Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Computer
Sonet and T1: architectures for digital transport networks
Sonet and T1: architectures for digital transport networks
Optical networks: a practical perspective
Optical networks: a practical perspective
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Understanding SONET/SDH and ATM: Communications Networks for the Next Millennium
Understanding SONET/SDH and ATM: Communications Networks for the Next Millennium
Fundamentals of Computer Alori
Fundamentals of Computer Alori
Journal of Global Optimization
Computer Networks: The International Journal of Computer and Telecommunications Networking
Nonparametric genetic clustering: comparison of validity indices
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
An evolutionary autonomous agents approach to image featureextraction
IEEE Transactions on Evolutionary Computation
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
Traffic grooming in WDM networks
IEEE Communications Magazine
Networks Consolidation through Soft Computing
ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems
Optical Switching and Networking
| Hi-index | 0.25 |
In recent years, minimization of SONET-ADMs (Synchronous Optical NETwork-Add-Drop Multiplexers) in WDM (Wavelength Division Multiplexing) optical networks has gained a lot of attention in both the research and commercial arenas. This motivates the research presented in this article. The enhanced searching capability of genetic evolutionary algorithm has been exploited for this purpose. The individuals (chromosomes) have been represented by different sequence of the calls in the traffic matrix. A simple algorithm that minimizes the number of required ADMs based on the shortest path and a possible alternate shortest path has been applied. Some good chromosomes based on some intuitive reasoning have been introduced in the initial population to enhance the convergence of the proposed genetic evolutionary algorithm. The distinguished feature of the proposed algorithm is in introducing the catalyst to direct the convergence of genetic evolutionary algorithm towards its solution. However, the catalyst has been kept small enough to be able to bias the solution. To establish the effectiveness of the proposed algorithm, the simulation results are compared with that of presented in literature with same network configuration and traffic matrix.