An approach to wide area WDM optical network design using genetic algorithm

Saha, Debashis; Purkayastha, Madhumita; Mukherjee, Amitava

doi:10.1016/s0140-3664(98)00238-2

Cited by 36 publications

(18 citation statements)

References 21 publications

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“…The following performance evaluation is based on NSFNET network topology [24]. Since the optimization objective of the proposed algorithm is to minimize the tree cost while the user QoS satisfaction degree is high, there is a tradeoff between the tree cost and delay.…”

Section: Performance Evaluationmentioning

confidence: 99%

A multipopulation parallel genetic simulated annealing-based QoS routing and wavelength assignment integration algorithm for multicast in optical networks

Cheng

Wang

Yang

et al. 2009

Applied Soft Computing

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In this paper, we propose an integrated Quality of Service (QoS) routing algorithm for optical networks. Given a QoS multicast request and the delay interval specified by users, the proposed algorithm can find a flexible-QoS-based cost suboptimal routing tree. The algorithm first constructs the multicast tree based on the multipopulation-parallel-genetic-simulated-annealing algorithm, and then assigns wavelengths to the tree based on the wavelength graph. In the algorithm, routing and wavelength assignment are integrated into a single process. For routing, the objective is to find a cost suboptimal multicast tree. For wavelength assignment, the objective is to minimize the delay of the multicast tree, which is achieved by minimizing the number of wavelength conversion. Thus both the cost of multicast tree and the user QoS satisfaction degree can approach the optimal. Our algorithm also considers load balance. Simulation results show that the proposed algorithm is feasible and effective. We also discuss the practical realization mechanisms of the algorithm.

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Section: Performance Evaluationmentioning

confidence: 99%

A multipopulation parallel genetic simulated annealing-based QoS routing and wavelength assignment integration algorithm for multicast in optical networks

Cheng

Wang

Yang

et al. 2009

Applied Soft Computing

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“…Genetic algorithm has been widely used in various optimization tasks, including numerical optimization, combinatorial optimization problems such as knapsack problem and airline crew scheduling problem [16][17][18]. It has also been used in a number of communication network design problems such as multihop lightwave network topology design [19] and wide area wavelength division multiplexing optical design [20]. In this paper, genetic algorithm is used for seismic reliability optimization of lifeline systems.…”

Section: Genetic Algorithm For Seismic Reliability Optimization Of LImentioning

confidence: 99%

Genetic algorithm for seismic topology optimization of lifeline network systems

Liu

Bao

2008

Earthq Engng Struct Dyn

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SUMMARYLifeline systems, such as water distribution and gas supply networks, usually cover large areas. For these systems, seismic design is always a difficult problem because of the complexity of large-scale networks. In this paper, a topology optimization technology for lifeline networks is established. Firstly, in order to speed up the convergence of optimization process, an element investment importance analysis is carried out to evaluate the importance of components to the lifeline network. Then a topology optimization model is established. The aim of the model is to find the least-cost network topology while the seismic reliability between the sources and each terminal satisfies prescribed reliability constraints. For this optimization problem, a genetic algorithm, which takes network topologies as the individuals of its population, is used to search for the optimal solutions by suitable operators, including selection, crossover and mutation operators. The capacity of the proposed algorithm is illustrated by its applications to a simple example network consisting of 10 nodes and an actual network with 391 nodes located in a large city of China.

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“…The individuals of the genetic algorithm's population were graph topologies. Saha et al in [27] used a genetic algorithm to generate and map an optimal virtual topology onto a wavelength-routed all-optical physical network. Basically the work was an extension to [1] where the authors used genetic algorithm in place of simulated annealing to achieve better throughput and less delay.…”

Section: Use Of Evolutionary Algorithm In Optical Networkmentioning

confidence: 99%

“…The approach generates an initial population based on the kshortest paths for a given source-destination pair. This approach is different from the initial encoding in Saha et al [27], where an initial virtual topology is generated using Prufer sequence technique.…”

Section: The Evolutionary Algorithmmentioning

confidence: 99%

Multihop Virtual Topology Design in WDM Optical Networks for Self-Similar Traffic

et al. 2005

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Abstract. In this paper, we consider the problem of designing virtual topologies for multihop optical WDM networks when the traffic is self-similar in nature. Studies over the last few years suggest that the network traffic is bursty and can be much better modeled using self similar process instead of Poisson process. We examine buffer sizes of a network and observe that, even with reasonably low buffer overflow probability, the maximum buffer size requirement for self-similar traffic can be very large. Therefore, a self-similar traffic model has an impact on the queuing delay which is usually much higher than that obtained with the Poisson model. We investigate the problem of constructing the virtual topology with these two types of traffic and solve it with two algorithmic approaches: Greedy (Heuristic) algorithm and Evolutionary algorithm. While the greedy algorithm performs a leastcost search on the total delay along paths for routing traffic in a multihop fashion, the evolutionary algorithm uses genetic methods to optimize the average delay in a network. We analyze and compare our proposed algorithms with an existing algorithm via different performance parameters. Interestingly, with both the proposed algorithms the difference in the queuing delays, caused by self-similar and Poisson traffic, results in different multihop virtual topologies.

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An approach to wide area WDM optical network design using genetic algorithm

Cited by 36 publications

References 21 publications

A multipopulation parallel genetic simulated annealing-based QoS routing and wavelength assignment integration algorithm for multicast in optical networks

A multipopulation parallel genetic simulated annealing-based QoS routing and wavelength assignment integration algorithm for multicast in optical networks

Genetic algorithm for seismic topology optimization of lifeline network systems

Multihop Virtual Topology Design in WDM Optical Networks for Self-Similar Traffic

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