Abstract:

Elitist teaching-learning-based optimization (ETLBO) algorithm is inspired by practical teaching-learning process. A novel group search optimizer, modified elitist teaching-learning-based optimization (mETLBO), was proposed to improve low precision and poor stability of the ETLBO. First, an autonomous learning process was introduced to strengthen local search of high quality solution so as to improve algorithm's elite-searching speed. Second, differentiated support and self-adaptive strategy providing appropriate and flexible learning approach to students at various levels, were applied to offer desirable assistance and balance searching rate and accuracy of the algorithm. Third, global searching ability of the algorithm was enhanced by increasing communication frequency between students. Optimization results on standardized functions show that the proposed algorithm is obviously superior to the original one in performance and efficiency. Finally, satisfactory results were achieved by applying the improved algorithm to process optimization with mechanism model of methanol synthesis.

Key words: algorithm, methanol synthesis, ETLBO, model, optimization

摘要：

为了提高精英教学算法（ETLBO）的寻优能力，特别是精度差、寻优速度慢的问题，提出改进的精英教学算法。首先，通过自主学习过程，加强对优质解所在区域的局部勘探，提高算法的寻优效率。其次，引入“差异化帮扶”思想及自适应机制，对不同水平的学生施予适宜的、灵活的学习方式，有针对性的帮助，平衡了算法的勘探速度、精度。通过增加学生间的交流次数，提高了算法的全局勘探能力。标准函数优化结果表明，改进后的算法在寻优能力和勘探效率两方面都有明显提高。最后，建立甲醇合成的机理模型，将改进后的算法应用于甲醇合成过程的优化，取得了良好的效果。

关键词: 算法, 甲醇合成, 精英教学算法, 模型, 优化