Routing and wavelength assignment in all-optical networks
IEEE/ACM Transactions on Networking (TON)
A weighted coding in a genetic algorithm for the degree-constrained minimum spanning tree problem
SAC '00 Proceedings of the 2000 ACM symposium on Applied computing - Volume 1
Comparison between Genetic Algorithms and Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Analysis of blocking probability for distributed lightpath establishment in WDM optical networks
IEEE/ACM Transactions on Networking (TON)
The gregarious particle swarm optimizer (G-PSO)
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Dissipative particle swarm optimization
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Comparison among five evolutionary-based optimization algorithms
Advanced Engineering Informatics
A genetic algorithm for dynamic routing and wavelength assignment in WDM networks
ISPA'04 Proceedings of the Second international conference on Parallel and Distributed Processing and Applications
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Greater demand of bandwidth and network usage flexibility from customers along with new automated means for network resource management has led to the concept of dynamic resource provisioning in WDM optical networks where unlike the traditional static channel assignment process, network resources can be assigned dynamically. This paper examines a novel particle swarm optimization (PSO)-based scheme to solve dynamic routing and wavelength assignment (dynamic RWA) process needed to provision optical channels for wavelength continuous Wavelength Division Multiplexed (WDM) optical network without any wavelength conversion capability. The proposed PSO scheme employs a novel fitness function which is used during quantization of solutions represented by respective particles of the swarm. The proposed fitness function takes into account the normalized path length of the chosen route and the normalized number of free wavelengths available over the whole route, enabling the PSO-based scheme to be self-tuning by minimizing the need to have a dynamic algorithmic parameter `驴' needed for better performance in terms of blocking probability of the connection requests. Simulation results show better performance of the proposed PSO scheme employing novel fitness function for solving dynamic RWA problem, not only in terms of connection blocking probability but also route computation time as compared to other evolutionary schemes like genetic algorithms.