Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Routing and wavelength assignment with power considerations in optical networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
IEEE/ACM Transactions on Networking (TON)
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
Genetic Algorithms
Genetic Local Search Algorithms for the Travelling Salesman Problem
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Traffic grooming in unidirectional WDM ring networks using genetic algorithms
Computer Communications
An approach to wide area WDM optical network design using genetic algorithm
Computer Communications
Converter placement in all-optical networks using genetic algorithms
Computer Communications
Grooming of arbitrary traffic in SONET/WDM BLSRs
IEEE Journal on Selected Areas in Communications
On optimal traffic grooming in WDM rings
IEEE Journal on Selected Areas in Communications
A new approach to improving the grooming performance with dynamic traffic in SONET rings
Computer Networks: The International Journal of Computer and Telecommunications Networking
Information Sciences: an International Journal
Genetic evolutionary algorithm for static traffic grooming to SONET over WDM optical networks
Computer Communications
Reconfigurable grooming of dynamic traffic in SONET/WDM ring networks
Journal of High Speed Networks
Grooming of Dynamic Traffic in WDM Tree Networks Using Genetic Algorithms
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
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We propose a genetic algorithm (GA) to solve the strictly nonblocking grooming problems with dynamic traffic in unidirectional synchronous optical network/wavelength division multiplexing rings with arbitrary asymmetric traffic patterns. We give out the theoretical lower and upper bounds on the numbers of both add/drop multiplexers (ADMs) and wavelengths required and define the blocking properties in grooming dynamic traffic in detail. In applying GAs to this problem, we propose a first fit approach incorporated with a greedy local improvement algorithm to decode the chromosome. Four different local improvement scenarios are designed and compared in this paper. We also introduce the concepts of Link Load Imparity, Weighted Traffic Imparity and Average Link Load of each traffic pattern and use the latter two to estimate the first parameter before grooming. Comparison between the genetic and the random search results is also made. Computer simulation results show that it is important to combine the GA with an appropriate local improvement scenario in order to achieve near optimal results and to reduce the running time and that the algorithm proposed in this paper is able to achieve good results in reducing the numbers of ADMs and wavelengths.