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Multiobjective Evolutionary Algorithms for intradomain routing optimization

机译:域内路由优化的多目标进化算法

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Evolutionary Algorithms (EAs) have been used to develop methods for Traffic Engineering (TE) over IP-based networks in the last few years, being used to reach the best set of link weights in the configuration of intra-domain routing protocols, such as OSPF. In this work, the multiobjective nature of a class of optimization problems provided by TE with Quality of Service constraints is identified. Multiobjective EAs (MOEAs) are developed to tackle these tasks and their results are compared to previous approaches using single objective EAs. The effect of distinct genetic representations within the MOEAs is also explored. The results show that the MOEAs provide more flexible solutions for network management, but are in some cases unable to reach the level of quality obtained by single objective EAs. Furthermore, a freely available software application is described that allows the use of the mentioned optimization algorithms by network administrators, in an user-friendly way by providing adequate user interfaces for the main TE tasks.
机译:过去几年,进化算法(EA)已用于开发基于IP网络的流量工程(TE)的方法,用于在域内路由协议的配置中达到最佳的链路权重集,例如OSPF协议在这项工作中,确定了TE提供的具有服务质量约束的一类优化问题的多目标性质。开发多目标EA(MOEA)来解决这些任务,并将其结果与使用单目标EA的先前方法进行比较。还探讨了MOEA中不同遗传表示的影响。结果表明,MOEA为网络管理提供了更为灵活的解决方案,但在某些情况下无法达到单一目标EA所获得的质量水平。此外,描述了一种免费提供的软件应用程序,该应用程序允许网络管理员通过为主要TE任务提供足够的用户界面,以用户友好的方式使用上述优化算法。

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