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Citation:
　Jiehui Tan,Yushan Sun,Kaiqian Cai,et al.Energy-Efficient AUV Path Planning Under Multiple Constraints Using a Synergistic Differential Evolution Algorithm[J].Journal of Marine Science and Application,2026,(5):1471-1489.[doi:10.1007/s11804-025-00789-z]
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Energy-Efficient AUV Path Planning Under Multiple Constraints Using a Synergistic Differential Evolution Algorithm

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Title:
Energy-Efficient AUV Path Planning Under Multiple Constraints Using a Synergistic Differential Evolution Algorithm
Author(s):
Jiehui Tan1;  Yushan Sun1;  Kaiqian Cai1; 2;  Yinghao Zhang3;  Liwen Zhang1
Affilations:
Author(s):
Jiehui Tan1;  Yushan Sun1;  Kaiqian Cai1; 2;  Yinghao Zhang3;  Liwen Zhang1
1 National Key Laboratory of Autonomous Marine Vehicle Technology, Harbin Engineering University, Harbin 150001, China;
2 Nanhai Institute of Harbin Engineering University, Sanya 572025, China;
3 Wuhan Second Ship Design and Research Institute, Wuhan 430200, China
Keywords:
Autonomous underwater vehicle; Path planning; Energy optimization; Differential evolution; Swarm intelligence
分类号:
-
DOI:
10.1007/s11804-025-00789-z
Abstract:
Path planning is essential for an autonomous underwater vehicle (AUV) to efficiently perform underwater missions. To address the challenges of energy-efficient three-dimensional path planning under multiple constraints, this study proposes a synergistic differential evolution (SDE) algorithm. First, a comprehensive underwater environment model is developed, including a path encoding model and a deterministic path-evaluation method. Second, a synergistic evolution mechanism is introduced to improve global exploration by adaptively sharing the evolutionary information of two synergistic populations. Third, evolutionary guidance is proposed to refine the search direction by integrating successful population movements based on fitness improvements and positional correlations. Finally, a cosine-based strategy is employed to adjust the population size by eliminating low-quality solutions. Compared to state-of-the-art methods, the proposed algorithm achieves average improvements of 2.37% in best fitness, 15.99% in mean fitness, 13.42% in median fitness, 80.69% in standard deviation, 66.65% in convergence quality, and 39.91% in runtime. In addition, AUV field tests further validate the feasibility and reliability of the SDE-based path-planning approach.

References:

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Memo

Memo:
Received date:2025-4-25;Accepted date:2025-10-13。<br>Corresponding author:Yushan Sun,E-mail:sunyushan@hrbeu.edu.cn
Last Update: 2026-11-04