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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
College of Management, Shenzhen University, Shenzhen, 518060, China
Ben Niu, Yuda Wang & Jia Liu
Greater Bay Area International Institute for Innovation, Shenzhen University, Shenzhen, 518060, China
Yuda Wang
School of Computing Science, University of Glasgow, Glasgow, G12 8QN, UK
Qianying Liu
- Ben Niu
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- Yuda Wang
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- Jia Liu
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Correspondence toQianying Liu.
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School of Computing and Informatics, University of Louisiana at Lafayette, Lafayette, IN, USA
Yuan Xu
Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, China
Hongyang Yan
Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, China
Huang Teng
Guangdong Polytechnic Normal University, Guangzhou, China
Jun Cai
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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