A Genetic Algorithm for Dynamic Routing and Wavelength Assignment in WDM Networks

Le, Vinh Trong; Ngo, Son Hong; Jiang, Xiaohong; Horiguchi, Susumu; Guo, Minyi

doi:10.1007/978-3-540-30566-8_103

Cited by 15 publications

(11 citation statements)

References 4 publications

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“…Figure 2 shows the performance of the multiobjective algorithms (dotted lines) against the blocking probability as a function of the normalized load for four mono-objective algorithms (solid lines) found in the mentioned scientific literature. The mono-objective algorithms were based on Genetic Algorithms (GA1) (Bisbal et al, 2004) and (GA2) (Vinh, 2004), a traditional particle swarm optimization algorithm (PSOT) (Hassan et al, 2009), (Hassan et al, 2009a) and an algorithm based on a graph theory model (Y. Zhou et al, 2007). In this comparative framework the network topology managed 8 wavelengths and no wavelength conversion is implemented in the nodes.…”

Section: Resultsmentioning

confidence: 99%

Performance of multiobjective computational intelligence algorithms for the routing and wavelength assignment problem

2016

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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 number of 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.

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Section: Resultsmentioning

confidence: 99%

Performance of multiobjective computational intelligence algorithms for the routing and wavelength assignment problem

2016

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“…By keeping a suitable number of ants in a network to cooperatively explore the network states and continuously update the pheromone tables, this algorithm enables the route for a connection request to be determined promptly by the current states of routing tables, with a small setup delay. Note, however, that the algorithm proposed in [20] usually results in a higher blocking probability than that of the GA-based algorithm for dynamic RWA in WDM networks without wavelength conversion [14], [15]. Since the main idea of the algorithm in [20] will be adopted in our hybrid algorithm for generating the first population of routes, we describe this algorithm here in more detail.…”

Section: Mobile Agents Technique For Routing In Wdm Networkmentioning

confidence: 99%

“…Note that the blocking probability of GA-based algorithms for dynamic RWA is usually significantly lower than that of mobile agents-based algorithms [14], [15], [20]. However, they may incur a very large setup delay because they require a very time consuming random searching process to generate the first population of routes after the arrival of a new request.…”

Section: Domentioning

confidence: 99%

“…However, conventional dynamic routing algorithms may not work well in an environment with sparse or/ and full wavelength conversion [10]. GAs represent a promising approach to the RWA problem in WDM networks, and several GA-based RWA algorithms have been proposed [12], [13], [14], [15]. In [12], [13], N. Banerjee and S. Pandey et al formulated both the static RWA and dynamic RWA problems as a multi-objective optimization problem and solved it using genetic algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…Also, these authors did not consider wavelength conversion in their GA-based RWA algorithms. D.Bisbal et al [14] proposed a novel GA-based algorithm for dynamic RWA problem in WDM networks with wavelength continuity constraint, and V.T.Le et al [15] improved Bisbal's algorithm by using a more general cost function to achieve a better load balance. Bisbal's algorithm does not require the re-routing of available connections and it can achieve a significantly low blocking probability.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic RWA Based on the Combination of Mobile Agents Technique and Genetic Algorithms in WDM Networks with Sparse Wavelength Conversion

Jiang

Ngo

et al. 2005

IEICE Transactions on Information and Systems

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SummaryGenetic Algorithms (GA) provide an attractive approach to solving the challenging problem of dynamic routing and wavelength assignment (RWA) in optical WDM networks, because they usually achieve a significantly low blocking probability. Available GA-based dynamic RWA algorithms were designed mainly for WDM networks with a wavelength continuity constraint, and they can not be applied directly to WDM networks with wavelength conversion capability. Furthermore, the available GA-based dynamic RWA algorithms suffer from the problem of requiring a very time consuming process to generate the first population of routes for a request, which may results in a significantly large delay in path setup. In this paper, we study the dynamic RWA problem in WDM networks with sparse wavelength conversion and propose a novel hybrid algorithm for it based on the combination of mobile agents technique and GA. By keeping a suitable number of mobile agents in the network to cooperatively explore the network states and continuously update the routing tables, the new hybrid algorithm can promptly determine the first population of routes for a new request based on the routing table of its source node, without requiring the time consuming process associated with current GA-based dynamic RWA algorithms. To achieve a good load balance in WDM networks with sparse wavelength conversion, we adopt in our hybrid algorithm a new reproduction scheme and a new fitness function that simultaneously takes into account the path length, number of free wavelengths, and wavelength conversion capability in route selection. Our new hybrid algorithm achieves a better load balance and results in a significantly lower blocking probability than does the Fixed-Alternate routing algorithm, both for optical networks with sparse and full-range wavelength converters and for optical networks with sparse and limited-range wavelength converters. This was verified by an extensive simulation study on the ns-2 network simulator and two typical network topologies. The ability to guarantee both a low blocking probability and a small setup delay makes the new hybrid dynamic RWA algorithm very attractive for current optical circuit switching networks and also for the next generation optical burst switching networks.

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An Effective Evolutionary Computational Approach for Routing Optimization in Networks with Shared Risk Link Groups

Luo

2012

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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A Genetic Algorithm for Dynamic Routing and Wavelength Assignment in WDM Networks

Cited by 15 publications

References 4 publications

Performance of multiobjective computational intelligence algorithms for the routing and wavelength assignment problem

Performance of multiobjective computational intelligence algorithms for the routing and wavelength assignment problem

Dynamic RWA Based on the Combination of Mobile Agents Technique and Genetic Algorithms in WDM Networks with Sparse Wavelength Conversion

An Effective Evolutionary Computational Approach for Routing Optimization in Networks with Shared Risk Link Groups

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