Multiobjective optimization of coal-fired boiler combustion based on LS-SVM and SPEA2

Research on multiobjective optimization of boiler combustion to lower emission and increase its efficiency is presented.Strength Pareto Evolutionary Algorithm(SPEA2) was employed to solve the multiple and conflicting objectives and perform a search to determine the optimum solution of the Least Square Support Vector Machines model(LS-SVM),which was used to set up a boiler combustion response property model for NO_x emission and efficiency,so as to obtain currently optimum combustion adjustment mode of boilers.Comparison with the artificial neural network model shows the superiority of the proposed LS-SVM approach,and confirms that the Multiobjective Evolutionary Algorithm(MOEA) approach can find multiple Pareto-optimal solutions in one single run and this ability makes it attractive for solving problems of multiobjective optimization for boiler combustion.