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In this study, we have a research of a hybrid algorithm by combining BP neural network and improved Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to solve the multi-objective optimization problem of a nanoscale coal-fired boiler combustion electronical systems, the two objectives considered are minimization of overall heat loss and NOx emissions from coal-fired boiler. First, Back Propagation (BP) neural network was dopted to establish a mathematical model predicting the NOx emissions and overall heat loss of the coal-fired boiler with the inputs such as operational parameters of the nanoscale coal-fired boiler. Then, BP model and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) were combined to gain the optimal operating parameters which lead to lower NOx emissions and overall heat loss boiler. 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 optimization results show that hybrid algorithm by combining BP neural network and improved NSGA-II can be a good tool to solve the problem of multi-objective optimization of a nanoscale coal-fired boiler combustion electronical systems in green food bases, which can reduce NOx emissions and overall heat loss effectively for the nanoscale coal-fired boiler combustion electronical systems in green food bases. Compared with original NSGA-II, the Pareto set obtained by the improved NSGA-II shows a better distribution and better quality.

Application of Improved NSGA-II to Multi-objective Optimization of a Coal-fired Boiler Combustion Electronical Systems in Green Food Bases