Traffic engineering approaches are increasingly important in network management to allow an optimized configuration and resource allocation. In link-state routing, setting appropriate weights to the links is an important and challenging optimization task. Different approaches have been put forward towards this aim, including evolutionary algorithms (EAs). This work addresses the evaluation of a single and two multi-objective EAs, in two tasks related to weight setting optimization towards optimal intra-domain routing, knowing the network topology and aggregated traffic demands and seeking to minimize network congestion. In both tasks, the optimization considers scenarios where there is a dynamic alteration in the network, with (1) changes in the traffic demand matrices, and (2) link failures. The methods will simultaneously optimize for both conditions, the normal and the altered one, following a preventive TE approach. Since this leads to a bi-objective function, the use of multi-objective EAs, such as SPEA2 and NSGA-II, came naturally; those are compared to a single-objective EA previously proposed by the authors. The results show a remarkable performance and scalability of NSGA-II in the proposed tasks presenting itself as the most promising option for TE.

流量工程方法在网络管理中变得越来越重要，以允许优化的配置和资源分配。在链路状态路由中，为链路设置适当的权重是一项重要且具有挑战性的优化任务。已经针对该目标提出了不同的方法，包括进化算法（EA）。这项工作解决了单个和两个多目标EA的评估，涉及两项任务，即权重设置优化，以实现最佳域内路由，了解了网络拓扑和聚合的流量需求，并试图最大程度地减少网络拥塞。在这两个任务中，优化均考虑网络中动态变化的场景，其中（1）流量需求矩阵发生变化，（2）链路故障。遵循预防性TE方法，该方法将同时针对正常情况和变更情况这两种情况进行优化。由于这导致了双目标功能，因此自然而然地使用了SPEA2和NSGA-II等多目标EA；将这些与作者先前提出的单目标EA进行比较。结果表明，在提出的任务中，NSGA-II具有出色的性能和可扩展性，是TE的最有希望的选择。