The invention discloses a
blast furnace ironmaking multi-objective intelligent optimization method based on an adaptive
genetic algorithm. According to the self-adaptive
genetic algorithm, the
population fitness skewness coefficient is continuously calculated in the
iteration process. The
population scale is automatically updated according to the change trend of the
population fitness skewness coefficient so as to obtain the optimal search performance. The method is applied to
blast furnace ironmaking process index multi-objective optimization. Aiming at different crude fuel qualities, production conditions and
market conditions, a factory has different requirements on various indexes of the
blast furnace. The
fitness function of the
genetic algorithm is solved by setting the weight of each index through the furnace length. The
population size is automatically updated according to the positive and negative change trend of the
fitness function in the evolution process so as to ensure that the
algorithm has the optimal optimization performance. By applying the self-adaptive genetic
algorithm to the ironmaking process, the problem of multi-target optimization of mutual
coupling of blast furnaces can be effectively solved. Compared with a traditional optimization
algorithm, the method has the advantages that local extremum can be effectively avoided, and a globally optimal solutioncan be efficiently and accurately solved.