The Application of Improved Grasshopper Optimization Algorithm to Flight Delay Prediction-Based on Spark

Flight delay prediction can improve the quality of airline services, help air traffic control agencies to develop more accurate flight plans. This paper proposes a distributed and improved grasshopper optimization algorithm based on Spark to optimize the classification model of random forest parameters (SPGOA-RF) for flight delay prediction. The SPGOA-RF uses the method of adaptive chaotic descent which based on Logistic mapping and Sigmoid curve to enhance the randomness of the grasshopper optimization algorithm, thereby improve the early exploration and later optimization capabilities of the algorithm and accelerate the speed of convergence. The improved grasshopper optimization algorithm is used to adjust the random forest parameters to obtain a better performance classification model. In addition, the Spark platform is used to implement a distributed grasshopper optimization algorithm training model to effectively improve its operating efficiency. The results of simulation experiment prove that in comparison to the unoptimized algorithm, the SPGOA-RF flight delay prediction accuracy rate could achieve to 89.17%.