Modified Non-Dominated Sorting Genetic Algorithm (MNSGA-II) Applied in Multi-Objective Optimization of a Coal-Fired Boiler Combustion

Abstract

This paper discussed application of modified non-dominated sorting genetic algorithm-II (MNSGA-II) to multi-objective optimization of a coal-fired boiler combustion, the two objectives considered are minimization of overall heat loss and NOx emissions from coal-fired boiler. In the first step, BP neural network was proposed to establish a mathematical model predicting the NOx emissions & overall heat loss from the boiler. Then, BP model and the non-dominated sorting genetic algorithm II (NSGA-II) were combined to gain the optimal operating parameters. According to the problems such as premature convergence and uneven distribution of Pareto solutions exist in the application of NSGA-II, corresponding improvements in the crowded-comparison operator and crossover operator were performed. The optimal results show that MNSGA-II can be a good tool to solve the problem of multi-objective optimization of a coal-fired combustion, which can reduce NOx emissions and overall heat loss effectively for the coal-fired boiler. Compared with NSGA-II, the Pareto set obtained by the MNSGA-II shows a better distribution and better quality.