<xml>
  <records>
    <record>
       <contributors>
          <authors>
             <author>Liu, W.L.</author>
             <author>Cong, P.T.</author>
             <author>Jiang, H.</author>
             <author>Wang, Z.M.</author>
             <author>Zheng, S.X.</author>
          </authors>
       </contributors>
       <titles>
          <title>
             An Optimization Method of the Nose-cone Buncher Cavity
          </title>
       </titles>
		 <publisher>JACoW Publishing</publisher>
       <pub-location>Geneva, Switzerland</pub-location>
		 <isbn>2226-0366</isbn>
		 <isbn>978-3-95450-194-6</isbn>
		 <electronic-resource-num>10.18429/JACoW-LINAC2018-THPO042</electronic-resource-num>
		 <language>English</language>
		 <pages>778-780</pages>
       <pages>THPO042</pages>
       <keywords>
          <keyword>cavity</keyword>
          <keyword>simulation</keyword>
          <keyword>proton</keyword>
          <keyword>radiation</keyword>
          <keyword>bunching</keyword>
       </keywords>
       <work-type>Contribution to a conference proceedings</work-type>
       <dates>
          <year>2019</year>
          <pub-dates>
             <date>2019-01</date>
          </pub-dates>
       </dates>
       <urls>
          <related-urls>
              <url>https://doi.org/10.18429/JACoW-LINAC2018-THPO042</url>
              <url>http://jacow.org/linac2018/papers/thpo042.pdf</url>
          </related-urls>
       </urls>
       <abstract>
          The nose-cone buncher cavity is widely used on proton accelerators. It’s important to properly optimize the cavity geometry for fine RF performance. Howev-er, currently the optimization is usually carried out manually and the criteria are not objective enough. In this paper, an optimization method using the multi-objective, multi-variable optimization approach is presented. The geometry and RF parameters are con-sidered as the variables and objectives respectively. The goal function is defined as the weighted sum of multiple RF parameters. The multi-variable functions are approximately derived from the single-variable functions based on electromagnetic simulation. And an optimization code is developed accordingly which has been applied to the XiPAF debuncher optimization.
       </abstract>
    </record>
  </records>
</xml>
