Soft Computing

, Volume 21, Issue 22, pp 6755–6765 | Cite as

An adaptive guided variable neighborhood search based on honey-bee mating optimization algorithm for the course timetabling problem

  • Rafidah Abdul Aziz
  • Masri Ayob
  • Zalinda Othman
  • Zulkifli Ahmad
  • Nasser R. Sabar
Methodologies and Application
First Online:
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Abstract

A standard honey-bee mating optimization algorithm (HBMO) utilizes the steepest descent local search algorithm as a worker. The steepest descent algorithm has the advantage of being simple to understand, fast and is easy to implement. However, it can easily trapped in a local optimum and subsequently restrict the performance of HBMO. Furthermore, the type of neighborhood structures that are used within the local search algorithm might impact on the performance of algorithm. This work aimed to enhance the performance of HBMO by using an adaptive guided variable neighborhood search (AGVNS) as a worker. The AGVNS algorithm is a variant of variable neighborhood search algorithm that incorporates some problem-specific knowledge and utilizes an adaptive learning mechanism to find the most suitable neighborhood structure during the searching process. In order to evaluate the effectiveness of the proposed algorithm, the Socha course timetabling dataset has been chosen as the tested domain problem. The results demonstrated that the performance of the proposed algorithm is comparable to other approaches in the literature. Indeed, the proposed algorithm obtained the best results as compared to other approaches on some instances. These results indicate the effectiveness of combining HBMO and AGVNS for solving course timetabling problems, hence demonstrated that the AGVNS can enhance the performance of HBMO.

Keywords

Honey-bee mating optimization Course timetabling problem Adaptive guided variable neighborhood search 
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Notes

Acknowledgments

This work was supported by Universiti Kebangsaan Malaysia Grant Dana Impak Perdana (DIP-2014-039).

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest regarding the publication of this article.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Rafidah Abdul Aziz
    • 1
  • Masri Ayob
    • 1
  • Zalinda Othman
    • 1
  • Zulkifli Ahmad
    • 2
  • Nasser R. Sabar
    • 1
  1. 1.Data Mining and Optimization Research Group, Centre of Artificial Intelligence TechnologyUniversiti Kebangsaan Malaysia (UKM)BangiMalaysia
  2. 2.School of Linguistics and Language Studies, Faculty of Social Sciences and HumanitiesUniversiti Kebangsaan Malaysia (UKM)BangiMalaysia

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