A Hybrid Fish Swarm Optimisation Algorithm for Solving Examination Timetabling Problems

A hybrid fish swarm algorithm has been proposed to solve exam timetabling problems where the movement of the fish is simulated when searching for food inside water (refer as a search space). The search space is categorised into three categories which are crowded, not crowded and empty areas. The movement of fish (where the fish represents the solution) is determined based on a Nelder-Mead simplex search algorithm. The quality of the solution is enhanced using a great deluge algorithm or a steepest descent algorithm. The proposed hybrid approach is tested on a set of benchmark examination timetabling problems in comparison with a set of state-of-the-art methods from the literature. The experimental results show that the proposed hybrid approach is able to produce promising results for the test problem.

[1]  Edmund K. Burke,et al.  Hybridizations within a graph-based hyper-heuristic framework for university timetabling problems , 2009, J. Oper. Res. Soc..

[2]  Kathryn A. Dowsland,et al.  Ant colony optimization for the examination scheduling problem , 2005, J. Oper. Res. Soc..

[3]  Ana Maria A. C. Rocha,et al.  Fish swarm intelligent algorithm for bound constrained global optimization , 2009 .

[4]  Nikos E. Mastorakis,et al.  Proceedings of the European Computing Conference , 2009 .

[5]  Michael W. Carter,et al.  OR Practice - A Survey of Practical Applications of Examination Timetabling Algorithms , 1986, Oper. Res..

[6]  Giuseppe Cattaneo,et al.  Algorithm engineering , 1999, CSUR.

[7]  Giuseppe F. Italiano,et al.  New Algorithms for Examination Timetabling , 2000, WAE.

[8]  Nikos E. Mastorakis,et al.  Image Segmentation with Improved Artificial Fish Swarm Algorithm , 2009 .

[9]  G. Dueck.  New optimization heuristics , 1993 .

[10]  Jens Gottlieb,et al.  Evolutionary Computation in Combinatorial Optimization , 2006, Lecture Notes in Computer Science.

[11]  Salwani Abdullah,et al.  Dual Sequence Simulated Annealing with Round-Robin Approach for University Course Timetabling , 2010, EvoCOP.

[12]  Rhyd Lewis,et al.  A survey of metaheuristic-based techniques for University Timetabling problems , 2007, OR Spectr..

[13]  Luca Di Gaspero,et al.  Tabu Search Techniques for Examination Timetabling , 2000, PATAT.

[14]  Min Huang,et al.  An Artificial Fish Swarm Algorithm Based and ABC Supported QoS Unicast Routing Scheme in NGI , 2006, ISPA Workshops.

[15]  Peter J. Stuckey,et al.  A Hybrid Algorithm for the Examination Timetabling Problem , 2002, PATAT.

[16]  Salwani Abdullah,et al.  An integrated hybrid approach to the examination timetabling problem , 2011 .

[17]  Robert Sabourin,et al.  A Hybrid Multi-objective Evolutionary Algorithm for the Uncapacitated Exam Proximity Problem , 2004, PATAT.

[18]  Edmund K. Burke,et al.  Solving Exam Timetabling Problems with the Flex-Deluge Algorithm , 2006 .

[19]  Andrea Schaerf,et al.  A Survey of Automated Timetabling , 1999, Artificial Intelligence Review.

[20]  Yong Wang,et al.  Optimal Multiuser Detection with Artificial Fish Swarm Algorithm , 2007, ICIC.

[21]  Edmund K. Burke,et al.  Adaptive automated construction of hybrid heuristics for exam timetabling and graph colouring problems , 2009, Eur. J. Oper. Res..

[22]  Joseph Y.-T. Leung,et al.  Handbook of Scheduling: Algorithms, Models, and Performance Analysis , 2004 .

[23]  Jian-Wei Ma,et al.  An improved artificial fish-swarm algorithm and its application in feed-forward neural networks , 2005, 2005 International Conference on Machine Learning and Cybernetics.

[24]  Gilbert Laporte,et al.  Examination Timetabling: Algorithmic Strategies and Applications , 1994 .

[25]  Jonathan M. Thompson,et al.  GRASPing the Examination Scheduling Problem , 2002, PATAT.

[26]  Norman Sadeh,et al.  MICRO-OPPORTUNISTIC SCHEDULING THE MICRO-BOSS FACTORY SCHEDULER , 1994 .

[27]  Edmund K. Burke,et al.  Practice and Theory of Automated Timetabling VI, 6th International Conference, PATAT 2006, Brno, Czech Republic, August 30 - September 1, 2006, Revised Selected Papers , 2007, PATAT.

[28]  Geyong Min.  Frontiers of High Performance Computing and Networking - ISPA 2006 Workshops, ISPA 2006 International Workshops, FHPCN, XHPC, S-GRACE, GridGIS, HPC-GTP, PDCE, ParDMCom, WOMP, ISDF, and UPWN, Sorrento, Italy, December 4-7, 2006, Proceedings , 2006, ISPA Workshops.

[29]  Edmund K. Burke,et al.  Applications to timetabling , 2004 .

[30]  Edmund K. Burke,et al.  Practice and Theory of Automated Timetabling IV , 2002, Lecture Notes in Computer Science.

[31]  Edmund K. Burke,et al.  A Multi-Start Very Large Neighbourhood Search Approach with Local Search Methods for Examination Timetabling , 2006, ICAPS.

[32]  Sanja Petrovic,et al.  A Novel Similarity Measure for Heuristic Selection in Examination Timetabling , 2004, PATAT.

[33]  Mark S. Fox,et al.  Intelligent Scheduling , 1998 .

[34]  Sanja Petrovic,et al.  Hybrid variable neighbourhood approaches to university exam timetabling , 2010, Eur. J. Oper. Res..

[35]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..

[36]  Barry McCollum,et al.  A Perspective on Bridging the Gap Between Theory and Practice in University Timetabling , 2006, PATAT.

[37]  Edmund K. Burke,et al.  A survey of search methodologies and automated system development for examination timetabling , 2009, J. Sched..

[38]  Edmund K. Burke,et al.  Practice and Theory of Automated Timetabling V, 5th International Conference, PATAT 2004, Pittsburgh, PA, USA, August 18-20, 2004, Revised Selected Papers , 2005, PATAT.

[39]  Norman M. Sadeh,et al.  Why is Scheduling Difficult? A CSP Perspective , 1990, ECAI.