Self-adaptive multi-objective teaching-learning-based optimization and its application in ethylene cracking furnace operation optimization

A self-adaptive multi-objective teaching-learning-based optimization (SA-MTLBO) is proposed in this paper. In SA-MTLBO, the learners can self-adaptively select the modes of learning according to their levels of knowledge in classroom. The excellent learners are more likely to choose the learner phase to enhance population diversity, and the common learners are tend to choose the teacher phase to improve the convergence ability of the algorithm. So learners at different levels choose appropriate modes of learning and carry out corresponding search function to efficiently enhance the performance of algorithm. To evaluate the effectiveness of the proposed algorithm, SA-MTLBO is firstly compared with other algorithms in twelve test problems. The results demonstrate that SA-MTLBO can generate Pareto optimal fronts with good convergence and distribution. Finally, SA-MTLBO is used to maximize the yields of ethylene, propylene, and butadiene of the naphtha pyrolysis process. The computational results of SA-MTLBO indicate that the operation of ethylene cracking furnace can be improved by increasing the yields of ethylene, propylene, and butadiene.