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Optimal design of ad hoc injection networks by using genetic algorithms

Published: 07 July 2007 Publication History
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  • Abstract

    This work aims at optimizing injection networks, which consist in adding a set of long-range links (called bypass links) in mobile multi-hop ad hoc networks so as to improve connectivity and overcome network partitioning. To this end, we rely on small-world network properties, that comprise a high clustering coefficient and a low characteristic path length. We investigate the use of two genetic algorithms (generational and steady-state) to optimize three instances of this topology control problem and present results that show initial evidence of their capacity to solve it.

    References

    [1]
    Zbigniew Michalewicz. Genetic Algorithms + Data Structures = Evolution Programs (2nd, extended ed.). Springer-Verlag New York, New York, NY, USA, 1994. Danoy
    [2]
    Duncan J. Watts. Small Worlds - The Dynamics of Networks between Order and Randomness. Princeton University Press, Princeton, New Jersey, USA, 1999.

    Cited By

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    • (2019)Toward real-world vehicle placement optimization in round-trip carsharingProceedings of the Genetic and Evolutionary Computation Conference10.1145/3321707.3321825(1138-1146)Online publication date: 13-Jul-2019
    • (2014)Network TopologyEvolutionary Algorithms for Mobile Ad Hoc Networks10.1002/9781118833209.ch8(173-189)Online publication date: 2-May-2014
    • (2013)An overlay approach for optimising small-world properties in VANETsProceedings of the 16th European conference on Applications of Evolutionary Computation10.1007/978-3-642-37192-9_4(32-41)Online publication date: 3-Apr-2013
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    Published In

    cover image ACM Conferences
    GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
    July 2007
    2313 pages
    ISBN:9781595936974
    DOI:10.1145/1276958

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 07 July 2007

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    Author Tags

    1. genetic algorithms
    2. optimization
    3. performance analysis
    4. telecommunications

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    Acceptance Rates

    GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;
    Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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    • (2019)Toward real-world vehicle placement optimization in round-trip carsharingProceedings of the Genetic and Evolutionary Computation Conference10.1145/3321707.3321825(1138-1146)Online publication date: 13-Jul-2019
    • (2014)Network TopologyEvolutionary Algorithms for Mobile Ad Hoc Networks10.1002/9781118833209.ch8(173-189)Online publication date: 2-May-2014
    • (2013)An overlay approach for optimising small-world properties in VANETsProceedings of the 16th European conference on Applications of Evolutionary Computation10.1007/978-3-642-37192-9_4(32-41)Online publication date: 3-Apr-2013
    • (2010)A Multi-Agent Organizational Framework for Coevolutionary OptimizationTransactions on Petri Nets and Other Models of Concurrency IV10.1007/978-3-642-18222-8_9(199-224)Online publication date: 2010
    • (2009)Towards connectivity improvement in VANETs using bypass linksProceedings of the Eleventh conference on Congress on Evolutionary Computation10.5555/1689599.1689891(2201-2208)Online publication date: 18-May-2009
    • (2009)Overcoming partitioning in large ad hoc networks using genetic algorithmsProceedings of the 11th Annual conference on Genetic and evolutionary computation10.1145/1569901.1570082(1347-1354)Online publication date: 8-Jul-2009
    • (2009)Towards connectivity improvement in VANETs using bypass links2009 IEEE Congress on Evolutionary Computation10.1109/CEC.2009.4983214(2201-2208)Online publication date: May-2009

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