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Multi-neighbourhood Local Search with Application to Course Timetabling

  • Luca Di Gaspero
  • Andrea Schaerf
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2740)

Abstract

A recent trend in local search concerns the exploitation of several different neighbourhood functions so as to increase the ability of the algorithm to navigate the search space.

In this paper we investigate the use of local search techniques based on various combinations of neighbourhood functions, and we apply this to a timetabling problem. In particular, we propose a set of generic operators that automatically compose neighbourhood functions, giving rise to more complex ones. In the exploration of large neighbourhoods, we rely on constraint techniques to prune the list of candidates. In this way, we are able to select the most effective search technique through a systematic analysis of all possible combinations built upon a set of basic, human-defined, neighbourhood functions.

The proposed ideas are applied to a practical problem, namely the Course Timetabling problem. Our algorithms are systematically tested and compared on real-world instances. The experimental analysis shows that neighbourhood composition leads to much better results than traditional local search techniques.

Keywords

Local Search Tabu Search Local Search Algorithm Hard Constraint Tabu List 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Luca Di Gaspero
    • 1
  • Andrea Schaerf
    • 2
  1. 1.Dipartimento di Matematica e InformaticaUniversità di UdineUdineItaly
  2. 2.Dipartimento di Ingegneria Elettrica, Gestionale e MeccanicaUniversità di UdineUdineItaly

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