Use of Computational Intelligence Techniques for Designing Optical Networks*

<p>This paper presents an evaluation performance of computational intelligence algorithms based on the multiobjective theory for the solution of the Routing and Wavelength Assignment problem (RWA) in optical networks. The study evaluates the Firefly Algorithm, the Differential Evolutionary Algorithm, the Simulated Annealing Algorithm and two versions of the Particle Swarm Optimization algorithm. The paper provides a description of the multiobjective algorithms; then, an evaluation based on the performance provided by the multiobjective algorithms versus mono-objective approaches when dealing with different traffic loads, different numberof wavelengths and wavelength conversion process over the NSFNet topology is presented. Simulation results show that monoobjective algorithms properly solve the RWA problem for low values of data traffic and low number of wavelengths. However, the multiobjective approaches adapt better to online traffic when the number of wavelengths available in the network increases as well as when wavelength conversion is implemented in the nodes.</p>

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Applications of Computational Intelligence to Impairment-Aware Routing and Wavelength Assignment in Optical Networks

Intelligent Systems for Optical Networks Design ◽

10.4018/978-1-4666-3652-1.ch009 ◽

2013 ◽

pp. 194-216

Author(s):

Joaquim F. Martins-Filho ◽

Carmelo J. A. Bastos-Filho ◽

Daniel A. R. Chaves ◽

Helder A. Pereira

Keyword(s):

Optical Networks ◽

Computational Intelligence ◽

Assignment Problem ◽

Routing And Wavelength Assignment ◽

Wavelength Assignment ◽

Evolutionary Strategy ◽

Wavelength Converters ◽

Routing Problem ◽

Hard Problems ◽

Logical Topology

Computational intelligence techniques have been used to solve hard problems in optical networks, such as the routing and wavelength assignment problem, the design of the physical and the logical topology of these networks, and the placement of some high cost devices along the network when it is necessary, such as regenerators and wavelength converters. In this chapter, the authors concentrate on the application of computational intelligence to solve the impairment-aware routing and wavelength assignment problem. They present a brief survey on this topic and a detailed description and results for two applications of computational intelligence, one to solve the wavelength assignment problem with an evolutionary strategy approach and the other to tackle the routing problem using ant colony optimization.

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Applications of computational intelligence in optical networks

2011 13th International Conference on Transparent Optical Networks ◽

10.1109/icton.2011.5970869 ◽

2011 ◽

Author(s):

Joaquim F. Martins-Filho ◽

Daniel A. R. Chaves ◽

Carmelo J. A. Bastos-Filho ◽

Helder A. Pereira

Keyword(s):

Optical Networks ◽

Computational Intelligence

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Computational Intelligence in Photonics Technology and Optical Networks: A Survey and Future Perspectives

2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA) ◽

10.1109/citisia50690.2020.9371775 ◽

2020 ◽

Author(s):

Gigi Varghese ◽

Nurfiana

Keyword(s):

Optical Networks ◽

Computational Intelligence ◽

Future Perspectives

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A comparative study of multiobjective computational intelligence algorithms to find the solution to the RWA problem in WDM networks

DYNA ◽

10.15446/dyna.v82n194.49663 ◽

2015 ◽

Vol 82 (194) ◽

pp. 221-229

Author(s):

Bryan Montes Castañeda ◽

Jorge Patiño Garzón ◽

Gustavo Puerto Leguizamón

Keyword(s):

Comparative Study ◽

Optical Networks ◽

Computational Intelligence ◽

Simulated Annealing Algorithm ◽

Wavelength Conversion ◽

Search Space ◽

Wavelength Assignment ◽

Wdm Networks ◽

Differential Evolutionary Algorithm ◽

The Impact

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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