Solving the Shortest Path Routing Problem Using Noisy Hopfield Neural Networks

2009 ◽  
Author(s):  
Wen Liu ◽  
Lipo Wang
Keyword(s):  
Neural Networks ◽  
Shortest Path ◽  
Hopfield Neural Networks ◽  
Routing Problem ◽  
Shortest Path Routing

scholarly journals A Class of Evolutionary Algorithms for Determining Shortest Path Routing in 5G Ultra Dense Heterogeneous Networks

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Shortest Path ◽  
Optimal Path ◽  
Pso Algorithm ◽  
Small Cells ◽  
5G Networks ◽  
Routing Problem ◽  
Shortest Path Routing ◽  
Individual Fitness ◽  
Evolutionary Optimization Algorithms

Ultra Dense Network (UDN), an important element of the upcoming 5G networks are characterised by extremely dynamic operations due to the presence and mobility of large number of users spread over small cells of varying sizes. It makes optimal path between the source/destination pairs for communication and data transmission be highly dynamic in nature and hence a challenging issue to deal with. Under such dynamic backdrops, routing procedures have to exhibit robustness, scalability and time efficiency in order to ensure seamless link reliability and Quality of Service (QOS) of the network. In the proposed work, the shortest optimal route of the source and destination pair is found using a combination of evolutionary optimization algorithms namely Genetic Algorithm (GA), Particle Swarm Optimization (PSO) Algorithm and our novel hybrid PSOGA approach which searches for an optimized solution by determining cost functions of individual fitness state and comparing states generated between individual solutions. Application of all the three above mentioned algorithms to the Shortest Path Routing Problem in UDNs and the results obtained have shown that the hybrid PSO-GA comparatively provided enhanced optimized solution.

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