In this paper, a heuristic method for the optimal placement of monitors in communication networks is proposed. In order to be able to make informed decisions, a first step towards securing a communication network is deploying an adequate sensor infrastructure. However, appropriate monitoring should take into account the priority of the communication links as well as the location of monitors. The goal is to cover the whole network with the minimum investment and impact on performance, i.e., the optimal amount and positions of monitors in the network. In order to be able to counteract dynamic changes in those networks, e.g., link failures, attacks, or entering and leaving nodes, this work focuses on swiftly obtaining results having an acceptable quality. To achieve this goal, an effective hybrid search heuristic is introduced, combining the computational efficiency of a greedy local search method with the robustness of evolution-based heuristics. It is shown that this approach works well on synthetic benchmark instances and real-world network models, having up to millions of nodes, by comparing the performance of a common evolutionary algorithm (EA) to its hybrid search counterparts. It is observed that the hybrid search heuristics produce good solutions on the instances under study in a reasonable amount of time. Regarding the fitness of the solutions found, the hybrid approach outperforms the common EA in all the experiments. Moreover, on all problem instances, the hybrid EA finds the best solutions significantly earlier in the search process, which is key when monitoring a communication infrastructure which is subject to change.

中文翻译：

---

用于通信网络中监视器放置的进化混合搜索启发式方法

本文提出了一种启发式方法，用于在通信网络中优化监视器的位置。为了能够做出明智的决定，确保通信网络安全的第一步是部署适当的传感器基础架构。但是，适当的监视应考虑到通信链路的优先级以及监视器的位置。目标是以最小的投资和对性能的影响（即，网络中监视器的最佳数量和位置）覆盖整个网络。为了能够应对那些网络中的动态变化，例如链路故障，攻击或进入和离开节点，这项工作着眼于迅速获得具有可接受质量的结果。为实现此目标，引入了有效的混合搜索启发式算法，将贪婪的局部搜索方法的计算效率与基于进化的启发式算法的鲁棒性相结合。通过比较通用进化算法（EA）与其混合搜索对应算法的性能，可以证明该方法在合成基准实例和具有多达数百万个节点的真实网络模型上效果很好。可以看出，混合搜索启发式算法可以在合理的时间内为正在研究的实例提供良好的解决方案。关于找到的解决方案的适用性，在所有实验中，混合方法均优于普通EA。此外，在所有有问题的情况下，混合EA都会在搜索过程中尽早找到最佳解决方案，这在监视可能发生变化的通信基础结构时至关重要。