Optimal Broadcasting in Metropolitan MANETs Using Multiobjective Scatter Search
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 capital 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 makespan. In this paper, we face this multiobjective problem with a state-of-the-art multiobjective scatter search algorithm called AbSS (Archive-based Scatter Search) that computes a Pareto front of solutions to empower a human designer with the ability of choosing the preferred configuration for the network. Results are compared against those obtained with the previous proposal used for solving the problem, a cellular multiobjective genetic algorithm (cMOGA). We conclude that AbSS outperforms cMOGA with respect to three different metrics.
KeywordsPareto Front Multiobjective Optimization Scatter Search High Quality Solution Nondominated Solution
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- 1.Hogie, L., Guinand, F., Bouvry, P.: A Heuristic for Efficient Broadcasting in the Metropolitan Ad Hoc Network. In: 8th Int. Conf. on Knowledge-Based Intelligent Information and Engineering Systems, pp. 727–733 (2004)Google Scholar
- 2.Alba, E., Dorronsoro, B., Luna, F., Nebro, A., Bouvry, P.: A Cellular Multi- Objective Genetic Algorithm for Optimal Broadcasting Strategy in Metropolitan MANETs. In: IPDPS-NIDISC 2005, p. 192 (2005)Google Scholar
- 3.Nebro, A.J., Luna, F., Dorronsoro, B., Alba, E., Beham, A.: AbSS: An Archivebased Scatter Search Algorithm for Multiobjective Optimization. European Journal of Operational Research (2005) (submitted)Google Scholar
- 6.Glover, F., Laguna, M., Martí, R.: Scatter Search. In: Advances in Evolutionary Computing: Theory and Applications, pp. 519–539. Springer, Heidelberg (2003)Google Scholar
- 10.Williams, B., Camp, T.: Comparison of Broadcasting Techniques for Mobile Ad Hoc Networks. In: Proc. of the ACM International Symposium on Mobile Ad Hoc Networking and Computing (MOBIHOC), pp. 194–205 (2002)Google Scholar
- 11.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)Google Scholar
- 13.Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the Strength Pareto Evolutionary Algorithm. Technical report, Swiss Federal Inst. of Technology (2001)Google Scholar
- 15.Zitzler, E.: Evolutionary Algorithms for Multiobjective Optimization: Methods and Applications. PhD thesis, Swiss Federal Institute of Technology, ETH (1999)Google Scholar
- 16.Zitzler, E., Thiele, L.: Multiobjective Optimization Using Evolutionary Algorithms – A Comparative Study. In: PPSN V, pp. 292–301 (1998)Google Scholar