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Variable Neighborhood Descent

In: Handbook of Heuristics

Author

Listed:
  • Abraham Duarte

    (Universidad Rey Juan Carlos, Department of Ciencias de la Computación)

  • Jesús Sánchez-Oro

    (Universidad Rey Juan Carlos)

  • Nenad Mladenović

    (GERAD and Ecole des Hautes Etudes Commerciales
    University of Valenciennes, LAMIH
    Université de Valenciennes, LAMIH, France and Mathematical Institute, SANU)

  • Raca Todosijević

    (Université de Valenciennes, LAMIH, France and Mathematical Institute, SANU)

Abstract

Local search heuristic that explores several neighborhood structures in a deterministic way is called variable neighborhood descent (VND). Its success is based on the simple fact that different neighborhood structures do not usually have the same local minimum. Thus, the local optima trap problem may be resolved by deterministic change of neighborhoods. VND may be seen as a local search routine and therefore could be used within other metaheuristics. In this chapter, we discuss typical problems that arise in developing VND heuristic: what neighborhood structures could be used, what would be their order, what rule of their change during the search would be used, etc. Comparative analysis of usual sequential VND variants is performed in solving traveling salesman problem.

Suggested Citation

  • Abraham Duarte & Jesús Sánchez-Oro & Nenad Mladenović & Raca Todosijević, 2018. "Variable Neighborhood Descent," Springer Books, in: Rafael Martí & Panos M. Pardalos & Mauricio G. C. Resende (ed.), Handbook of Heuristics, chapter 12, pages 341-367, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-07124-4_9
    DOI: 10.1007/978-3-319-07124-4_9
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    Cited by:

    1. Aloise, Daniel & Moine, Robin & Ribeiro, Celso C. & Jalbert, Jonathan, 2025. "First-improvement or best-improvement? An in-depth local search computational study to elucidate a dominance claim," European Journal of Operational Research, Elsevier, vol. 326(3), pages 413-426.

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