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Variable Neighborhood Descent

In: Handbook of Heuristics

Author

Listed:
  • Abraham Duarte

    (Universidad Rey Juan Carlos, Department of Computer Science and Statistics)

  • Nenad Mladenović

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

  • Jesús Sánchez-Oro

    (Universidad Rey Juan Carlos, Department of Computer Science and Statistics)

  • Raca Todosijević

    (Univ. Polytechnique Hauts-de-France, LAMIH, CNRS
    INSA Hauts-de-France)

Abstract

Variable neighborhood descent (VND) is a local search-based heuristic which performs a deterministic exploration of a fixed set of neighborhood structures. Its success is based on the simple fact that different neighborhood structures usually have different local minimum. Thus, the local optima trap problem may be eventually solved by deterministic change of neighborhoods. VND can be used as a local search routine, and, therefore, it could be used within other metaheuristics. In this chapter we discuss typical problems that arise when designing VND: 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 considering the traveling salesman problem.

Suggested Citation

  • Abraham Duarte & Nenad Mladenović & Jesús Sánchez-Oro & Raca Todosijević, 2025. "Variable Neighborhood Descent," Springer Books, in: Rafael Martí & Panos M. Pardalos & Mauricio G.C. Resende (ed.), Handbook of Heuristics, edition 0, chapter 18, pages 511-539, Springer.
    • Handle: RePEc:spr:sprchp:978-3-032-00385-0_9
    DOI: 10.1007/978-3-032-00385-0_9
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