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<record>
 <datafield tag="088" ind1="" ind2="">
  <subfield code="a">GOM08</subfield> 
  </datafield>
<datafield tag="909" ind1="C" ind2="0">
<subfield code="p">TRANSP-OR</subfield>
</datafield>
<datafield tag="980" ind1="" ind2="">
<subfield code="a">TALK</subfield>
</datafield>
 <datafield tag="700" ind1="" ind2="">
  <subfield code="a">Bierlaire, Michel</subfield> 
  </datafield>
 <datafield tag="700" ind1="" ind2="">
  <subfield code="a">Thémans, Michaël</subfield> 
  </datafield>
 <datafield tag="700" ind1="" ind2="">
  <subfield code="a">Zufferey, Nicolas</subfield> 
  </datafield>
<datafield tag="245" ind1="" ind2="">
<subfield code="a">
A heuristic for nonlinear global optimization</subfield>
</datafield>
<datafield tag="260" ind1="" ind2="">
<subfield code="c">2008</subfield>
</datafield>
<datafield tag="711" ind1="2" ind2="">
<subfield code="a">
Graph and Optimization Meeting 2008</subfield>
<subfield code="c">
Saint-Maximin La Sainte Baume, France</subfield>
<subfield code="d">August 26, 2008</subfield>
</datafield>
<datafield tag="520" ind1="" ind2="">
<subfield code="a">
We propose a new heuristic for nonlinear global optimization combining a variable neighbourhood 
search framework with a modified trust-region algorithm as local search. The proposed method presents 
the capability to prematurely interrupt the local search if the iterates are converging to a local minimum 
which has already been visited or if they are reaching an area where no significant improvement can be 
expected. The neighborhoods as well as the neighbors selection procedure are exploiting the curvature of 
the objective function. Numerical tests are performed on a set of unconstrained nonlinear problems from 
the literature. Results illustrate that the new method significantly outperforms existing heuristics from the literature in terms of success rate, CPU time, and number of function evaluations.</subfield>
</datafield>
  </record>



  </collection>
