Recent advances in the telecommunication industry offer great opportunities to citizens and organizations in a globally connected world, but they also arise a vast number of complex challenges that decision makers must face. Some of these challenges can be modeled as combinatorial optimization problems (COPs). Frequently, these COPs are large-size, NP-hard, and must be solved in “real time,” which makes necessary the use of metaheuristics. The first goal of this paper is to provide a review on how metaheuristics have been used so far to deal with COPs associated with telecommunication systems, detecting the main trends and challenges. Particularly, the analysis focuses on the network design, routing, and allocation problems. In addition, due to the nature of these challenges, the paper discusses how the hybridization of metaheuristics with methodologies such as simulation and machine learning can be employed to extend the capabilities of metaheuristics when solving stochastic and dynamic COPs in the telecommunication industry.

Modern telecommunication networks are comprised of a countless number of nodes exchanging data among them. In particular, multimedia traffic – composed of audio, video, and images – represents a challenging scenario requiring link optimisation techniques. The hub-and-spoke topology is frequently used to design more effective telecommunication networks. This work considers a hub-and-spoke network in which a large number of nodes are exchanging real-time multimedia data, and where the quantity of data sent from one node to another is a random variable. Given a fixed number of hubs, $p$, the goal is to select the best location for these $p$ hubs in order to minimise the total expected cost of transmission. This scenario is modelled as an uncapacitated single-allocation $p$-hub median problem under uncertainty conditions. Additionally, with the purpose of considering the effect of transmission delays on the video signal, service quality thresholds are assigned to every potential hub in the network. Since real-life networks tend to be large in size, we propose a simheuristic algorithm to cope with this stochastic and large-scale optimisation problem. A series of computational experiments illustrate these concepts and allow for testing the performance of our simheuristic approach. Finally, a statistical analysis of the obtained results is provided.

This work addresses the problem of using Unmanned Aerial Vehicles (UAV) to deploy a wireless aerial relay communications infrastructure for stations scattered on the ground. In our problem, every station in the network must be assigned to a single UAV, which is responsible for handling all data transfer on behalf of the stations that are assigned to it. Consequently, the placement of UAVs is key to achieving both network coverage and the maximization of the aggregate link capacities between UAVs and stations, and among the UAVs themselves. Because the complexity of this problem increases significantly with the number of stations to cover, for a given fixed number p of available UAVs, we model it as a single allocation p-hub median optimization problem, and we propose a hybrid metaheuristic algorithm to solve it. A series of numerical experiments illustrate the efficiency of the proposed algorithm against traditional optimization tools, which achieves high-quality results in very short time intervals, thus making it an attractive solution for real-world application scenarios.

Blind inversion of nonlinear systems is a complex task that requires some sort of prior information about the source e.g. whether it is composed of independent samples or, particularly in this work, a dependence “signature” which is assumed to be known via the autocorrentropy function. Furthermore, it involves the solution of a nonlinear, multimodal optimization problem to determine the parameters of the inverse model. Thus, we propose a blind method for Wiener systems inversion, which is composed of a correntropy-based criterion in association to the well-known CLONALG immune-inspired optimization metaheuristic. The empirical results validate the methodology for continuous and discrete signals.