基于混合遗传算法的NGNs流量工程实现

A. Vallejo, A. Zaballos, D. Vernet, David Cutiller, J. Dalmau
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引用次数: 4

摘要

流量工程,特别是路由优化,是在下一代网络(NGN)中提供QoS时需要考虑的最重要的方面之一。传统链路状态路由协议的权值设置问题一直是一些作者研究的对象。为了解决大型网络的这一问题,人工智能启发式算法被应用到具体的遗传算法(GA)中。有些建议在所谓的混合遗传算法(HGA)或模因算法中加入局部搜索过程以优化遗传算法结果。本文对混合遗传算法(HGA)的主要方案进行了比较分析,并通过仿真的方法将其与其他算法进行了比较。从结果分析中选择了一种HGA算法,并在商用路由器的实际测试平台上实现了成功的OSPFv3路由优化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Implementation of Traffic Engineering in NGNs Using Hybrid Genetic Algorithms
Traffic engineering, particularly routing optimization, is one of the most important aspects to take into account when providing QoS in next generation networks (NGN). The problem of weight setting with conventional link state routing protocols for routing optimization has been object of study by a few authors. To solve this problem for big networks artificial intelligence heuristics have been used, in concrete genetic algorithms (GA). Some of the proposals incorporate local search procedures in order to optimize the GA results, in the so-called hybrid genetic algorithm (HGA) or memetic algorithm. This paper presents an inedited comparative analysis of the main hybrid genetic algorithms (HGA) proposals, as well as comparing them with other algorithms for the same problem by means of simulations. One of the HGA algorithms was chosen from the results analysis and was implemented over a real testbed with commercial routers with successful OSPFv3 routing optimization.
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