label-constrained time-varying shortest route query
Time-varying network
Artificial intelligence
Auto-wave neuron
Wave delay neural network
ISSN:2376-5992, 2376-5992
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Shrnutí:The focus of the research is on the label-constrained time-varying shortest route query problem on time-varying communication networks. To the best of our knowledge, research on this issue is still relatively limited, and similar studies have the drawbacks of low solution accuracy and slow computational speed. In this study, a wave delay neural network (WDNN) framework and corresponding algorithms is proposed to effectively solve the label-constrained time-varying shortest routing query problem. This framework accurately simulates the time-varying characteristics of the network without any training requirements. WDNN adopts a new type of wave neuron, which is independently designed and all neurons are parallelly computed on WDNN. This algorithm determines the shortest route based on the waves received by the destination neuron (node). Furthermore, the time complexity and correctness of the proposed algorithm were analyzed in detail in this study, and the performance of the algorithm was analyzed in depth by comparing it with existing algorithms on randomly generated and real networks. The research results indicate that the proposed algorithm outperforms current existing algorithms in terms of response speed and computational accuracy.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 23
ISSN:2376-5992
2376-5992
DOI:10.7717/peerj-cs.2116