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Handbook of Heuristics pp 759–787Cite as

Variable Neighborhood Search

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Abstract

Variable neighborhood search (VNS) is a metaheuristic for solving combinatorial and global optimization problems. Its basic idea is 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 variable neighborhood search and some of its extensions. We next present four families of applications of VNS in which it has proved to be very successful: (i) finding feasible solutions to large mixed-integer linear programs, by hybridization of VNS and local branching, (ii) finding good feasible solutions to continuous nonlinear programs, (iii) finding programs in automatic fashion (artificial intelligence field) by building variable neighborhood programming methodology, and (iv) exploring graph theory in order to find conjectures, refutations, and proofs or ideas of proofs.

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Author information

Authors and Affiliations

  1. GERAD and Ecole des Hautes Etudes Commerciales, Montréal, QC, Canada

    Pierre Hansen & Nenad Mladenović

  2. LAMIH, University of Valenciennes, Famars, France

    Nenad Mladenović

Authors
  1. Pierre Hansen
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  2. Nenad Mladenović
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Corresponding author

Correspondence to Pierre Hansen .

Editor information

Editors and Affiliations

  1. Statistics and Operations Research Department, University of Valencia, Valencia, Spain

    Rafael Martí

  2. Department of Industrial and Systems Engineering, University of Florida, Gainesville, Florida, USA

    Panos M. Pardalos

  3. Amazon.com, Inc. and University of Washington, Seattle, Washington, USA

    Mauricio G. C. Resende

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Hansen, P., Mladenović, N. (2018). Variable Neighborhood Search. In: Martí, R., Pardalos, P., Resende, M. (eds) Handbook of Heuristics. Springer, Cham. https://doi.org/10.1007/978-3-319-07124-4_19

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