Journal of Scheduling

, Volume 17, Issue 3, pp 249–262 | Cite as

An adaptive artificial bee colony and late-acceptance hill-climbing algorithm for examination timetabling

  • M. Alzaqebah
  • S. Abdullah


The artificial bee colony (ABC) is a population-based metaheuristic that mimics the foraging behaviour of honeybees in order to produce high-quality solutions for optimisation problems. The ABC algorithm combines both exploration and exploitation processes. In the exploration process, the worker bees are responsible for selecting a random solution and applying it to a random neighbourhood structure, while the onlooker bees are responsible for choosing a food source based on a selection strategy. In this paper, a disruptive selection strategy is applied within the ABC algorithm in order to improve the diversity of the population and prevent premature convergence in the evolutionary process. A self-adaptive strategy for selecting neighbourhood structures is added to further enhance the local intensification capability (adaptively choosing the neighbourhood structure helps the algorithm to escape local optima). Finally, a modified ABC algorithm is hybridised with a local search algorithm, i.e. the late-acceptance hill-climbing algorithm, to quickly descend to a good-quality solution. The experiments show that the ABC algorithm with the disruptive selection strategy outperforms the original ABC algorithm. The hybridised ABC algorithm also outperforms the lone ABC algorithm when tested on examination timetabling problems.


Artificial bee colony Late-acceptance hill climbing Examination timetabling problems  Selection strategy Self-adaptive strategy 


  1. Abdullah, S., & Burke, E. K. (2006). A multi-start large neighbourhood search approach with local search methods for examination timetabling. In D. Long, S. F. Smith, D. Borrajo, & L. McCluskey (Eds.), International Conference on Automated Planning and Scheduling (ICAPS 2006), Cumbria, UK, 6–10 June (pp. 334–337).Google Scholar
  2. Abdullah, S., Burke, E. K., & McCollum, B. (2007a). Using a randomised iterative improvement algorithm with composite neighbourhood structures for university course timetabling. In Metaheuristics: Progress in Complex Systems Optimization (Operations Research/Computer Science Interfaces Series) (Chap. 8, pp. 153–169). New York: Springer.Google Scholar
  3. Abdullah, S., Ahmadi, S., Burke, E. K., & Dror, M. (2007b). Investigating Ahuja-Orlin’s large neighbourhood search approach for examination timetabling. OR Spectrum, 29(2), 351–372.CrossRefGoogle Scholar
  4. Abdullah, S., Turabieh, H., & McCollum, B. (2009). A hybridization of electromagnetic like mechanism and great deluge for examination timetabling problems. In HM2009: 6th International Workshop on Hybrid Metaheuristics, Udine (pp. 60–72).Google Scholar
  5. Alzaqebah, M., & Abdullah, S. (2011a). Comparison of the selection strategy in the artificial bee colony algorithm for examination timetabling problems. International Journal of Soft Computing and Engineering, 1(5), 158–163.Google Scholar
  6. Alzaqebah, M., Abdullah, S. (2011b). Hybrid artificial bee colony search algorithm based on disruptive selection for examination timetabling problems. In International Conference on Combinatorial Optimization and Applications (COCOA 2011). LNCS (Vol. 6831, pp. 31–45). Berlin: Springer-Verlag.Google Scholar
  7. Atsuta, M., Nonobe, N., & Ibaraki, T. (2007). ITC2007 Track 1: An Approach using general CSP solver.
  8. Bao, L., & Zeng, J. (2009). Comparison and analysis of the selection mechanism in the artificial bee colony algorithm. HIS, 1, 411–416.Google Scholar
  9. Baykasoglu, A., Ozbakir, L., & Tapkan, P. (2007). Artificial bee colony algorithm and its application to generalized assignment problem. In T. S. C. Felix, & K. T. Manoj (Eds.), Swarm Intelligence: Focus on Ant and Particle Swarm Optimization (pp. 113–143). Vienna: Itech Education and Publishing.Google Scholar
  10. Burke, E. K., & Bykov, Y. (2008). A late acceptance strategy in hill-climbing for exam timetabling problems. In Conference on the Practice and Theory of Automated Timetabling (PATAT 2008).Google Scholar
  11. Burke, E. K., & Bykov, Y. (2012). The late acceptance hill-climbing heuristic, technical report CSM-192. Stirling: Computing Science and Mathematics, University of Stirling.Google Scholar
  12. Burke, E. K., & Newall, J. P. (2004). Solving examination timetabling problems through adaptation of heuristic orderings. Annals of Operations Research, 129, 107–134.CrossRefGoogle Scholar
  13. Burke, E. K., Bykov, Y., Newall, J. P., & Petrovic, S. (2004). A time-predefined local search approach to exam timetabling problem. IIE Transactions, 36(6), 509–528.CrossRefGoogle Scholar
  14. Burke, E. K., Eckersley, A. J., McCollum, B., Petrovic, S., & Qu, R. (2010). Hybrid variable neighbourhood approaches to university exam timetabling. European Journal of Operation Research, 206(1), 46–53.CrossRefGoogle Scholar
  15. Burke, E. K., Elliman, D. G., Ford, P. H., & Weare, R. F. (1996). Examination timetabling in British universities: A survey. In E. K. Burke & P. Ross (Eds.), Practice and Theory of Automated Timetabling: Selected Papers from the First International Conference (Vol. 1153, pp. 76–90). Lecture Notes in Computer Science. Berlin: Springer-Verlag.Google Scholar
  16. Caramia, M., Dellolmo, P., & Italiano, G. F. (2009). Novel local search-based approaches to university examination timetabling. INFORMS Journal on Computing, 20(1), 86–99.CrossRefGoogle Scholar
  17. Carter, M. W. (1986). A survey of practical applications of examination timetabling algorithms. Operations Research, 34(2), 193–202.CrossRefGoogle Scholar
  18. Carter, M. W., Laporte, G., & Lee, S. Y. (1996). Examination timetabling: Algorithmic strategies and applications. Journal of the Operational Research Society, 47, 373–383.CrossRefGoogle Scholar
  19. Chu, S. C., Chen, Y. T., & Ho, J. H. (2006). Timetable scheduling using particle swarm optimization. In First International Conference on Innovation Computing, Information and Control (pp. 324–327). Washington, DC: IEEE Computer Society.Google Scholar
  20. Cooper, T. B., & Kingston, J. H. (1995). The complexity of timetable construction problems. In Proceedings of the 1st International Conference on Practice and Theory of Automated Timetabling (PATAT 1995). Lecture Notes in Computer Science (Vol. 1153, pp. 283–295). New York: Springer-Verlag.Google Scholar
  21. Dowsland, K. A., & Thompson, J. (2005). Ant colony optimization for the examination scheduling problem. Journal of Operational Research Society, 56, 426–438.CrossRefGoogle Scholar
  22. Garcia, S., Fernández, A., Luengo, J., & Herrera, F. (2010). Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power. Information Sciences, 180(10), 2044–2064.CrossRefGoogle Scholar
  23. Garcia, S., Molina, D., Lozano, M., & Herrera, F. (2009). A study on the use of non-parametric tests for analysing the evolutionary algorithms’ behaviour: A case study on the CEC’2005 special session on real parameter optimization. Journal of Heuristics, 15, 617–644.CrossRefGoogle Scholar
  24. Gogos, C., Alefragis, P., Housos, E. (2008). A multi-staged algorithmic process for the solution of the examination timetabling problem. In Practice and Theory of Automated Timetabling (PATAT 2008), Montreal (pp. 19–22).Google Scholar
  25. Holland, J. (1975). Adaptation in natural and artificial systems. Ann Arbor: University of Michigan Press.Google Scholar
  26. Kang, F., Li, J., & Xu, Q. (2009). Structural inverse analysis by hybrid simplex artificial bee colony algorithms. Computers and Structures, 87, 861–870. Google Scholar
  27. Karaboga, N., (2005). An idea based on honey bee swarm for numerical optimization. Technical Report TR06. Engineering Faculty, Computer Engineering Department, Erciyes University, Turkey.Google Scholar
  28. Karaboga, N. (2009). A new design method based on artificial bee colony algorithm for digital IIR filters. Journal of the Franklin Institute, 346(4), 328–348.CrossRefGoogle Scholar
  29. Karaboga, N., & Akay, B. (2009). A comparative study of artificial bee colony algorithm. Applied Mathematics and Computation, 1(214), 108–132.Google Scholar
  30. Karaboga, N., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm. Journal of Global Optimization, 39, 459–471.CrossRefGoogle Scholar
  31. Karaboga, N., & Basturk, B. (2008). On the performance of artificial bee colony (ABC) algorithm. Applied Soft Computing, 8, 687–697.CrossRefGoogle Scholar
  32. Kuo, T., & Huang, S. Y. (1997). Using disruptive selection to maintain diversity in genetic algorithms. Applied Intelligence, 7(3), 257–267.CrossRefGoogle Scholar
  33. Lewis, R. (2008). A survey of metaheuristic-based techniques for university timetabling problems. OR Spectrum, 30(1), 167–190.CrossRefGoogle Scholar
  34. McCollum, B., Schaerf, A., Paechter, B., McMullan, P., Lewis, R., Parkes, A., et al. (2010). Setting the research agenda in automated timetabling: The second international timetabling competition. INFORMS Journal on Computing, 22, 120–130.CrossRefGoogle Scholar
  35. Muller, T. (2009). ITC2007 solver description: A hybrid approach. Annals of Operation Research, 172(1), 429–446.CrossRefGoogle Scholar
  36. Pan, Q. K., Tasgetiren, M. F., Suganthan, P. N., & Chua, T. J. (2011). A discrete artificial bee colony algorithm for the lot-streaming flow shop scheduling problem. Information Sciences, 181(12), 2455–2468.CrossRefGoogle Scholar
  37. Pham, D. T., Ghanbarzadeh, A., Koc, E., & Otri, S. (2006). Application of the bees algorithm to the training of radial basis function networks for control 213 chart pattern recognition. In 5th CIRP International Seminar on Intelligent Computation in Manufacturing Engineering, CIRP ICME, Ischia, Italy (pp. 711–716).Google Scholar
  38. Pillay, A. (2007). Developmental approach to the examination timetabling problem.
  39. Qu, R., Burke, E. K., McCollum, B., & Merlot, L. T. G. (2009). A survey of search methodologies and automated system development for examination timetabling. Journal of Scheduling, 12(1), 55–89.CrossRefGoogle Scholar
  40. Sabar, N. R., Ayob, M., & Kendall, G. (2009). Solving examination timetabling problems using honey-bee mating optimization (ETP-HBMO). In Proceedings of the 4th Multidisciplinary International Scheduling Conference: Theory and Applications (MISTA 2009), Dublin, Ireland (pp. 399–408).Google Scholar
  41. Schaerf, A. (1999). A survey of automated timetabling. Artificial Intelligence Review, 13(2), 87–127.CrossRefGoogle Scholar
  42. Singh, A. (2009). An artificial bee colony algorithm for the leaf-constrained minimum spanning tree problem. Applied Soft Computing, 9, 625–631.CrossRefGoogle Scholar
  43. Turabieh, H. & Abdullah, S. (2011a). A hybrid fish swarm optimisation algorithm for solving the examination timetabling problems. In Learning and Intelligent Optimisation Workshop (LION 5), Rome. Lecture Notes in Computer Science (Vol. 6683, pp. 539–551). Berlin: Springer-Verlag.Google Scholar
  44. Turabieh, H., & Abdullah, S. (2011b). An integrated hybrid approach to the examination timetabling problem. OMEGA–The International Journal of Management Science, 39(6), 598–607.Google Scholar
  45. Yang, Y., & Petrovic, S. (2005). A novel similarity measure for heuristic selection in examination timetabling. In E. K. Burke & M. Trick (Eds.), Practice and Theory of Automated Timetabling V: Selected Papers from the 5th International Conference (Vol. 3616, pp. 377–396). Lecture Notes in Computer Science. Berlin: Springer.Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Universiti Kebangsaan MalaysiaBangiMalaysia

Personalised recommendations