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Guided Local Search

In: Handbook of Heuristics

Author

Listed:
  • Abdullah Alsheddy

    (Al-Imam Muhammad Ibn Saud Islamic University (IMSIU), College of Computer and Information Sciences (CCIS))

  • Christos Voudouris

    (University of Essex, Department of Computer Science)

  • Edward P. K. Tsang

    (University of Essex, Department of Computer Science)

  • Ahmad Alhindi

    (Umm Al-Qura University, Department of Computer Science)

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 approachpenalty-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.

Suggested Citation

  • Abdullah Alsheddy & Christos Voudouris & Edward P. K. Tsang & Ahmad Alhindi, 2018. "Guided Local Search," Springer Books, in: Rafael Martí & Panos M. Pardalos & Mauricio G. C. Resende (ed.), Handbook of Heuristics, chapter 10, pages 261-297, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-07124-4_2
    DOI: 10.1007/978-3-319-07124-4_2
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    Cited by:

    1. Rajabighamchi, Farzaneh & van Hoesel, Stan & Defryn, Christof, 2023. "The order picking problem under a scattered storage policy," Research Memorandum 006, Maastricht University, Graduate School of Business and Economics (GSBE).

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