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General variable neighborhood search for the continuous optimization

Listed author(s):
  • Mladenovic, Nenad
  • Drazic, Milan
  • Kovacevic-Vujcic, Vera
  • Cangalovic, Mirjana
Registered author(s):

    We suggest a new heuristic for solving unconstrained continuous optimization problems. It is based on a generalized version of the variable neighborhood search metaheuristic. Different neighborhoods and distributions, induced from different metrics are ranked and used to get random points in the shaking step. We also propose VNS for solving constrained optimization problems. The constraints are handled using exterior point penalty functions within an algorithm that combines sequential and exact penalty transformations. The extensive computer analysis that includes the comparison with genetic algorithm and some other approaches on standard test functions are given. With our approach we obtain encouraging results.

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    Article provided by Elsevier in its journal European Journal of Operational Research.

    Volume (Year): 191 (2008)
    Issue (Month): 3 (December)
    Pages: 753-770

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    Handle: RePEc:eee:ejores:v:191:y:2008:i:3:p:753-770
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    1. Hansen, Pierre & Mladenovic, Nenad, 2001. "Variable neighborhood search: Principles and applications," European Journal of Operational Research, Elsevier, vol. 130(3), pages 449-467, May.
    2. Brimberg, J. & Urosevic, D. & Mladenovic, N., 2006. "Variable neighborhood search for the vertex weighted k-cardinality tree problem," European Journal of Operational Research, Elsevier, vol. 171(1), pages 74-84, May.
    3. Charles Audet & Jack Brimberg & Pierre Hansen & Sébastien Le Digabel & Nenad Mladenovi\'{c}, 2004. "Pooling Problem: Alternate Formulations and Solution Methods," Management Science, INFORMS, vol. 50(6), pages 761-776, June.
    4. Mladenovic, N. & Petrovic, J. & Kovacevic-Vujcic, V. & Cangalovic, M., 2003. "Solving spread spectrum radar polyphase code design problem by tabu search and variable neighbourhood search," European Journal of Operational Research, Elsevier, vol. 151(2), pages 389-399, December.
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