TY - GEN
T1 - Nature-inspired metaheuristics for optimizing information dissemination in vehicular networks
AU - Masegosa, Antonio D.
AU - Osaba, Eneko
AU - Angarita-Zapata, Juan S.
AU - Laña, Ibai
AU - Ser, Javier Del
N1 - Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019/7/13
Y1 - 2019/7/13
N2 - 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.
AB - 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.
KW - Inteligent Transportation Systems
KW - Nature-inspired Metaheuristics
KW - VANETs
KW - Vehicular Communications
KW - Vertex Covering
UR - https://www.scopus.com/pages/publications/85070597344
U2 - 10.1145/3319619.3326847
DO - 10.1145/3319619.3326847
M3 - Conference contribution
AN - SCOPUS:85070597344
T3 - GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion
SP - 1312
EP - 1320
BT - GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion
PB - Association for Computing Machinery, Inc
T2 - 2019 Genetic and Evolutionary Computation Conference, GECCO 2019
Y2 - 13 July 2019 through 17 July 2019
ER -