A comparative study of multiobjective computational intelligence algorithms to find the solution to the RWA problem in WDM networks
This paper presents a comparative study of multiobjective algorithms to solve the routing and wavelength assignment problem in optical networks. The study evaluates five computational intelligence algorithms, namely: the Firefly Algorithm, the Differential Evolutionary Algorithm, the Simulated Annea...
- Autores:
-
Montes Castañeda, Bryan
Patiño Garzón, Jorge
Puerto Leguizamón, Gustavo
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2015
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/60633
- Acceso en línea:
- https://repositorio.unal.edu.co/handle/unal/60633
http://bdigital.unal.edu.co/58965/
- Palabra clave:
- 62 Ingeniería y operaciones afines / Engineering
multiobjective algorithms
optical networks
heuristic algorithms
RWA problem.
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
Summary: | This paper presents a comparative study of multiobjective algorithms to solve the routing and wavelength assignment problem in optical networks. The study evaluates five computational intelligence algorithms, namely: the Firefly Algorithm, the Differential Evolutionary Algorithm, the Simulated Annealing Algorithm and two versions of the Particle Swarm Optimization algorithm. Each algorithm is assessed based on the performance provided by two different network topologies under different data traffic loads and with a different number of wavelengths available in the network. The impact of implementing wavelength conversion processes is also taken into account in this study. Simulated results show that, in general, the evaluated algorithms appropriately solve the problem in small-sized networks in which a similar performance was found. However, key differences were found when the size of the network is significant. This means that more suitable algorithms optimize the search space and the fall into local minimums is avoided. |
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