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General variable neighborhood search for the continuous optimization

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  • Mladenovic, Nenad
  • Drazic, Milan
  • Kovacevic-Vujcic, Vera
  • Cangalovic, Mirjana
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    Abstract

    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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    Bibliographic Info

    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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    Web page: http://www.elsevier.com/locate/eor

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    References

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    1. 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.
    2. Hansen, Pierre & Mladenovic, Nenad, 2001. "Variable neighborhood search: Principles and applications," European Journal of Operational Research, Elsevier, vol. 130(3), pages 449-467, May.
    3. 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.
    4. 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.
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    Cited by:
    1. Massimo Spadoni & Luciano Stefanini, 2012. "A Differential Evolution algorithm to deal with box, linear and quadratic-convex constraints for boundary optimization," Journal of Global Optimization, Springer, vol. 52(1), pages 171-192, January.
    2. Carrizosa, Emilio & Olivares-Nadal, Alba V. & Ramírez-Cobo, Pepa, 2013. "Time series interpolation via global optimization of moments fitting," European Journal of Operational Research, Elsevier, vol. 230(1), pages 97-112.

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