Nature-inspired metaheuristics for optimizing information dissemination in vehicular networks
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2019-07-13
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Association for Computing Machinery, Inc
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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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Publisher Copyright: © 2019 Association for Computing Machinery.
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Masegosa , A D , Osaba , E , Angarita-Zapata , J S , Laña , I & Ser , J D 2019 , Nature-inspired metaheuristics for optimizing information dissemination in vehicular networks . in GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion . GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion , Association for Computing Machinery, Inc , pp. 1312-1320 , 2019 Genetic and Evolutionary Computation Conference, GECCO 2019 , Prague , Czech Republic , 13/07/19 . https://doi.org/10.1145/3319619.3326847
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