Abstract
Due to the highly unpredictable topology of ad hoc networks, most of the existing communication protocols rely on different thresholds for adapting their behavior to the environment. Good performance is required under any circumstances. Therefore, finding the optimal configuration for those protocols and algorithms implemented in these networks is a complex task. We propose in this work to automatically fine tune the AEDB broadcasting protocol for MANETs thanks to the use of cooperative coevolutionary multi-objective evolutionary algorithms. AEDB is an advanced adaptive protocol based on the Distance Based broadcasting algorithm that acts differently according to local information to minimize the energy and network use, while maximizing the coverage of the broadcasting process. In this work, it will be fine tuned using multi-objective techniques in terms of the conflicting objectives: coverage, energy and network resources, subject to a broadcast time constraint. Because of the few parameters of AEDB, we defined new versions of the problem in which variables are discretized into bit-strings, making it more suitable for cooperative coevolutionary algorithms. Two versions of the proposed method are evaluated and compared versus the original NSGA-II, providing highly accurate tradeoff configurations in shorter execution times.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Abdou, W., Henriet, A., Bloch, C., Dhoutaut, D., Charlet, D., Spies, F.: Using an evolutionary algorithm to optimize the broadcasting methods in mobile ad hoc networks. Journal of Network and Computer Applications 34, 1794–1804 (2011)
Alba, E., Bouvry, P., Dorronsoro, B., Luna, F., Nebro, A.J.: A cellular multi-objective genetic algorithm for optimal broadcasting strategy in metropolitan MANETs. In: Nature Inspired Distributed Computing (NIDISC), p. 192b (2005)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. on Evol. Comp. 6(2), 182–197 (2002)
Dorronsoro, B., Danoy, G., Bouvry, P., Nebro, A.J.: Multi-objective Cooperative Coevolutionary Evolutionary Algorithms for Continuous and Combinatorial Optimization. In: Bouvry, P., González-Vélez, H., Kołodziej, J. (eds.) Intelligent Decision Systems in Large-Scale Distributed Environments. SCI, vol. 362, pp. 49–74. Springer, Heidelberg (2011)
Dorronsoro, B., Danoy, G., Nebro, A.J., Bouvry, P.: Achieving super-linear performance in parallel multi-objective evolutionary algorithms by means of cooperative coevolution. Computers & Operations Research 40(6), 1552–1563 (2013)
Dorronsoro, B., Ruiz, P., Danoy, G., Pigné, Y., Bouvry, P.: Evolutionary Algorithms for Mobile Ad Hoc Networks. Wiley/IEEE Computer Society (2014)
Durillo, J.J., Nebro, A.J., Luna, F., Alba, E.: Solving Three-Objective Optimization Problems Using a New Hybrid Cellular Genetic Algorithm. In: Rudolph, G., Jansen, T., Lucas, S., Poloni, C., Beume, N. (eds.) PPSN 2008. LNCS, vol. 5199, pp. 661–670. Springer, Heidelberg (2008)
Garc\’ıa-Nieto, J., Alba, E.: Automatic Parameter Tuning with Metaheuristics of the AODV Routing Protocol for Vehicular Ad-Hoc Networks. In: Di Chio, C., Brabazon, A., Di Caro, G.A., Ebner, M., Farooq, M., Fink, A., Grahl, J., Greenfield, G., Machado, P., O’Neill, M., Tarantino, E., Urquhart, N. (eds.) EvoApplications 2010, Part II. LNCS, vol. 6025, pp. 21–30. Springer, Heidelberg (2010)
Groenevelt, R., Altman, E., Nain, P.: Relaying in mobile ad hoc networks: The brownian motion mobility model. J. of Wireless Networks, 561–571 (2006)
Hsiao, P.-C., Chiang, T.-C., Fu, L.-C.: Particle swarm optimization for the minimum energy broadcast problem in wireless ad-hoc networks. In: IEEE Congress on Evolutionary Computation (CEC), pp. 1–8 (2012)
Ni, S., Tseng, Y., Chen, Y., Sheu, J.: The broadcast storm problem in a mobile ad hoc network. In: Conf. on Mobile Comp. and Networking, pp. 151–162 (1999)
Ruiz, P., Bouvry, P.: Distributed energy self-adaptation in ad hoc networks. In: Proc. of IEEE Int. Workshop on Management of Emerging Networks and Services (MENS), in Conjunction with IEEE Globecom, pp. 539–543 (2010)
Ruiz, P., Dorronsoro, B., Bouvry, P.: Finding scalable configurations for AEDB broadcasting protocol using multi-objective evolutionary algorithms. Cluster Computing 16(3), 527–544 (2013)
Toutouh, J., Nesmachnow, S., Alba, E.: Fast energy-aware OLSR routing in VANETs by means of a parallel evolutionary algorithm. Cluster Computing 16(3), 435–450 (2013)
Wolf, S., Merz, P.: Evolutionary Local Search for the Minimum Energy Broadcast Problem. In: van Hemert, J., Cotta, C. (eds.) EvoCOP 2008. LNCS, vol. 4972, pp. 61–72. Springer, Heidelberg (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Dorronsoro, B., Ruiz, P., Talbi, EG., Bouvry, P., Piyatumrong, A. (2014). Optimizing AEDB Broadcasting Protocol with Parallel Multi-objective Cooperative Coevolutionary NSGA-II. In: Esparcia-Alcázar, A., Mora, A. (eds) Applications of Evolutionary Computation. EvoApplications 2014. Lecture Notes in Computer Science(), vol 8602. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45523-4_4
Download citation
DOI: https://doi.org/10.1007/978-3-662-45523-4_4
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45522-7
Online ISBN: 978-3-662-45523-4
eBook Packages: Computer ScienceComputer Science (R0)