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

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Complex, Intelligent and Software Intensive Systems (CISIS 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 278))

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Abstract

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%.

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Acknowledgments

This research is supported by National Natural Science Foundation of China under grant number 61602162, 61772180.

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Authors and Affiliations

  1. School of Computer Science, Hubei University of Technology, Wuhan, 430068, China

    Hongwei Chen, Shenghong Tu & Hui Xu

Authors
  1. Hongwei Chen
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  2. Shenghong Tu
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  3. Hui Xu
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Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. Department of Information Security Engineering, Soonchunhyang University, Asan, Korea (Republic of)

    Kangbin Yim

  3. Faculty of Bussiness Administration, Rissho University, Tokyo, Japan

    Tomoya Enokido

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Chen, H., Tu, S., Xu, H. (2021). The Application of Improved Grasshopper Optimization Algorithm to Flight Delay Prediction–Based on Spark. In: Barolli, L., Yim, K., Enokido, T. (eds) Complex, Intelligent and Software Intensive Systems. CISIS 2021. Lecture Notes in Networks and Systems, vol 278. Springer, Cham. https://doi.org/10.1007/978-3-030-79725-6_8

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