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Variable Neighborhood Search

  • Pierre Hansen
  • Nenad Mladenović
  • Jack Brimberg
  • José A. Moreno Pérez
Chapter
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 146)

Abstract

Variable neighborhood search (VNS) is a metaheuristic for solving combinatorial and global optimization problems whose basic idea is a systematic change of neighborhood both within a descent phase to find a local optimum and in a perturbation phase to get out of the corresponding valley. In this chapter we present the basic schemes of VNS and some of its extensions. We then describe a recent development, i.e., formulation space search. We then present five families of applications in which VNS has proven to be very successful: (i) exact solution of large-scale location problems by primal–dual VNS; (ii) generation of feasible solutions to large mixed integer linear programs by hybridization of VNS and local branching; (iii) generation of good feasible solutions to continuous nonlinear programs; (iv) generation of feasible solutions and/or improved local optima for mixed integer nonlinear programs by hybridization of sequential quadratic programming and branch and bound within a VNS framework, and (v) exploration of graph theory to find conjectures, refutations, and proofs or ideas of proofs.

Keywords

Local Search Mixed Integer Linear Programming Variable Neighborhood Search Global Optimization Problem Extremal Graph 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.GERAD and Ecole des Hautes Etudes CommercialesMontréalCanada
  2. 2.School of MathematicsBrunel University-West LondonUxbridgeUK
  3. 3.Department of Mathematics and Computer ScienceRoyal Military College of CanadaKingstonCanada
  4. 4.IUDR and DEIOC, Universidad de La LagunaLa LagunaSpain

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