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首页> 外文会议>Optical Transmission, Switching, and Subsystems IV pt.1 >An Optimal Policy Based on the Genetic Algorithm for the Dynamic Threshold of the Optical Network
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An Optimal Policy Based on the Genetic Algorithm for the Dynamic Threshold of the Optical Network

机译：基于遗传算法的光网络动态门限优化策略

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摘要

The complete partitioning policy (CP) for the wavelength resource in optical networks is now widely focused on. The dynamic threshold is one of the ways to make CP policy more efficient. Furthermore, an optimized threshold will be better for reducing the blocking probability and improving the utilization of the wavelength resource. Hence, the genetic algorithm is selected as the optimal policy on virtue of its excellent global search performance for getting optimized value of the dynamic threshold. Moreover, a maximal threshold as the high limit for the dynamic threshold is needed to be decided for making wavelengths shared between different wavelength classes, because the class with higher priority can share its wavelengths with the lower one after its own call setups are satisfied. Therefore, a neural network predictor that can predict the number of the next call setup is designed on the basis of the genetic algorithm to solve this problem. The values of the dynamic threshold and the maximal threshold are calculated, and the simulation results show that they take good effect in reducing the blocking probability and improving the utilization of the wavelength resource.
机译：现在，光网络中用于波长资源的完整分区策略（CP）受到了广泛关注。动态阈值是提高CP策略效率的方法之一。此外，优化的阈值将更好地减小阻塞概率并提高波长资源的利用率。因此，遗传算法由于其出色的全局搜索性能而获得了动态阈值的最优值，因此被选择为最优策略。此外，需要确定最大阈值作为动态阈值的上限，以使波长在不同波长类别之间共享，因为在满足自己的呼叫设置后，具有较高优先级的类别可以与较低波长类别共享其波长。因此，在遗传算法的基础上，设计了一种可以预测下一次呼叫建立次数的神经网络预测器，以解决该问题。计算了动态阈值和最大阈值，仿真结果表明它们在降低阻塞概率和提高波长资源利用率方面具有良好的效果。

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