Diversity Optimization for the Detection and Concealment of Spatially Defined Communication Networks

Abstract

In recent years, computing diverse sets of high quality solutions for an optimization problem has become an important topic. The goal of computing diverse sets of high quality solutions is to provide a variety of options to decision makers, allowing them to choose the best solution for their particular problem. We consider the problem of constructing a wireless communication network for a given set of entities. Our goal is to minimize the area covered by the senders' transmissions while also avoiding adversaries that may observe the communication. We provide evolutionary diversity optimization (EDO) algorithms for this problem. We provide a formulation based on minimum spanning forests that are used as a representation and show how this formulation can be turned into a wireless communication network that avoids a given set of adversaries. We evaluate our EDO approach based on a number of benchmark instances and compare the diversity of the obtained populations in respect to the quality criterion of the given solutions as well as the chosen algorithm parameters. Our results demonstrate the effectiveness of our EDO approaches for the detection and concealment of communication networks both in terms of the quality and the diversity of the obtained solutions.