Applied Intelligence

, Volume 40, Issue 1, pp 44–53 | Cite as

Population based Local Search for university course timetabling problems

  • Anmar Abuhamdah
  • Masri Ayob
  • Graham Kendall
  • Nasser R. Sabar


Population based algorithms are generally better at exploring a search space than local search algorithms (i.e. searches based on a single heuristic). However, the limitation of many population based algorithms is in exploiting the search space. We propose a population based Local Search (PB-LS) heuristic that is embedded within a local search algorithm (as a mechanism to exploit the search space). PB-LS employs two operators. The first is applied to a single solution to determine the force between the incumbent solution and the trial current solution (i.e. a single direction force), whilst the second operator is applied to all solutions to determine the force in all directions. The progress of the search is governed by these forces, either in a single direction or in all directions. Our proposed algorithm is able to both diversify and intensify the search more effectively, when compared to other local search and population based algorithms. We use university course timetabling (Socha benchmark datasets) as a test domain. In order to evaluate the effectiveness of PB-LS, we perform a comparison between the performances of PB-LS with other approaches drawn from the scientific literature. Results demonstrate that PB-LS is able to produce statistically significantly higher quality solutions, outperforming many other approaches on the Socha dataset.


Course timetabling problem Metaheuristics Population based algorithm Hybrid methods Gravitational emulation 


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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Anmar Abuhamdah
    • 1
    • 2
  • Masri Ayob
    • 2
  • Graham Kendall
    • 3
    • 4
  • Nasser R. Sabar
    • 2
  1. 1.Department of Computer Science, College of Computer Science and EngineeringTaibah UniversityMadinahKingdom of Saudi Arabia
  2. 2.Data Mining and Optimisation Research Group (DMO), Center for Artificial Intelligence TechnologyUniversiti Kebangsaan MalaysiaBangiMalaysia
  3. 3.ASAP Research Group, School of Computer ScienceThe University of NottinghamNottinghamUK
  4. 4.The University of Nottingham Malaysia CampusSemenyihMalaysia

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