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Learning Path Construction Based on Ant Colony Optimization and Genetic Algorithm

In: New Trends in Computational Vision and Bio-inspired Computing

Author

Listed:
  • V. Vanitha

    (Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technoloy)

  • P. Krishnan

    (ICAR—National Academy of Agricultural Research Management (NAARM))

Abstract

Providing personalized learning path according to the individual learning characteristics poses great challenge. In the personalized learning environment, the content and the sequence of contents into path vary with individuals depending on their needs. Researchers have been proposing several variations on swarm intelligence and evolutionary algorithms since last decade. Their intention is to improve the performance of these algorithms on various optimization problems such as travelling salesman problem, scheduling problem. In this paper, Genetic Algorithm (GA) and Ant Colony Optimization Algorithm (ACO) have been combined for constructing personalized learning path. Firstly, experiment was conducted to choose the best possible value for crucial parameters that influence the efficiency of the algorithm. Secondly, proposed method was compared for its execution time and quality of results to state the better performing algorithm. The experiment results indicated that the proposed method performs better produces good quality result with smaller to medium solution space.

Suggested Citation

  • V. Vanitha & P. Krishnan, 2020. "Learning Path Construction Based on Ant Colony Optimization and Genetic Algorithm," Springer Books, in: S. Smys & Abdullah M. Iliyasu & Robert Bestak & Fuqian Shi (ed.), New Trends in Computational Vision and Bio-inspired Computing, pages 689-699, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-41862-5_68
    DOI: 10.1007/978-3-030-41862-5_68
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