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Guided Local Search

In: Handbook of Metaheuristics

Author

Listed:
  • Christos Voudouris

    (Group Chief Technology Office, BT plc, Orion Building, mlb1/pp12, Marthlesham Heath)

  • Edward P.K. Tsang

    (University of Essex)

  • Abdullah Alsheddy

    (University of Essex)

Abstract

Combinatorial explosion is a well-known phenomenon that prevents complete algorithms from solving many real-life combinatorial optimization problems. In many situations, heuristic search methods are needed. This chapter describes the principles of Guided Local Search (GLS) and Fast Local Search (FLS) and surveys their applications. GLS is a penalty-based metaheuristic algorithm that sits on top of other local search algorithms, with the aim to improve their efficiency and robustness. FLS is a way of reducing the size of the neighbourhood to improve the efficiency of local search. The chapter also provides guidance for implementing and using GLS and FLS. Four problems, representative of general application categories, are examined with detailed information provided on how to build a GLS-based method in each case.

Suggested Citation

  • Christos Voudouris & Edward P.K. Tsang & Abdullah Alsheddy, 2010. "Guided Local Search," International Series in Operations Research & Management Science, in: Michel Gendreau & Jean-Yves Potvin (ed.), Handbook of Metaheuristics, chapter 0, pages 321-361, Springer.
    • Handle: RePEc:spr:isochp:978-1-4419-1665-5_11
    DOI: 10.1007/978-1-4419-1665-5_11
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    Citations

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    Cited by:

    1. Mori, Masakatsu & Kobayashi, Ryoji & Samejima, Masaki & Komoda, Norihisa, 2017. "Risk-cost optimization for procurement planning in multi-tier supply chain by Pareto Local Search with relaxed acceptance criterion," European Journal of Operational Research, Elsevier, vol. 261(1), pages 88-96.
    2. Shen, Yunzhuang & Sun, Yuan & Li, Xiaodong & Eberhard, Andrew & Ernst, Andreas, 2023. "Adaptive solution prediction for combinatorial optimization," European Journal of Operational Research, Elsevier, vol. 309(3), pages 1392-1408.
    3. Simona Mancini, 2013. "Multi-echelon distribution systems in city logistics," European Transport \ Trasporti Europei, ISTIEE, Institute for the Study of Transport within the European Economic Integration, issue 54, pages 1-2.
    4. Lakmali Weerasena & Aniekan Ebiefung & Anthony Skjellum, 2022. "Design of a heuristic algorithm for the generalized multi-objective set covering problem," Computational Optimization and Applications, Springer, vol. 82(3), pages 717-751, July.
    5. Carl H. Häll & Anders Peterson, 2013. "Improving paratransit scheduling using ruin and recreate methods," Transportation Planning and Technology, Taylor & Francis Journals, vol. 36(4), pages 377-393, June.
    6. Alokananda Dey & Siddhartha Bhattacharyya & Sandip Dey & Debanjan Konar & Jan Platos & Vaclav Snasel & Leo Mrsic & Pankaj Pal, 2023. "A Review of Quantum-Inspired Metaheuristic Algorithms for Automatic Clustering," Mathematics, MDPI, vol. 11(9), pages 1-44, April.
    7. Jaszkiewicz, Andrzej, 2018. "Many-Objective Pareto Local Search," European Journal of Operational Research, Elsevier, vol. 271(3), pages 1001-1013.
    8. Takfarinas Saber & Xavier Gandibleux & Michael O’Neill & Liam Murphy & Anthony Ventresque, 2020. "A comparative study of multi-objective machine reassignment algorithms for data centres," Journal of Heuristics, Springer, vol. 26(1), pages 119-150, February.

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