改进的教学优化算法及其应用

针对教学优化算法（TLBO）收敛速度慢，容易陷入局部最优的问题，本文提出了一种改进的方法。算法的改进主要在两方面：一是对教学因子（TF）进行自适应调整，使TF随算法迭代减小，这样算法在搜索前期采用全局搜索，搜索空间快速收敛于最优解附近，提高搜索速度，搜索后期采用局部精细搜索以获得高精度的解。二是引入信任权重，对学生已获得的知识采取部分信任的策略，避免对已获取知识的过分信任，增加学生个体与教师及学生之间的信息共享，利于算法跳出局部最优。算法在8个标准测试函数上应用，仿真结果表明改进的算法有更快的收敛速度并且能够跳出局部最优。最后将改进的算法应用到乙烯裂解炉裂解运行效益优化中，显著提高了裂解炉的效益。

Study and application of improved teaching-learning-based optimization algorithm

To overcome the slow convergence speed and easy trapping in local optimum of the Teaching-Learning-Based Optimization (TLBO) algorithm，an improved algorithm was proposed. First，adaptive adjustment of teaching factor TF was proposed to make the TF decrease with iterative algorithm，so the algorithm used the global search in the early search stage to make the search space rapid convergence to near optimal solution to increase search speed. In the later stage，fine local search was used to obtain the higher accuracy solution. After that，trust weight was proposed to take a partial trust strategy in the already acquired knowledge by students，avoiding too much trust in the acquired knowledge and increasing the information share between students and teachers and among the students，so that the algorithm jumped out of the local optimal. The simulation results showed that the improved algorithm had faster convergence speed and the ability to jump out of local optimal. Finally the improved algorithm was used to optimize the benefits of ethylene cracking furnace，the benefits was promoted observably.