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Improved Teaching-Learning-Based Optimization Algorithm for Modeling NOX Emissions of a Boiler

Xia Li1,2, Peifeng Niu1,*, Jianping Liu2, Qing Liu2
School of Electrical Engineering, Yanshan University, Qinhuangdao, 066004, China.
School of Mathematics and Information Science &Technology, Hebei Normal University of Science and Technology, Qinhuangdao, 066004, China.
*Corresponding Author: Peifeng Niu. Email: .

Computer Modeling in Engineering & Sciences 2018, 117(1), 29-57. https://doi.org/10.31614/cmes.2018.04020

Abstract

An improved teaching-learning-based optimization (I-TLBO) algorithm is proposed to adjust the parameters of extreme learning machine with parallel layer perception (PELM), and a well-generalized I-TLBO-PELM model is obtained to build the model of NOX emissions of a boiler. In the I-TLBO algorithm, there are four major highlights. Firstly, a quantum initialized population by using the qubits on Bloch sphere replaces a randomly initialized population. Secondly, two kinds of angles in Bloch sphere are generated by using cube chaos mapping. Thirdly, an adaptive control parameter is added into the teacher phase to speed up the convergent speed. And then, according to actual teaching-learning phenomenon of a classroom, students learn some knowledge not only by their teacher and classmates, but also by themselves. Therefore, a self-study strategy by using Gauss mutation is introduced after the learning phase to improve the exploration ability. Finally, we test the performance of the I-TLBO-PELM model. The experiment results show that the proposed model has better regression precision and generalization ability than eight other models.

Keywords

Bloch sphere, qubits, self-learning, improved teaching-learning-based optimization (I-TLBO) algorithm.

Cite This Article

Li, X., Niu, P., Liu, J., Liu, Q. (2018). Improved Teaching-Learning-Based Optimization Algorithm for Modeling NOX Emissions of a Boiler. CMES-Computer Modeling in Engineering & Sciences, 117(1), 29–57.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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