Hybridizing genetic algorithms and particle swarm optimization transplanted into a hyper-heuristic system for solving university course timetabling problem

WSEAS Transactions on Computers

Volumn 12, Issue 3, 2013, Pages 128-143

  Ahandani, Morteza Alinia ^a   Vakil Baghmisheh, Mohammad Taghi ^b  

^a ISLAMIC AZAD UNIVERSITY   (Iran)

^b UNIVERSITY OF TABRIZ   (Iran)

Author keywords

Crossover, Genetic algorithm; Hybrid algorithm; Hyper Heuristic; Particle swarm optimization; University course timetabling

Indexed keywords

COMPARATIVE EXPERIMENTS; GENETIC ALGORITHM (GAS); GLOBAL SEARCH ALGORITHM; GRAPH COLOURING HEURISTICS; HYBRID ALGORITHMS; HYPERHEURISTIC; UNIVERSITY COURSE TIMETABLING; UNIVERSITY COURSE TIMETABLING-PROBLEMS;

GRAPH THEORY; HEURISTIC METHODS; PARTICLE SWARM OPTIMIZATION (PSO); SCHEDULING;

GENETIC ALGORITHMS;

EID: 84879921827     PISSN: 11092750     EISSN: 22242872     Source Type: Journal    
DOI: None     Document Type: Article

References (76)

1. R. Qu, Case-based reasoning for course timetabling problems, PH.D. thesis, University of Nottingham, UK, 2002.
2. S. Abdullah, Heuristic approaches for university timetabling problem, PH.D. thesis, University of Nottingham, UK, 2006.
3. M.W. Carter, A comprehensive course timetabling and student scheduling system at the university of waterloo, In: Burke, E. K., Erben, W., eds.: The Practice and Theory of Automated Timetabling: Selected papers from the third International Conference, Lecture Notes in Computer Science, Springer-Verlag, Berlin, Vol.2079, 2000, pp. 64-82.
4. M. Amintoosi, J. Haddadnia, Feature selection in a fuzzy student sectioning algorithm, In: Burke, E. K., Trick, M., eds.: The Practice and Theory of Automated Timetabling V: Selected Papers from 5th International Conference on the Practice and Theory of Automated Timetabling, Lecture Notes in Computer Science, Springer-Verlag, Pittsburg, USA, Vol.3616, 2005, pp. 147-160.
5. P. De Causmaecker, P. Demeester, G.V. Berghe, A decomposed metaheuristic approach for a real-world university timetabling problem, Eur. J. Oper. Res, Vo.195, 2009, pp. 307-318.
6. G. Lajos, Complete university modular timetabling using constraint logic programming, In: Burke, E. K., Ross, P., eds.: The Practice and Theory of Automated Timetabling I: Selected Papers from 1st International Conference on the Practice and Theory of Automated Timetabling, Lecture Notes in Computer Science, Springer-Verlag, Edinburgh, UK, Vol.1153, 1996, pp. 146-161.
7. M. Henz, J. Würtz, Using Oz for college timetabling, In: Burke, E. K., Ross, P., eds.: The Practice and Theory of Automated Timetabling I: Selected Papers from 1st International Conference on the Practice and Theory of Automated Timetabling, Lecture Notes in Computer Science, Springer-Verlag, Edinburgh, UK, Vol.1153, 1996, pp. 162-177.
8. H. Cambazard, F. Demazeau, N. Jussien, P. David, Interactively solving school timetabling problems using extensions of constraint programming, In: Burke, E. K., Trick, M., eds.: The Practice and Theory of Automated Timetabling: Selected papers from the third International Conference, Lecture Notes in Computer Science, Springer-Verlag, Vol.124, 2004, pp. 107-124.
9. M.W. Carter, G. Laporte, Recent developments in practical examination timetabling, In: Burke E. K., and Ross, P., eds.: The Practice and Theory of Automated Timetabling I: Selected Papers from 1st International Conference on the Practice and Theory of Automated Timetabling, Lecture Notes in Computer Science, Springer-Verlag, Edinburgh, UK, Vol.1153, 1996, pp. 3-21.
10. E.K. Burke, J.H. Kingston, D. De Werra, Applications to timetabling, In: Gross, J., Yellen, J., eds.: The Handbook of Graph Theory, Chapman Hall/CRC Press, 2004, pp. 445-474.
11. M.W. Carter, G. Laporte, J.W. Chinneck, A general examination scheduling system, Interfaces, Vol.24, 1994, pp. 109-120.
12. E.K.Burke, J.P. Newall, R.F. Weare, A simple heuristically guided search for the timetable problem, In: proceedings of the International ICSC Symposium on Engineering of Intelligent System (EIS'98), Canada/Switzerland, 1998, pp. 574-579.
13. H. Asmuni, E.K. Burke, J. Garibaldi, B. Mc Collum, Fuzzy multiple ordering criteria for examination timetabling, In: Burke, E. K., Trick, M., eds: The practice and theory of automated timetabling (PATAT), Springer, Berlin, Vol.3616, 2005, pp. 334-353.
14. E.K. Burke, J.P. Newall, Solving examination timetabling problems through adaption of heuristic orderings, Ann. Oper. Res. Vol.129, 2004, pp. 107-134.
15. E.K. Burke, B. Mc Collum, A. Meisels, S. Petrovic, R. Qu, A graph-based hyper-heuristic for educational timetabling problem, Eur. J. Oper. Res, Vol. 176, 2007, pp. 177-192.
16. N. Pillay, W. Banzhaf, G.V. Berghe, A study of heuristic combinations for hyper-heuristic systems for the uncapacitated examination timetabling problem, Eur. J. Oper. Res, Vol.197, 2009, pp. 482-491.
17. A. Tripathy, School timetabling-a case in large binary integer linear programming, Manage. Sci, Vol.30, 1984, pp. 1473-1489.
18. E.K. Burke, D.G. Elliman, R.F. Weare, A genetic algorithm for university timetabling In: proceedings of the Artificial Intelligence and Simulation of Behaviour (AISB), University of Leeds, Pittsburg, UK, 1994, pp. 334-353.
19. A. Ergül, GA-based examination scheduling experience at Middle East Technical University, In: Burke, E. K., Ross, P., eds.: The Practice and Theory of Automated Timetabling (PATAT) I, Springer, Berlin, Vol. 1153, 1996, pp. 212-226.
20. N. Pillay, W. Banzhaf, An informed genetic algorithm for the examination timetabling problem, Appl. Soft. Comput, Vol.10, 2010, pp. 457-467.
21. A. Hertz, Finding a Feasible Course Schedule Using Tabu Search, Discrete Appl Math, Vol.35, 1992, pp. 255-270.
22. G.M. White, B.S. Xie, S. Zonjic, Using tabu search with longer-term memory and relaxation to create examination timetables, Eur. J. Oper. Res., Vol.153, 2004, pp. 80-91.
23. C.H. Aladag, G. Hocaoglu, MA. Basaran, The effect of neighborhood structures on tabu search algorithm in solving course timetabling problem, Expert. Syst. Appl. Vol.36, 2009, pp. 12349-12356.
24. J.M. Thompson., KA. Dowsland, Variants of simulated annealing for the examination timetabling problem, Ann. Oper. Res, Vol.63, 1996, pp. 105-128.
25. J.M. Thompson, K.A. Dowsland, A robust simulated annealing based examination timetabling system, Comput. Oper. Res, Vol.25, 1996, pp. 637-648.
26. D. Abramson, M. Krishnamoorthy, H. Dang, Simulated annealing cooling schedules for the school timetabling problem, Asia. Pacc. J. Oper. Res, Vol.16, 1999, pp. 1-22.
27. J. Frausto-Solís, F. Alonso-Pecina, J. Mora-Vargas, An Efficient Simulated Annealing Algorithm for Feasible Solutions of Course Timetabling, In: Gelbukh, A., Morales, E. F., eds.: MICAI 2008, Springer, Berlin, Vol.5317, 2008, pp. 675-685.
28. D. Zhang, L. Yongkai, R. M'Hallah, S.C.H. Leung, A simulated annealing with a new neighborhood structure based algorithm for high school timetabling problems, Eur. J. Oper. Res, Vol.203, 2010, pp. 550-558.
29. K. Socha, J. Knowles, M. Samples, A max-min ant system for the university course timetabling problem, In: proceedings of the 3rd international workshop on ant algorithms (ANTS 2002), Springer, Vol.2463, 2002, pp. 1-13.
30. K. Socha, M. Samples,. M. Manfrin, Ant algorithm for the university course timetabling problem with regard to the state-of-the art, In: proceedings of the 3 European workshop on evolutionary computation in combinatorial optimisation, Essex, UK, Springer, Vol.2611, 2003, pp. 334-345.
31. N. Ejaz, M. Javed, An Approach for Course Scheduling Inspired by Die-Hard Co-Operative Ant Behavior, In: proceedings of the IEEE international conference on automation and logistics, Jinan, china, 2007, pp. 3095-3100.
32. O. Rossi-Doria, M. Sampels, M. Birattari, M. Chiarandini, M. Dorigo, L. Gambardella, J. Knowles, M. Manfrin, M. Mastrolilli, B. Paechter, L. Paquete, T. Sẗutzle, A comparison of the performance of different metaheuristics on the timetabling problem, In: Proc. 4th Int. Conf. Pract. Theory Automated Timetabling, Lecture Notes in Computer Science, Vol.2740, 2003, pp. 329-351.
33. P. Pongcharoena, W. Promtetb, P. Yenradeec, C. Hicks, Stochastic Optimisation Timetabling Tool for university course scheduling, Int. J. Prod. Econ., Vol.112, 2008, pp. 903-918.
34. D. Landa-Silva, J.H. Obit, Great deluge with non-linear decay rate for solving course timetabling problems, In: Proc. 4th IEEE Int. Conf. Intell. Syst., 2008, pp. 811-818.
35. R. Lewis, A survey of metaheuristic-based techniques for university timetabling problems, OR. Spectrum., Vol.30, 2008, pp. 167-190.
36. S.C. Kong, L.F. Kwok., A conceptual model of knowledge-based timetabling system, Knowl-Based. Syst., Vol.12, 1999, pp. 81-93.
37. L.R. Foulds, D.G. Johinson, Slot Manager: a microcomputer-based decision support system for university timetabling, Decis. Support. Syst., Vol.27, 2000, pp. 307-381.
38. J.L. Kolodner, Case-based reasoning. Morgan Kaufman Publishers, Inc. San Mateo 1993.
39. E.K. Burke, B. Mac Carthy, S. Petrovic, R. Qu, Structured Cases in CBR-Re-using and Adapting Cases for Timetabling Problems, Knowl-Based. Syst. Vol.13, 2000, pp. 159-165.
40. E.K. Burke, S. Petrovic, R. Qu, Case based heuristic selection for timetabling problems, J. Scheduling., Vol.9, 2006, pp. 115-132.
41. E.K. Burke, E. Hart, G Kendall, J.P. Newall, P. Ross, S. Schulenburg, Hyper-heuristics: An emerging direction in modern search technology, In: Glover, F., Kochenberger, G., eds.: Chapter 16 in Handbook of Meta-Heuristics, Kluwer, 2003, pp. 457-474.
42. R. Qu, E.K. Burke, Hybridisations within a Graph Based Hyperheuristic Framework for University Timetabling Problems, Technical Report NOTTCS-TR-2006-1, School of CSi T, University of Nottingham, UK, 2006.
43. H. Asmuni, E.K. Burke, J.M. Garibaldi, Fuzzy multiple heuristic ordering for course timetabling, In: proceedings of the 5th united kingdom workshop on computational intelligence (UKCI05), London, UK, 2003, pp. 302-309.
44. th International Conference on Recent Advances in Soft Computing, Nottingham, UK, 2004, pp. 288-293.
45. S. Petrovic, V. Pate, Y. Yang, University timetabling with fuzzy constraints, In: Burke, E. K., Trick, M., eds.: The Practice and Theory of Automated Timetabling I: Selected Papers from 5th International Conference on the Practice and Theory of Automated Timetabling, Lecture Notes in Computer Science, Springer-Verlag, Pittsburg, USA, Vol.3616, 2005, pp. 313-333.
46. A. Chaudhuri, K. De, Fuzzy genetic heuristic for university course timetable problem, Intern. J. Adv. Soft. Comput. Appl., Vol.2, 2010, pp. 100-123.
47. K.A. Smith, D. Abramson, D. Duke, Hopfield neural networks for timetabling: formulations, methods, and comparative results, Comput. Ind. Eng., Vol.44, 2003, pp. 283-305.
48. M.P. Carrasco, M.V. Pato, A comparison of discrete and continuous neural network approaches to solve the class/teacher timetabling problem, Eur. J. Oper. Res., Vol.153, 2004, pp. 65-79.
49. S. Daskalaki, T. Birbas, Efficient solutions for a university timetabling problem through integer programming, Eur. J. Oper. Res., Vol.160, 2005, pp. 106-120.
50. K. Schimmelpfeng, S. Helber, Application of a real-world university-course timetabling model solved by integer programming, OR. Spectrum, Vol.29, 2007, pp. 783-803.
51. S. Abdullah., E.K. Burke, B. Mc Collum, An investigation of a variable neighbourhood search approach for course timetabling, In: proceedings of the 2nd multidisciplinary international conference on scheduling: theory and applications (MISTA 2005), New York, USA, 2005, pp. 413-427.
52. S. Abdullah, E.K. Burke, B. Mc Collum, Hybrid evolutionary approach to the university course timetabling problem. In proceedings of the IEEE congress on evolutionary computation (CEC 2007), 2007, pp. 1764-1768.
53. A. Schaerf, A survey of automated timetabling. Artif Intell. Rev., Vol.13, 1999, pp. 87-127.
54. R. Qu, E.K. Burke, B. Mc Collum, L.T.G. Merlot, S.Y. Lee, A survey of search methodologies and automated approaches for examination timetabling, Computer Science Technical Report No. NOTTCS-TR-2006-4, School of Computer Science and Information Technology, University of Nottingham, UK, 2006.
55. S. Petrovic, E.K. Burke, University timetabling, In: Leung J ed.: Chapter 45 in Handbook of Scheduling: Algorithms, Models, and Performance Analysis, CRC Press, 2004.
56. E.K. Burke, S. Petrovic, Recent research directions in automated timetabling, Eur. J. Oper. Res., Vol.140, 2002, pp. 266-280.
57. S.C. Chu, H.L. Fang, Genetic algorithms vs tabu search in timetable scheduling, In: proceedings of the 3rd international conference on knowledge-based intelligent information engineering system, Australia, Adelaide, 1999, pp. 492-495.
58. P. Ross, E. Hart, D. Corne, Some observations about GA based timetabling, In: Burke, E. K., Carter M eds.: The Practice and Theory of Automated Timetabling (PATAT) II, Springer, Berlin, Vol.1408, 1998, pp. 115-129.
59. E.K. Burke, J.P. Newall, R.F. Weare, A memetic algorithm for university exam timetabling, In: Burke, E. K., Ross, P. eds.: Practice and theory of automated timetabling (PATAT) I, Springer, Berlin, Vol.1153, 1996, pp. 241-250.
60. L.T.G. Merlot, N. Boland, B.D. Hughes, O.J. Stuckey, A hybrid algorithm for the examination timetabling problem, In: Burke, E. K., De Causmaecker P eds.: Practice and theory of automated timetabling (PATAT) IV, Springer, Berlin, Vol.2740, 2003, pp. 207-231.
61. Z.N. Azimi, Hybrid heuristics for examination timetabling problem, Appl. Math. Comput., Vol.163, 2005, pp. 705-733.
62. M. Chiarandini, M. Birattari, K. Socha, O. Rossi-Doria, An effective hybrid algorithm for university course timetabling, J. Scheduling., Vol.9, 2006, pp. 403-432.
63. S. Yang, S.N. Jat, Genetic algorithms with guided and local search strategies for university course timetabling, IEEE. T. Sys. Man. CY. C, Vol.41, 2011, pp. 93-106.
64. S. Abdullah, H. Turabieh, Generating university course timetable using genetic algorithm and local search, In: Proc 3rd Int Conf Hybrid Inform Tech, 2008, pp. 254-260.
65. S. Abdullah, E.K. Burke, B. Mc Collum, H. Turabieh, A hybrid evolutionary approach to the university course timetabling problem, In Proc 2007 Congr Evoll Comput, 2007, pp. 1764-1768.
66. S.N. Jat, S. Yang, A Memetic algorithm for the university course timetabling problem, In: 2008 20th IEEE International Conference on Tools with Artificial Intelligence DOI 10.1109/ICTAI.2008.126, 2008.
67. E.K. Burke, D.G. Elliman, R.F. Weare, A university timetabling system based on graph colouring and constraint manipulation, J. Res. Comput. Educ., 27, pp. 1-18, 1994.
68. Burke, E. K., Kingston, J., De Werra, D., Applications to timetabling, In Gross J, Yellen J eds.: Handbook of Graph, Chapman Hall/CRC Press, Vol.24, 2004, pp. 445-474.
69. R.L. Haupt, S.E. Haupt, Practical genetic algorithms. John Wiley & Sons, 2004, pp. 38-41.
70. P. Ross, D. Corne, H.L. Fang, Improving evolutionary timetabling with delta evolution and directed mutation, In: proceedings of the Conference on parallel problem solving from nature III, Springer, Berlin, Vol.866, 1994, pp. 556-565.
71. S. Abdullah, E.K. Burke, B. Mc Collum, Using a randomised iterative improvement algorithm with composite neighbourhood structures for the university course timetabling problem, In: Doerner, F. K., Gendreau, M., Greistorfer, P., Gutjahr, W. J., Hartl, R. F., and Reimann, M., eds. Metaheuristics-Progress in Complex Systems Optimization, Computer Science Interfaces Book Series, Springer Operations Research, ISBN-13, pp. 978-0-387-71919-1, Vol.39, 2007, pp. 153-169.
72. D. Corne, H.L. Fang, C. Mellish, Solving the modular exam scheduling problem with genetic algorithms, In: proceedings of the 6th international conference of industrial and engineering applications of AI and expert system, 1993, pp. 370-373.
73. V. Bhatt, R. Sahajpal, Lecture timetabling using hybrid genetic algorithms, In: proceedings of 2004 international conference on intelligent sensing and information processing, 2004, pp. 29-34.
74. Q.K. Pan, M.F. Tasgetiren, Y.C. Liang, A discrete particle swarm optimization algorithm for the no-wait flowshop scheduling problem, Comput. Oper. Res., Vol.35, 2008, pp. 2807-2839.
75. E.K. Burke, G. Kendall, E. Soubeiga, A tabu-search hyper-heuristic for timetabling and rostering, J. Heuristics., Vol.9, 2003, pp. 451-470.
76. H. Asmuni, E.K. Burke., J.M. Garibaldi, A hybrid approach for course scheduling inspired by die-hard co-operative ant behavior, In proceedings of the IEEE international conference on automation and logistics, Jinan, China, 2007, pp. 3095-3100.