用智能算法优化个性化学习路径，提升进度与稳定性。
A Memetic Walrus Algorithm with Expert-guided Strategy for Adaptive Curriculum Sequencing
- 融合专家策略与老化机制，避免陷入局部最优。
- 实现95.3%的学习难度递进率，显著优于基线方法。
- 适合需要自适应课程规划的教育科技应用。
自适应课程序列对个性化在线学习至关重要，但现有方法难以平衡复杂教育约束并保持优化稳定。本文提出一种记忆启发式鲸鱼优化器（MWO），通过三项创新提升优化性能：(1) 带老化机制的专家引导策略，增强跳出局部最优能力；(2) 自适应控制信号框架，动态平衡探索与利用；(3) 三层优先级机制，生成具有教育意义的序列。将ACS建模为多目标优化问题，综合考虑知识点覆盖、时间约束和学习风格匹配。在OULAD数据集上的实验表明，MWO达到95.3%的难度递进率（基线为87.2%），收敛稳定性显著提升（标准差18.02，对比基线28.29–696.97）。基准函数验证也证明其在多种场景下的鲁棒性。结果表明，MWO能高效生成高质量个性化学习序列。
原文摘要 · Abstract (English)
Adaptive Curriculum Sequencing (ACS) is essential for personalized online learning, yet current approaches struggle to balance complex educational constraints and maintain optimization stability. This paper proposes a Memetic Walrus Optimizer (MWO) that enhances optimization performance through three key innovations: (1) an expert-guided strategy with aging mechanism that improves escape from local optima; (2) an adaptive control signal framework that dynamically balances exploration and exploitation; and (3) a three-tier priority mechanism for generating educationally meaningful sequences. We formulate ACS as a multi-objective optimization problem considering concept coverage, time constraints, and learning style compatibility. Experiments on the OULAD dataset demonstrate MWO's superior performance, achieving 95.3% difficulty progression rate (compared to 87.2% in baseline methods) and significantly better convergence stability (standard deviation of 18.02 versus 28.29-696.97 in competing algorithms). Additional validation on benchmark functions confirms MWO's robust optimization capability across diverse scenarios. The results demonstrate MWO's effectiveness in generating personalized learning sequences while maintaining computational efficiency and solution quality.
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