Variable Neighborhood Descent

Abraham Duarte (), Nenad Mladenović (), Jesús Sánchez-Oro () and Raca Todosijević
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Abraham Duarte: Universidad Rey Juan Carlos, Department of Computer Science and Statistics
Nenad Mladenović: GERAD and Ecole des Hautes Etudes Commerciales
Jesús Sánchez-Oro: Universidad Rey Juan Carlos, Department of Computer Science and Statistics
Raca Todosijević: Univ. Polytechnique Hauts-de-France, LAMIH, CNRS

Chapter 18 in Handbook of Heuristics, 2025, pp 511-539 from Springer

Abstract: 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.

Keywords: VND; Local search; Neighborhood; Sequential VND; Nested VND; Mixed VND (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/978-3-032-00385-0_9

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