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  <front>

    <journal-meta>

      <journal-title>Information Technology Journal</journal-title>

      <issn pub-type="ppub">1812-5638</issn>

      <issn pub-type="epub">1812-5646</issn>

      <publisher>

        <publisher-name>Asian Network for Scientific Information</publisher-name>

      </publisher>

    </journal-meta>


    <article-meta>

      <article-id pub-id-type="doi">10.3923/itj.2013.2374.2381</article-id>


      <title-group>

        <article-title><![CDATA[Topology Control Research of Monitor Network Based on PID of Self-adaptive Hierarchical Genetic Algorithm]]></article-title>

      </title-group>


      <contrib-group>

        <contrib contrib-type="author" xlink:type="simple">


          <name name-style="western">

            <surname>Ruan</surname>

            <given-names>Dian-Xu</given-names>

          </name>


          <name name-style="western">

            <surname>Zhang</surname>

            <given-names>Xiao-Guang</given-names>

          </name>


        </contrib>

      </contrib-group>


      <pub-date pub-type="collection">




        <month>12</month>


        <year>2013</year>

      </pub-date>


      <volume>12</volume>

      <issue>12</issue>


      <abstract><![CDATA[<p>Due to the environment of coal mine underground is poor, need to establish a stable and reliable network topology quickly during building the monitoring network and the topology must has reconfigurability, connectivity and reliability. For the above request, the topology control algorithm for wireless sensor network based on the PID of self-adapt hierarchical genetic (SAHGA-PID) in the coal mine underground was proposed. Based on the adaptive genetic algorithm, introduced hierarchical search strategy, divided the solution space into two-layers and improved the optimization efficiency. This algorithm combined biological intelligence algorithm to closed-loop control theory, overcame the shortcoming of slow convergence and instability of the existing topology control algorithm and improved the energy efficiency and convergence rate. The simulations and results show that, this algorithm can achieve the goal of optimizing the topology control quickly and effectively.</p>]]></abstract>


    </article-meta>

  </front>


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