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Neighborhood Composition Strategies in Stochastic Local Search

  • Janniele A. S. AraujoEmail author
  • Haroldo G. Santos
  • Davi D. Baltar
  • Túlio A. M. Toffolo
  • Tony Wauters
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9668)

Abstract

Methods based on Stochastic Local Search (SLS) have been ranked as the best heuristics available for many hard combinatorial optimization problems. The design of SLS methods which use many neighborhoods poses difficult questions regarding the exploration of these neighborhoods: how much computational effort should be invested in each neighborhood? Should this effort remain fixed during the entire search or should it be dynamically updated as the search progresses? Additionally, is it possible to learn the best configurations during runtime without sacrificing too much the computational efficiency of the search method? In this paper we explore different tuning strategies to configure a state-of-the-art algorithm employing fourteen neighborhoods for the Multi-Mode Resource Constrained Multi-Project Scheduling Problem. An extensive set of computational experiments provide interesting insights for neighborhood selection and improved upper bounds for many hard instances from the literature.

Keywords

Stochastic local search Project scheduling Multi-neighborhood search Learning Online and offline tuning 

Notes

Acknowledgments

The authors thank CNPq and FAPEMIG for supporting this research.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Janniele A. S. Araujo
    • 1
    • 2
    Email author
  • Haroldo G. Santos
    • 2
  • Davi D. Baltar
    • 1
  • Túlio A. M. Toffolo
    • 2
    • 3
  • Tony Wauters
    • 3
  1. 1.Computer and Systems DepartmentFederal University of Ouro PretoOuro PretoBrazil
  2. 2.Department of ComputingFederal University of Ouro PretoOuro PretoBrazil
  3. 3.Computer Science Department, CODeSKU LeuvenLeuvenBelgium

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