Model NOx emissions by least squares support vector machine with tuning based on ameliorated teaching–learning-based optimization

Abstract

The teaching–learning-based optimization (TLBO) is a new efficient optimization algorithm. To improve the solution quality and to quicken the convergence speed and running time of TLBO, this paper proposes an ameliorated TLBO called A-TLBO and test it by classical numerical function optimizations. Compared with other several optimization methods, A-TLBO shows better search performance. In addition, the A-TLBO is adopted to adjust the hyper-parameters of least squares support vector machine (LS-SVM) in order to build NOx emissions model of a 330MW coal-fired boiler and obtain a well-generalized model. Experimental results show that the tuned LS-SVM model by A-TLBO has well regression precision and generalization ability.