Splitting Method for Spatio-temporal Sensors Deployment in Underwater Systems View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2012

AUTHORS

Mathieu Chouchane , Sébastien Paris , François Le Gland , Mustapha Ouladsine

ABSTRACT

In this paper, we present a novel stochastic optimization algorithm based on the rare events simulation framework for sensors deployment in underwater systems. More precisely, we focus on finding the best spatio-temporal deployment of a set of sensors in order to maximize the detection probability of an intelligent and randomly moving target in an area under surveillance. Based on generalized splitting technique with a dedicated Gibbs sampler, our approach does not require any state-space discretization and rely on the evolutionary framework. More... »

PAGES

243-254

References to SciGraph publications

  • 2009-12. The Gibbs Cloner for Combinatorial Optimization, Counting and Sampling in METHODOLOGY AND COMPUTING IN APPLIED PROBABILITY
  • 2010. Investigating the Local-Meta-Model CMA-ES for Large Population Sizes in APPLICATIONS OF EVOLUTIONARY COMPUTATION
  • 2008-12. An Efficient Algorithm for Rare-event Probability Estimation, Combinatorial Optimization, and Counting in METHODOLOGY AND COMPUTING IN APPLIED PROBABILITY
  • 2005-02. A Tutorial on the Cross-Entropy Method in ANNALS OF OPERATIONS RESEARCH
  • Book

    TITLE

    Evolutionary Computation in Combinatorial Optimization

    ISBN

    978-3-642-29123-4
    978-3-642-29124-1

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-642-29124-1_21

    DOI

    http://dx.doi.org/10.1007/978-3-642-29124-1_21

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1008145367


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