Multi-objective Path Planning for AUVs Based on Improved Whale Optimization Algorithms and Fluid Disturbance Algorithms
To address the challenges of low path planning efficiency for autonomous undersea vehicle(AUV) in multi-target environments, as well as the limitations of the traditional whale optimization algorithm(WOA) in terms of susceptibility to local optima and inadequate adaptability to three-dimensional obs...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Science Press (China)
2025-06-01
|
| Series: | 水下无人系统学报 |
| Subjects: | |
| Online Access: | https://sxwrxtxb.xml-journal.net/cn/article/doi/10.11993/j.issn.2096-3920.2025-0054 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849416638478155776 |
|---|---|
| author | Yuhong MA Wen PANG Daqi ZHU |
| author_facet | Yuhong MA Wen PANG Daqi ZHU |
| author_sort | Yuhong MA |
| collection | DOAJ |
| description | To address the challenges of low path planning efficiency for autonomous undersea vehicle(AUV) in multi-target environments, as well as the limitations of the traditional whale optimization algorithm(WOA) in terms of susceptibility to local optima and inadequate adaptability to three-dimensional obstacle avoidance requirements, this study proposed a collaborative planning strategy that integrated a fluid perturbation algorithm with an improved WOA. A hybrid population initialization method was developed by combining chaotic mapping to generate high-coverage initial solutions and a greedy algorithm to construct locally optimal sequences, effectively addressing the issue of poor solution quality caused by random initialization in traditional WOA. For the discrete characteristics of the traveling salesman problem(TSP), a discrete position update strategy based on random insertion and local inversion was proposed, significantly enhancing the algorithm’s capability to escape from local optima. An elite retention mechanism was introduced to ensure the global convergence of the algorithm through an iterative optimization framework that replaced the worst individuals with the optimal ones. During the path generation phase, a three-dimensional fluid disturbance field model was established, where obstacle perturbation matrices adjusted the original flow field direction to achieve continuous obstacle avoidance in complex obstacle environments. Simulation results demonstrate that the proposed algorithm reduces the average path length by 15.4% and 7.5% compared to traditional genetic algorithm and particle swarm optimization, respectively, while improving computational efficiency by 45.5% and 16.8%. |
| format | Article |
| id | doaj-art-47e744deadab4c6fbdfbee92c8bbeb46 |
| institution | Kabale University |
| issn | 2096-3920 |
| language | zho |
| publishDate | 2025-06-01 |
| publisher | Science Press (China) |
| record_format | Article |
| series | 水下无人系统学报 |
| spelling | doaj-art-47e744deadab4c6fbdfbee92c8bbeb462025-08-20T03:33:07ZzhoScience Press (China)水下无人系统学报2096-39202025-06-0133341041910.11993/j.issn.2096-3920.2025-00542025-0054Multi-objective Path Planning for AUVs Based on Improved Whale Optimization Algorithms and Fluid Disturbance AlgorithmsYuhong MA0Wen PANG1Daqi ZHU2School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, ChinaSchool of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, ChinaSchool of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, ChinaTo address the challenges of low path planning efficiency for autonomous undersea vehicle(AUV) in multi-target environments, as well as the limitations of the traditional whale optimization algorithm(WOA) in terms of susceptibility to local optima and inadequate adaptability to three-dimensional obstacle avoidance requirements, this study proposed a collaborative planning strategy that integrated a fluid perturbation algorithm with an improved WOA. A hybrid population initialization method was developed by combining chaotic mapping to generate high-coverage initial solutions and a greedy algorithm to construct locally optimal sequences, effectively addressing the issue of poor solution quality caused by random initialization in traditional WOA. For the discrete characteristics of the traveling salesman problem(TSP), a discrete position update strategy based on random insertion and local inversion was proposed, significantly enhancing the algorithm’s capability to escape from local optima. An elite retention mechanism was introduced to ensure the global convergence of the algorithm through an iterative optimization framework that replaced the worst individuals with the optimal ones. During the path generation phase, a three-dimensional fluid disturbance field model was established, where obstacle perturbation matrices adjusted the original flow field direction to achieve continuous obstacle avoidance in complex obstacle environments. Simulation results demonstrate that the proposed algorithm reduces the average path length by 15.4% and 7.5% compared to traditional genetic algorithm and particle swarm optimization, respectively, while improving computational efficiency by 45.5% and 16.8%.https://sxwrxtxb.xml-journal.net/cn/article/doi/10.11993/j.issn.2096-3920.2025-0054autonomous undersea vehiclemulti-targetpath planningwhale optimization algorithmtravelling salesman problemfluid disturbance algorithm |
| spellingShingle | Yuhong MA Wen PANG Daqi ZHU Multi-objective Path Planning for AUVs Based on Improved Whale Optimization Algorithms and Fluid Disturbance Algorithms 水下无人系统学报 autonomous undersea vehicle multi-target path planning whale optimization algorithm travelling salesman problem fluid disturbance algorithm |
| title | Multi-objective Path Planning for AUVs Based on Improved Whale Optimization Algorithms and Fluid Disturbance Algorithms |
| title_full | Multi-objective Path Planning for AUVs Based on Improved Whale Optimization Algorithms and Fluid Disturbance Algorithms |
| title_fullStr | Multi-objective Path Planning for AUVs Based on Improved Whale Optimization Algorithms and Fluid Disturbance Algorithms |
| title_full_unstemmed | Multi-objective Path Planning for AUVs Based on Improved Whale Optimization Algorithms and Fluid Disturbance Algorithms |
| title_short | Multi-objective Path Planning for AUVs Based on Improved Whale Optimization Algorithms and Fluid Disturbance Algorithms |
| title_sort | multi objective path planning for auvs based on improved whale optimization algorithms and fluid disturbance algorithms |
| topic | autonomous undersea vehicle multi-target path planning whale optimization algorithm travelling salesman problem fluid disturbance algorithm |
| url | https://sxwrxtxb.xml-journal.net/cn/article/doi/10.11993/j.issn.2096-3920.2025-0054 |
| work_keys_str_mv | AT yuhongma multiobjectivepathplanningforauvsbasedonimprovedwhaleoptimizationalgorithmsandfluiddisturbancealgorithms AT wenpang multiobjectivepathplanningforauvsbasedonimprovedwhaleoptimizationalgorithmsandfluiddisturbancealgorithms AT daqizhu multiobjectivepathplanningforauvsbasedonimprovedwhaleoptimizationalgorithmsandfluiddisturbancealgorithms |