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2019•Eleventh International Conference on Information Optics and Photonics (CIOP 2019)Requires access

A dynamic RWA algorithm based on genetic algorithm in SDON

Zhe Yang

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Abstract

Genetic algorithm (GA) is a commonly used algorithm in optical network routing and wavelength assignment (RWA). In order to adapt to the development trend of next-generation software-defined optical network (SDON) with higher speed, wideband, long distance and large capacity, this paper proposes a dynamic, improved GA-based RWA algorithm which reduces the complexity of the algorithm and improves the performance of the algorithm. The simulation results show that the algorithm can effectively reduce the blocking rate and improve resources utilization rate in SDON compared with the classical algorithm and GA.

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What this paper is about

Genetic algorithm (GA) is a commonly used algorithm in optical network routing and wavelength assignment (RWA). In order to adapt to the development trend of next-generation software-defined optical network (SDON) with higher speed, wideband, long distance and large capacity, this paper proposes a dynamic, improved GA-based RWA algorithm which reduces the complexity of the algorithm and improves the performance of the algorithm. The simulation results show that the algorithm can effectively reduce the blocking rate and improve resources utilization rate in SDON compared with the classical algorithm and GA.

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Available abstract

Genetic algorithm (GA) is a commonly used algorithm in optical network routing and wavelength assignment (RWA). In order to adapt to the development trend of next-generation software-defined optical network (SDON) with higher speed, wideband, long distance and large capacity, this paper proposes a dynamic, improved GA-based RWA algorithm which reduces the complexity of the algorithm and improves the performance of the algorithm. The simulation results show that the algorithm can effectively reduce the blocking rate and improve resources utilization rate in SDON compared with the classical algorithm and GA.

Key concepts: Computer science, Population-based incremental learning, Genetic algorithm, Algorithm, Routing and wavelength assignment, Algorithm design, Blocking (statistics), Routing algorithm

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