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A Tabu-Based Multi-objective Particle Swarm Optimization for Irregular Flight Recovery Problem

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Machine Learning for Cyber Security (ML4CS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13657))

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Abstract

Air transportation is eminent for its fast speed and low cargo damage rate among other ways. However, it is greatly limited by emergent factors like bad weather and current COVID-19 epidemic, where irregular flights may occur. Confronted with the negative impact caused by irregular flight, it is vital to rearrange the preceding schedule to reduce the cost. To solve this problem, first, we established a multi-objective model considering cost and crew satisfaction simultaneously. Secondly, due to the complexity of irregular flight recovery problem, we proposed a tabu-based multi-objective particle swarm optimization introducing the idea of tabu search. Thirdly, we devised an encoding scheme focusing on the characteristic of the problem. Finally, we verified the superiority of the tabu-based multi-objective particle swarm optimization through the comparison against MOPSO by the experiment based on real-world data.

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Acknowledgment

The study was supported in part by the Natural Science Foundation of China Grant No. 62103286, No. 71971143, No. 62001302, in part by Social Science Youth Foundation of Ministry of Education of China under Grant 21YJC630181, in part by Guangdong Provincial Philosophy and Social Sciences Planning Project under Grant GD22XGL22, in part by Guangdong Basic and Applied Basic Research Foundation under Grant 2019A1515110401, 2021A1515011348, 2019A1515111205, in part by Guangdong Province Philosophy and Social Science Planning Discipline Co-construction Project under Grant GD22XGL22, in part by Natural Science Foundation of Guangdong Province under Grant 2020A1515010749, 2020A1515010752, in part by Key Research Foundation of Higher Education of Guangdong Provincial Education Bureau under Grant 2019KZDXM030, in part by Natural Science Foundation of Shenzhen under Grant JCYJ20190808145011259, in part by Shenzhen Science and Technology Program under Grant RCBS20200714114920379, in part by Guangdong Province Innovation Team Intelligent Management and Interdisciplinary Innovation under Grant 2021WCXTD002, in part by Special Projects in Key Fields of Ordinary Colleges and Universities in Guangdong Province under Grant 2022ZDZX2054.

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Authors and Affiliations

  1. College of Management, Shenzhen University, Shenzhen, 518060, China

    Tianwei Zhou, Yichen Lai, Xiaojie Huang, Xumin Chen & Huifen Zhong

  2. Great Bay Area International Institute for Innovation, Shenzhen University, Shenzhen, 518060, China

    Tianwei Zhou

  3. Faculty of Business and Administration, University of Macau, Macau, 999078, China

    Huifen Zhong

Authors
  1. Tianwei Zhou
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  2. Yichen Lai
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  3. Xiaojie Huang
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  4. Xumin Chen
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  5. Huifen Zhong
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Corresponding author

Correspondence to Huifen Zhong .

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Editors and Affiliations

  1. School of Computing and Informatics, University of Louisiana at Lafayette, Lafayette, IN, USA

    Yuan Xu

  2. Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, China

    Hongyang Yan

  3. Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, China

    Huang Teng

  4. Guangdong Polytechnic Normal University, Guangzhou, China

    Jun Cai

  5. Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, China

    Jin Li

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Zhou, T., Lai, Y., Huang, X., Chen, X., Zhong, H. (2023). A Tabu-Based Multi-objective Particle Swarm Optimization for Irregular Flight Recovery Problem. In: Xu, Y., Yan, H., Teng, H., Cai, J., Li, J. (eds) Machine Learning for Cyber Security. ML4CS 2022. Lecture Notes in Computer Science, vol 13657. Springer, Cham. https://doi.org/10.1007/978-3-031-20102-8_10

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  • DOI: https://doi.org/10.1007/978-3-031-20102-8_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20101-1

  • Online ISBN: 978-3-031-20102-8

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