Optimization of monitoring in dynamic communication networks using a hybrid evolutionary algorithm.

In this paper, we propose a hybrid evolutionary algorithm (EA) for the optimization of efficient monitoring in dynamic communication networks. The first step towards improving communication infrastructures is gathering information about the current situation. One part of collecting this information is to implement an adequate monitoring in the network, i.e., the optimal positions and amount of monitoring devices, in order to analyze communication flows. Solving the general monitor selection problem using evolutionary computation has already been done in the past. Our approach focuses on the efficient optimization of monitors having a dynamic search landscape, i.e., having recurring substantial changes of the underlying network model in order to simulate bulks of entering or leaving nodes and edges. Here, we compare the steady optimization versions of a common genetic algorithm (GA), the proposed hybrid EA, and a local search based EA, in conjunction with a total restart version of the hybrid EA. Empirical results are obtained using multiple well-known real-world problem instances. We show that we can achieve reliably fast high quality results using the proposed hybrid EA.