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An introduction of preference based stepping ahead firefly algorithm for the uncapacitated examination timetabling.

Ravneil Nand1, Bibhya Sharma1, Kaylash Chaudhary1

  • 1The University of the South Pacific, Suva, Fiji.

Peerj. Computer Science
|September 12, 2022
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Summary

A new firefly algorithm (FA) method optimizes university examination timetabling (UETP). This discrete FA approach uses neighborhood search and a novel stepping ahead mechanism to improve examination scheduling, offering competitive results.

Keywords:
Meta-heuristic algorithmOptimizationPreferenceStepping-aheadSwarm intelligenceUncapicitated exam timetabling problem

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Area of Science:

  • Artificial Intelligence
  • Optimization Algorithms
  • Swarm Intelligence

Background:

  • Intelligent optimization algorithms, particularly swarm-based ones like the firefly algorithm (FA), are gaining attention.
  • The firefly algorithm, originally for continuous domains, has been adapted for discrete applications.
  • Examination timetabling in higher education institutions (HEI) presents a significant discrete optimization challenge.

Purpose of the Study:

  • To introduce a novel methodology based on the firefly algorithm for uncapacitated examination timetabling problems (UETP).
  • To extend the authors' previous work on continuous domains to address the discrete UETP.
  • To apply FA to a problem domain, university examination timetabling, not previously solved by this algorithm.

Main Methods:

  • A discrete firefly algorithm (FA) is employed for initial solution generation.
  • Neighborhood search heuristics are integrated for reordering examinations and time slots.
  • A preference-based stepping ahead mechanism, utilizing previous searches, is introduced for solution improvement.

Main Results:

  • The proposed preference-based stepping ahead FA was tested on 12 UETP instances.
  • Comparative results on the Toronto exam timetabling dataset show competitive performance against existing literature.
  • The findings serve as a proof of concept for the preliminary stage of this research.

Conclusions:

  • The developed preference-based stepping ahead mechanism effectively exploits the solution space for better optimization outcomes.
  • The methodology demonstrates the potential of discrete FA for complex scheduling problems like UETP.
  • Further validation on diverse educational datasets is recommended, and the mechanism may be applicable to other domains like robotics.