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| Format: | Artículo científico |
| Language: | en |
| Published: |
Universidade Federal de Santa Maria
2015
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| Online Access: | https://www.redalyc.org/articulo.oa?id=467547683042 |
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Table of Contents:
- Parallel Genetic Algorithm for Shortest Path Routing Problem with Collaborative Neighbors Reza Roshani Mohammad Karim Sohrabi Estudios Ambientales Fine Grained Genetic Algorithms Shortest path routing Parallel Genetic Algorithm Shortest path routing is generally known as a kind of routing widely availed in computer netwo rks nowadays. Although advantageous algorithms exist for finding the shortest path, however alternative methods may have their own supremacy. In this paper, para llel genetic algorithm for finding the shortest path routing is resorted to. In order to improv e 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 resul t 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. 2015 artículo científico 0100-8307 https://www.redalyc.org/articulo.oa?id=467547683042 en http://www.redalyc.org/revista.oa?id=4675 Ciência e Natura application/pdf Universidade Federal de Santa Maria Ciência e Natura (Brasil) Num.6-2 Vol.37