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A New Algorithm for Gravity Matching Navigation Path Planning: Particle Swarm Optimization and Grey Wolf Optimization
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Weixuan SUN1, 2, Yun XIAO2, 3, *, Jinbai ZHANG4, 5, Kaining CHEN1, 2
Journal of Geodesy and Geodynamics | 2025, 45(9) : 922 - 928, 936
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Journal of Geodesy and Geodynamics | 2025, 45(9): 922-928, 936
A New Algorithm for Gravity Matching Navigation Path Planning: Particle Swarm Optimization and Grey Wolf Optimization
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Weixuan SUN1, 2, Yun XIAO2, 3, *, Jinbai ZHANG4, 5, Kaining CHEN1, 2
Affiliations
  • 1 School of Geological Engineering and Geomatics, Chang'an University, 126 Yanta Road, Xi'an 710054, China
  • 2 State Key Laboratory of Geo-Information Engineering, 1 Mid-Yanta Road, Xi'an 710054, China
  • 3 Xi'an Institute of Surveying and Mapping, 1 Mid-Yanta Road, Xi'an 710054, China
  • 4 Institute of Geophysics, School of Physics, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, China
  • 5 National Precise Gravity Measurement Facility, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, China

    • SUN Weixuan, postgraduate, majors in gravity matching navigation, E-mail:

About Author:

SUN Weixuan, postgraduate, majors in gravity matching navigation, E-mail:

Published: 2025-09-15 doi: 10.14075/j.jgg.2024.10.484
Outline
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The accuracy of gravity matching navigation is influenced by the adaptability of the sailing sea area. To ensure the effectiveness of matching, it is crucial to conduct path planning for the submersible. In response to the issues of low efficiency, susceptibility to local optimal solutions, and unstable performance of existing algorithms, this paper proposes a hybrid algorithm-particle swarm optimization and grey wolf optimization(PSO-GWO) to enhance the path planning efficiency of submersibles. Building on the traditional grey wolf optimizer(GWO), the proposed algorithm incorporates the memory mechanism of the best position information of particle movement from particle swarm optimization(PSO), reconstructing the update functions for velocity and position, thereby improving the optimization capability and convergence speed of the GWO algorithm. Experiments were conducted using PSO-GWO, GWO, and PSO algorithms on a two-dimensional grid map discretized based on adaptability. The results show that compared with the GWO and PSO algorithms, the PSO-GWO algorithm reduces the planned path length by 10.99% and the number of iterations by 82.43%. This algorithm reduces redundant travel and accelerates convergence, significantly improving the underwater navigation efficiency of the submersible.

gravity matching navigation  /  track planning  /  grey wolf optimization(GWO)  /  particle swarm optimization(PSO)  /  multi-attribute decision
孙玮萱, 肖云, 张锦柏, 陈垲宁. 一种重力匹配导航路径规划新算法——粒子群优化灰狼算法. 大地测量与地球动力学, 2025 , 45 (9) : 922 -928, 936 . DOI: 10.14075/j.jgg.2024.10.484
Weixuan SUN, Yun XIAO, Jinbai ZHANG, Kaining CHEN. A New Algorithm for Gravity Matching Navigation Path Planning: Particle Swarm Optimization and Grey Wolf Optimization[J]. Journal of Geodesy and Geodynamics, 2025 , 45 (9) : 922 -928, 936 . DOI: 10.14075/j.jgg.2024.10.484
Funding
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  • National Natural Science Foundation of China(41374083)
Appendix
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Year 2025 volume 45 Issue 9
29
11
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Article Info
doi: 10.14075/j.jgg.2024.10.484
  • Receive Date：2024-10-23
  • Online Date：2026-10-29
  • Published：2025-09-15
Article Data
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History
  • Received：2024-10-23
Funding
National Natural Science Foundation of China(41374083)
Affiliations
    1 School of Geological Engineering and Geomatics, Chang'an University, 126 Yanta Road, Xi'an 710054, China
    2 State Key Laboratory of Geo-Information Engineering, 1 Mid-Yanta Road, Xi'an 710054, China
    3 Xi'an Institute of Surveying and Mapping, 1 Mid-Yanta Road, Xi'an 710054, China
    4 Institute of Geophysics, School of Physics, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, China
    5 National Precise Gravity Measurement Facility, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, China
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https://castjournals.cast.org.cn/joweb/ddcl/EN/10.14075/j.jgg.2024.10.484
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多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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