Variable Neighborhood Descent

Abraham Duarte (), Jesús Sánchez-Oro (), Nenad Mladenović () and Raca Todosijević ()
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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
Raca Todosijević: Université de Valenciennes, LAMIH, France and Mathematical Institute, SANU

Chapter 12 in Handbook of Heuristics, 2018, pp 341-367 from Springer

Abstract: 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.

Keywords: Variable neighborhood descent; Local search; Intensification; Deterministic exploration (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/978-3-319-07124-4_9

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