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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.2014.2560.2566</article-id>


      <title-group>

        <article-title><![CDATA[An Improved Particle Swarm Optimization]]></article-title>

      </title-group>


      <contrib-group>

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


          <name name-style="western">

            <surname>Zeng</surname>

            <given-names>Wenjuan</given-names>

          </name>


          <name name-style="western">

            <surname>Gao</surname>

            <given-names>Haibo</given-names>

          </name>


          <name name-style="western">

            <surname>Jing</surname>

            <given-names>Wang</given-names>

          </name>


        </contrib>

      </contrib-group>


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




        <month>12</month>


        <year>2014</year>

      </pub-date>


      <volume>13</volume>

      <issue>16</issue>


      <abstract><![CDATA[<p>The basic and improved algorithms of PSO are focused on how 
  to search effectively the optimalsolution in the solution space by using one 
  of the particle swarm. However, the particles are always chasing the global 
  optimal point and such points are currently found on their way of search, rapidly 
  leading their speed down to zero and hence being restrained in the local minimum. 
  Consequently, there are the convergence or early maturity of particles. The 
  improved PSO is based on the enlightenment of Back-Propagation (BP) neural network 
  while the improvement is similar to the smooth weight through low-pass filter. 
  The test of classical functions show that the PSO provides a promotion in the 
  convergence precision and make better a certain extent in the calculation velocity.</p>]]></abstract>


    </article-meta>

  </front>


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