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This study applies the ant colony algorithm, an evolutionary method, to optimize course scheduling. It transforms the problem into a bipartite graph matching, aiming for high-quality, needs-met curricula.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Operations Research

Background:

  • The ant colony algorithm is a robust evolutionary computation technique.
  • Its applications are expanding beyond the Traveling Salesperson Problem (TSP).
  • The algorithm utilizes positive and negative feedback for optimization.

Purpose of the Study:

  • To adapt the ant colony algorithm for effective course scheduling.
  • To analyze the core principles of the ant colony algorithm for this application.

Main Methods:

  • The course scheduling problem is modeled as a maximum bipartite matching problem.
  • The ant colony algorithm is employed to solve this matching problem.
  • This approach aims to generate optimal course arrangements.

Main Results:

  • The ant colony algorithm successfully addresses the course scheduling problem.
  • The method yields high-quality course schedules that meet essential requirements.
  • The transformation to bipartite matching proves effective for curriculum optimization.

Conclusions:

  • The ant colony algorithm is a viable and effective tool for complex scheduling tasks.
  • This research demonstrates a novel application of evolutionary algorithms in educational administration.
  • The proposed method offers a pathway to improved course arrangement efficiency.