%0 Generic
%A Masegosa, Antonio D.
%A Osaba, Eneko
%A Angarita-Zapata, Juan S.
%A Laña, Ibai
%A Ser, Javier Del
%T Nature-inspired metaheuristics for optimizing information dissemination in vehicular networks
%J GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion
%D 2019
%U https://hdl.handle.net/11556/2766
%X Connected vehicles are revolutionizing the way in which transport and mobility are conceived. The main technology behind is the so-called Vehicular Ad-Hoc Networks (VANETs), which are communication networks that connect vehicles and facilitate various services. Usually, these services require a centralized architecture where the main server collects and disseminates information from/to vehicles. In this paper, we focus on improving the downlink information dissemination in VANETs with this centralized architecture. With this aim, we model the problem as a Vertex Covering optimization problem and we propose four new nature-inspired methods to solve it: Bat Algorithm (BA), Firefly Algorithm (FA), Particle Swarm Optimization (PSO), and Cuckoo Search (CS). The new methods are tested over data from a real scenario. Results show that these metaheuristics, especially BA, FA and PSO, can be considered as powerful solvers for this optimization problem.
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