关键词: TLBO算法, 函数优化, 乙烯裂解炉

Abstract: 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.

Key words: teaching-learning-based optimization algorithm, function optimization, ethylene cracking furnace