Artificial Bee Colony Algorithm for Optimized Management of Students' Schedule in Academe

Author(s)	Ms. Kaye Alex M. Pascual, Ms. Jessalyn R. Burce, Prof. Raymund M. Dioses, Dr. Khatalyn E. Mata
Country	Philippines
Abstract	The Artificial Bee Colony (ABC) algorithm is a population-based metaheuristic inspired by the foraging behavior of honeybees. It has been widely used for solving complex optimization problems due to its simplicity and robustness. However, traditional implementations of ABC often face challenges such as slow convergence, premature stagnation, and reduced efficiency in high-dimensional search spaces. To overcome these issues, three enhancements were applied. First, Adaptive Neighborhood Search (ANS) dynamically adjusted neighborhood size based on solution fitness, improving exploration and reducing the best fitness from 629.02 to 323.17. Second, Latin Hypercube Sampling (LHS) ensured a diverse initial population, boosting convergence speed and accuracy—achieving a best fitness of 1.25e-6 in 1.28 seconds versus 3.65 seconds for the standard ABC. Third, Principal Component Analysis (PCA) reduced dimensionality, enabling efficient optimization and smoother convergence near the global optimum, where standard ABC stagnated. Applied to academic scheduling, the enhanced ABC managed constraints effectively, delivering faster, higher-quality solutions suitable for real-world problems.
Field	Computer
Published In	Volume 7, Issue 3, May-June 2025
Published On	2025-05-11
DOI	https://doi.org/10.36948/ijfmr.2025.v07i03.44382

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