Enhancing Multi-Objective Educational Optimization for MOOC Flipped Models
Min Zhang,Xiaoliang Sun
Digital Signal Processing and Artificial Intelligence for Automatic Learning, Vol. 4, No. 4, 2025.
DOI: https://doi.org/10.6025/dspaial/2025/4/4/162-169
Link: https://www.dline.info/dspai/fulltext/v4n4/dspaiv4n4_3.pdf
Abstract:
This paper explores enhancements to the Multi Objective Particle Swarm Optimization (MOPSO) algorithm to
address challenges in educational optimization, particularly in intelligent and online teaching environments.
It highlights the limitations of traditional teaching methods in applied universities and advocates integrating
digital innovations such as MOOCs and flipped classrooms. The study focuses on improving MOPSOâ€™s ability to
handle constrained, unconstrained, and partially constrained multi objective problems by enhancing particle
selection, maintaining solution diversity, and avoiding local optima. The proposed AAD MOPSO algorithm
demonstrates superior performance compared to IMOPSO and PCCSMOPSO, especially in maintaining highquality
external archives and achieving better convergence and distribution on benchmark ZDT test functions
(ZDT1â€“ZDT6). Experimental results show that AAD MOPSO accurately approximates the true Pareto front
with improved uniformity and coverage. The paper concludes that advanced computational intelligence
algorithms can support more effective educational strategies by optimizing complex, multi faceted objectivessuch
as fostering studentsâ€™ moral development, critical thinking, and practical skills aligning with the
needs of digital native learners. Ultimately, the research contributes a robust optimization framework
applicable not only to education but also to other domains requiring efficient, multi objective decision making.