FOIPM: Future-Oriented Intelligent Prediction Model Based on the Improvement of BPNN by GA Integrated with ACO

Future-Oriented Intelligent Prediction is proposed, which combines Ant Colony Operators with a strategy for randomly driving the variance factor to improve Genetic Algorithm and optimize the Back Propagation Neural Network. Ant Colony Operators are introduced to optimize the initial population of Genetic Algorithm, which is combined with a randomly driven strategy to avoid premature maturation of the algorithm. The performance of FOIPM after optimizing the parameters by the improved algorithm is significantly improved. The experiment proves that FOIPM performs well in the prediction of air quality index, which can accurately capture the data features, improve the prediction accuracy, and provide powerful support for air quality monitoring, prevention and control.