Abstract
Mobile Ad-hoc Networks (MANETs) are composed of a set of communicating devices which are able to spontaneously interconnect without any pre-existing infrastructure. In such scenario, broadcasting becomes an operation of tremendous importance for the own existence and operation of the network. Optimizing a broadcasting strategy in MANETs is a multiobjective problem accounting for three goals: reaching as many stations as possible, minimizing the network utilization, and reducing the duration of the operation itself. This research, which has been developed within the OPLINK project (http://oplink.lcc.uma.es), faces a wide study about this problem in metropolitan MANETs with up to seven different advanced multiobjective metaheuristics. They all compute Pareto fronts of solutions which empower a human designer with the ability of choosing the preferred configuration for the network. The quality of these fronts is evaluated by using the hypervolume metric. The obtained results show that the SPEA2 algorithm is the most accurate metaheuristic for solving the broadcasting problem.
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References
Conti, M., Giordano, S., Maselli, G., Turi, G.: MobileMAN: Mobile Metropolitan Ad Hoc Networks. In: Proc. of the Eight Int. IFIP-TC6 Conf, pp. 194–205 (2003)
Hogie, L., Guinand, F., Bouvry, P.: A Heuristic for Efficient Broadcasting in the Metropolitan Ad Hoc Network. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds.) KES 2004. LNCS (LNAI), vol. 3213, pp. 727–733. Springer, Heidelberg (2004)
Glover, F.W., Kochenberger, G.A.: Handbook of Metaheuristics. Kluwer, Dordrecht (2003)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Trans. on Evolutionary Computation 6(2), 182–197 (2002)
Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the Strength Pareto Evolutionary Algorithm. Technical report, Swiss Federal Inst. of Technology (2001)
Coello, C.A., Lechuga, M.S.: MOPSO: A proposal for multiple objective particle swarm optimization. In: CEC 2002, pp. 1051–1056 (2002)
Nebro, A.J., Luna, F., Dorronsoro, B., Alba, E., Beham, A.: AbYSS: Adapting Scatter Search to Multiobjective Optimization. Technical Report ITI-2006-2, Dpto. Lenguajes y Ciencias de la Computación (2006)
Alba, E., Dorronsoro, B., Luna, F., Nebro, A., Bouvry, P.: A Cellular Multi-Objective Genetic Algorithm for Optimal Broadcasting Strategy in Metropolitan MANETs. Computer Communications 30(4), 685–697 (2007)
Bäck, T., Rüdolph, G., Schwefel, H.: A survey of evolution strategies. In: 4th Int. Conf. on Genetic Algorithms, pp. 2–9 (1991)
Storn, R., Price, K.: Differential evolution – a simple efficient adaptive scheme for global optimization over continuous spaces. Technical Report 95-012, Int. Compt. Sci. Inst., Berkeley, CA (1995)
Hogie, L., Guinand, F., Bouvry, P.: The Madhoc Metropolitan Adhoc Network Simulator. Université du Luxembourg and Université du Havre, France (2005), Available at http://www-lih.univ-lehavre.fr/~hogie/madhoc/
Knowles, J., Corne, D.: The Pareto Archived Evolution Strategy: A New Baseline Algorithm for Multiobjective Optimization. In: Proceedings of the 1999 Congress on Evolutionary Computation, CEC, pp. 9–105 (1999)
Luna, F., Nebro, A., Dorronsoro, B., Luna, F., Alba, E., Bouvry, P., Hogie, L.: Optimal Broadcasting in Metropolitan MANETs Using Multiobjective Scatter Search. In: Rothlauf, F., Branke, J., Cagnoni, S., Costa, E., Cotta, C., Drechsler, R., Lutton, E., Machado, P., Moore, J.H., Romero, J., Smith, G.D., Squillero, G., Takagi, H. (eds.) EvoWorkshops 2006. LNCS, vol. 3907, pp. 255–266. Springer, Heidelberg (2006)
Glover, F., Laguna, M., Martí, R.: Scatter Search. In: Advances in Evolutionary Computing: Theory and Applications, pp. 519–539. Springer, New York (2003)
Alba, E., Tomassini, M.: Parallelism and evolutionary algorithms. IEEE Trans. on Evolutionary Computation 6(5), 443–462 (2002)
Zitzler, E., Thiele, L.: Multiobjective Optimization Using Evolutionary Algorithms – A Comparative Study. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) Parallel Problem Solving from Nature - PPSN V. LNCS, vol. 1498, pp. 292–301. Springer, Heidelberg (1998)
Kukkonen, S., Deb, K.: A fast and effective method for pruning of non-dominated solutions in many-objective problems. In: Runarsson, T.P., Beyer, H.-G., Burke, E., Merelo-Guervós, J.J., Whitley, L.D., Yao, X. (eds.) Parallel Problem Solving from Nature - PPSN IX. LNCS, vol. 4193, pp. 553–562. Springer, Heidelberg (2006)
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Alba, E. et al. (2007). Metaheuristic Approaches for Optimal Broadcasting Design in Metropolitan MANETs. In: Moreno Díaz, R., Pichler, F., Quesada Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2007. EUROCAST 2007. Lecture Notes in Computer Science, vol 4739. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75867-9_95
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DOI: https://doi.org/10.1007/978-3-540-75867-9_95
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