Skip to main content
SpringerLink
Log in

Particle Swarm Optimization with Transition Probability for Timetabling Problems

  • Conference paper
Adaptive and Natural Computing Algorithms (ICANNGA 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7824))

Included in the following conference series:

Abstract

In this paper, we propose a new algorithm to solve university course timetabling problems using a Particle Swarm Optimization (PSO). PSOs are being increasingly applied to obtain near-optimal solutions to many numerical optimization problems. However, it is also being increasingly realized that PSOs do not solve constraint satisfaction problems as well as other meta-heuristics do. In this paper, we introduce transition probability into PSO to settle this problem. Experiments using timetables of the University of Tsukuba showed that this approach is a more effective solution than an Evolution Strategy.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Schaerf, A.: A Survey of Automated Timetabling. Artificial Intelligence Review 13(2), 87–127 (1999)

    Article  Google Scholar 

  2. Gröbner, M., Wilke, P., Büttcher, S.: A Standard Framework for Timetabling Problems. In: Burke, E.K., De Causmaecker, P. (eds.) PATAT 2002. LNCS, vol. 2740, pp. 24–38. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  3. Dimopoulou, M., Miliotis, P.: An Automated University Course Timetabling System Developed in a Distributed Environment: a Case Study. European Journal of Operational Research 153, 136–147 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  4. Fukushima, M.: A Hybrid Algorithm for the University Course Timetabling Problems. Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 22(1), 142–147 (2010)

    Article  Google Scholar 

  5. Burke, E.K., MacCarthy, B., Petrovic, S., Qu, R.: Multiple-Retrieval Case Based Reasoning for Course Timetabling Problems. Journal of the Operational Research Society 57(2), 148–162 (2006)

    MATH  Google Scholar 

  6. Burke, E.K., et al.: A Graph-Based Hyper-Heuristic for Educational Timetabling Problems. European Journal of Operational Research 176(1), 177–192 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  7. Socha, K., Sampels, M., Manfrin, M.: Ant Algorithms for the University Course Timetabling Problem with Regard to the State-of-the-Art. In: Raidl, G.R., Cagnoni, S., Cardalda, J.J.R., Corne, D.W., Gottlieb, J., Guillot, A., Hart, E., Johnson, C.G., Marchiori, E., Meyer, J.-A., Middendorf, M. (eds.) EvoIASP 2003, EvoWorkshops 2003, EvoSTIM 2003, EvoROB/EvoRobot 2003, EvoCOP 2003, EvoBIO 2003, and EvoMUSART 2003. LNCS, vol. 2611, pp. 334–345. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  8. Burke, E.K., Landa, J.D.: The Design of Memetic Algorithms for Scheduling and Timetabling Problems. In: Recent Advances in Memetic Algorithms and Related Search Technologies, pp. 289–312. Springer (2004)

    Google Scholar 

  9. Lewis, R., Paechter, B.: Finding Feasible Timetables Using Group-Based Operators. IEEE Transactions on Evolutionary Computation 11(3), 397–413 (2007)

    Article  Google Scholar 

  10. Kanoh, H., Sakamoto, Y.: Knowledge-Based Genetic Algorithm for University Course Timetabling Problems. International Journal of Knowledge-Based and Intelligent Engineering Systems 12(4), 283–294 (2008)

    Google Scholar 

  11. Qu, R., Burke, E.K.: Hybridizations within a Graph-based Hyper-heuristic Framework for University Timetabling Problems. Journal of the Operational Research Society 60, 1273–1285 (2009)

    Article  MATH  Google Scholar 

  12. Fen, H.S., Safaai, D., Hashim, M., Zaiton, S.: University Course Timetable Planning Using Hybrid, Particle Swarm Optimization. In: GEC 2009, June 12-14 (2009)

    Google Scholar 

  13. Poli, R., Kennedy, J., Blackwell, T.: Particle Swarm Optimization: An Overview. Swarm Intell. 1, 33–57 (2007)

    Article  Google Scholar 

  14. Banks, A., Vincent, J., Anyakoha, C.: A Review of Particle Swarm Optimization Part II: Hybridisation, Combinatorial, Multicriteria and Constrained Optimization, and Indicative Applications. Nat. Comput. 7, 109–124 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  15. Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: IEEE Int. Conf. Neural Netw., vol. 4, pp. 1942–1948 (1995)

    Google Scholar 

  16. Eberhart, R., Kennedy, J.: A New Optimizer Using Particle Swarm Theory. In: Proc. 6th Int. Symp. Micro Machine Human Science, pp. 39–43 (1995)

    Google Scholar 

  17. Shi, Y., Eberhart, R.: A Modified Particle Swarm Optimizer. IEEE World Congr. Comput. Intell., 69–73 (1998)

    Google Scholar 

  18. Chen, W.N., et al.: A Novel Set-Based Particle Swarm Optimization Method for Discrete Optimization Problems. IEEE Transactions on Evolutionary Computation 14(2), 278–300 (2010)

    Article  Google Scholar 

  19. Shiau, D.F.: A Hybrid Particle Swarm Optimization for a University Course Scheduling Problem with Flexible Preferences. Expert Systems with Applications 38, 235–248 (2011)

    Article  Google Scholar 

  20. Tassopoulos, I.X., Beligiannis, G.N.: Solving Effectively the School Timetabling Problem using Particle Swarm Optimization. Expert Systems with Applications 39, 6029–6040 (2012)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Engineering, Information and Systems, University of Tsukuba, Tsukuba, Japan

    Hitoshi Kanoh

  2. Graduate School of Systems and Information Engineering, University of Tsukuba, Japan

    Satoshi Chen

Authors
  1. Hitoshi Kanoh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Satoshi Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Départment des Systémes d’Information, Quartier UNIL-Dorigny, Bâtiment Internef, Université de Lausanne, 105, Lausanne, Switzerland

    Marco Tomassini , Alberto Antonioni , Fabio Daolio  & Pierre Buesser , ,  & 

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Kanoh, H., Chen, S. (2013). Particle Swarm Optimization with Transition Probability for Timetabling Problems. In: Tomassini, M., Antonioni, A., Daolio, F., Buesser, P. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2013. Lecture Notes in Computer Science, vol 7824. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37213-1_27

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-37213-1_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37212-4

  • Online ISBN: 978-3-642-37213-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation