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Guided Local Search

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Handbook of Heuristics

Abstract

Guided local search (GLS) is a meta-heuristic method proposed to solve combinatorial optimization problems. It is a high-level strategy that applies an efficient penalty-based approach to interact with the local improvement procedure. This interaction creates a process capable of escaping from local optima, which improves the efficiency and robustness of the underlying local search algorithms. Fast local search (FLS) is a way of reducing the size of the neighborhood to improve the efficiency of local search. GLS can be efficiently combined with FLS in the form of guided fast local search (GFLS). This chapter describes the principles of GLS and provides guidance for implementing and using GLS, FLS, and GFLS. It also surveys GLS extensions, hybrids, and applications to optimization, including multi-objective optimization.

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Authors and Affiliations

  1. College of Computer and Information Sciences (CCIS), Al-Imam Muhammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia

    Abdullah Alsheddy

  2. Department of Computer Science, University of Essex, Colchester, UK

    Christos Voudouris & Edward P. K. Tsang

  3. Department of Computer Science, Umm Al-Qura University, Makkah, Saudi Arabia

    Ahmad Alhindi

Authors
  1. Abdullah Alsheddy
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  2. Christos Voudouris
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  3. Edward P. K. Tsang
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  4. Ahmad Alhindi
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Corresponding author

Correspondence to Abdullah Alsheddy .

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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Alsheddy, A., Voudouris, C., Tsang, E.P.K., Alhindi, A. (2018). Guided Local Search. In: Martí, R., Pardalos, P., Resende, M. (eds) Handbook of Heuristics. Springer, Cham. https://doi.org/10.1007/978-3-319-07124-4_2

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