TY - EJOU
AU - Li, Xia
AU - Niu, Peifeng
AU - Liu, Jianping
AU - Liu, Qing
TI - Improved Teaching-Learning-Based Optimization Algorithm for Modeling NO_{X} Emissions of a Boiler
T2 - Computer Modeling in Engineering \& Sciences
PY - 2018
VL - 117
IS - 1
SN - 1526-1506
AB - 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 NO_{X} 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.
KW - Bloch sphere
KW - qubits
KW - self-learning
KW - improved teaching-learning-based optimization (I-TLBO) algorithm
DO - 10.31614/cmes.2018.04020