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General variable neighborhood search for the continuous optimization

Author

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

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.

Suggested Citation

  • Mladenovic, Nenad & Drazic, Milan & Kovacevic-Vujcic, Vera & Cangalovic, Mirjana, 2008. "General variable neighborhood search for the continuous optimization," European Journal of Operational Research, Elsevier, vol. 191(3), pages 753-770, December.
    • Handle: RePEc:eee:ejores:v:191:y:2008:i:3:p:753-770
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
    4. Hansen, Pierre & Mladenovic, Nenad, 2001. "Variable neighborhood search: Principles and applications," European Journal of Operational Research, Elsevier, vol. 130(3), pages 449-467, May.
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    3. Yusuf Yilmaz & Can B. Kalayci, 2022. "Variable Neighborhood Search Algorithms to Solve the Electric Vehicle Routing Problem with Simultaneous Pickup and Delivery," Mathematics, MDPI, vol. 10(17), pages 1-22, August.
    4. Marco Locatelli & Fabio Schoen, 2016. "Global optimization based on local searches," Annals of Operations Research, Springer, vol. 240(1), pages 251-270, May.
    5. 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.
    6. 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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