Fish swarm intelligent algorithm for the course timetabling problem

  • Authors:

  • Hamza Turabieh;Salwani Abdullah;Barry McCollum;Paul McMullan

  • Affiliations:

  • Data Mining and Optimization Research Group, Center for Artificial Intelligence Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia;Data Mining and Optimization Research Group, Center for Artificial Intelligence Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia;Department of Computer Science, Queen's University Belfast, Belfast, United Kingdom;Department of Computer Science, Queen's University Belfast, Belfast, United Kingdom

  • Venue:
  • RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
  • Year:
  • 2010

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Abstract

In this work, a simulation of fish swarm intelligence has been applied on the course timetabling problem. The proposed algorithm simulates the movements of the fish when searching for food inside a body of water (refer as a search space). The search space is classified based on the visual scope of fishes into three categories which are crowded, not crowded and empty areas. Each fish represents a solution in the solution population. The movement direction of solutions is determined based on a Nelder-Mead simplex algorithm. Two types of local search i.e. a multi decay rate great deluge (where the decay rate is intelligently controlled by the movement direction) and a steepest descent algorithm have been applied to enhance the quality of the solution. The performance of the proposed approach has been tested on a standard course timetabling problem. Computational experiments indicate that our approach produces best known results on a number of these benchmark problems.