Digital Signal Processing and Artificial Intelligence for Automatic Learning Min Zhang,Xiaoliang Sun 4 4 2025 https://doi.org/10.6025/dspaial/2025/4/4/162-169 https://www.dline.info/dspai/fulltext/v4n4/dspaiv4n4_3.pdf 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 MOPSO’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 (ZDT1–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 students’ 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.