scholarly journals An Investigation of Using Parallel Genetic Algorithm for Solving the Shortest Path Routing Problem

2011 ◽  
Vol 7 (2) ◽  
pp. 206-215 ◽  
Author(s):  
Yussof
Keyword(s):  
Genetic Algorithm ◽  
Shortest Path ◽  
Parallel Genetic Algorithm ◽  
Routing Problem ◽  
Shortest Path Routing

scholarly journals Parallel Genetic Algorithm for Shortest Path Routing Problem with Collaborative Neighbors

2015 ◽  
Vol 37 ◽  
pp. 327
Author(s):  
Reza Roshani ◽  
Mohammad Karim Sohrabi
Keyword(s):  
Genetic Algorithm ◽  
Mutation Rate ◽  
Shortest Path ◽  
Migration Rate ◽  
Mesh Network ◽  
Alternative Methods ◽  
Parallel Genetic Algorithm ◽  
Routing Problem ◽  
Shortest Path Routing ◽  
Fine Grained

Shortest path routing is generally known as a kind of routing widely availed in computer networks nowadays. Although advantageous algorithms exist for finding the shortest path, however alternative methods may have their own supremacy. In this paper, parallel genetic algorithm for finding the shortest path routing is resorted to. In order to improve the computation time in this routing algorithm and to distribute the load balance between the processors as well, Fine-Grained parallel GA model is opted for. The proposed algorithm was simulated on Wraparound Mesh network topologies in different sizes. To this end, several experiments were anchored to identify the most influential parameters such as Migration rate, Mutation rate, and Crossover rate. The simulation result shows that best result of mutation rate is: about 0.02 and 0.03, and migration rate for transmission to the neighbor’s node is 3 of the best chromosomes. This study has already shown that through using performance-based GA which uses fine-grained parallel algorithms, timing germane shortest path routing can be improved.

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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