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Multi-objective Hydrologic Cycle Optimization for Integrated Container Terminal Scheduling Problem

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Machine Learning for Cyber Security (ML4CS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13655))

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Abstract

This paper uses the multi-objective rep-guided hydrological cycle optimization (MORHCO) algorithm to solve the Integrated Container Terminal Scheduling (ICTS) Problem. To enhance the global search capability of the algorithm and improve the quality of the Pareto front, MORHCO algorithm employs both elite flow operators and merit-based evaporation as well as precipitation operators to enhance its performance. Two test functions and the ICTS problem are used to validate the performance of the proposed algorithm. The results show that MORHCO algorithm significantly outperforms the original MOHCO algorithm and the four selected algorithms on the test functions as well as the ICTS problem. This is the first time that HCO algorithm has been applied to the solution of the NP-hard problem.

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Acknowledgement

The work described in this paper was supported by The Natural Science Foundation of China (Grant No. 71971143), Natural Science Foundation of Guangdong Province (Grant No. 2020A1515010749), Key Research Foundation of Higher Education of Guangdong Provincial Education Bureau (Grant No. 2019KZDXM030), University Innovation Team Project of Guangdong Province (Grant No. 2021WCXTD002).

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Authors and Affiliations

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

    Ben Niu, Yuda Wang & Jia Liu

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

    Yuda Wang

  3. School of Computing Science, University of Glasgow, Glasgow, G12 8QN, UK

    Qianying Liu

Authors
  1. Ben Niu
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  2. Yuda Wang
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  3. Jia Liu
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  4. Qianying Liu
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Corresponding author

Correspondence to Qianying Liu .

Editor information

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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Niu, B., Wang, Y., Liu, J., Liu, Q. (2023). Multi-objective Hydrologic Cycle Optimization for Integrated Container Terminal Scheduling 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 13655. Springer, Cham. https://doi.org/10.1007/978-3-031-20096-0_27

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  • DOI: https://doi.org/10.1007/978-3-031-20096-0_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20095-3

  • Online ISBN: 978-3-031-20096-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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