Multi-Strategy Improved Red-Tailed Hawk Algorithm for Real-Environment Unmanned Aerial Vehicle Path Planning
In recent years, unmanned aerial vehicle (UAV) technology has advanced significantly, enabling its widespread use in critical applications such as surveillance, search and rescue, and environmental monitoring. However, planning reliable, safe, and economical paths for UAVs in real-world environments...
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MDPI AG
2025-01-01
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| Series: | Biomimetics |
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| Online Access: | https://www.mdpi.com/2313-7673/10/1/31 |
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| author | Mingen Wang Panliang Yuan Pengfei Hu Zhengrong Yang Shuai Ke Longliang Huang Pai Zhang |
| author_facet | Mingen Wang Panliang Yuan Pengfei Hu Zhengrong Yang Shuai Ke Longliang Huang Pai Zhang |
| author_sort | Mingen Wang |
| collection | DOAJ |
| description | In recent years, unmanned aerial vehicle (UAV) technology has advanced significantly, enabling its widespread use in critical applications such as surveillance, search and rescue, and environmental monitoring. However, planning reliable, safe, and economical paths for UAVs in real-world environments remains a significant challenge. In this paper, we propose a multi-strategy improved red-tailed hawk (IRTH) algorithm for UAV path planning in real environments. First, we enhance the quality of the initial population in the algorithm by using a stochastic reverse learning strategy based on Bernoulli mapping. Then, the quality of the initial population is further improved through a dynamic position update optimization strategy based on stochastic mean fusion, which enhances the exploration capabilities of the algorithm and helps it explore promising solution spaces more effectively. Additionally, we proposed an optimization method for frontier position updates based on a trust domain, which better balances exploration and exploitation. To evaluate the effectiveness of the proposed algorithm, we compare it with 11 other algorithms using the IEEE CEC2017 test set and perform statistical analysis to assess differences. The experimental results demonstrate that the IRTH algorithm yields competitive performance. Finally, to validate its applicability in real-world scenarios, we apply the IRTH algorithm to the UAV path-planning problem in practical environments, achieving improved results and successfully performing path planning for UAVs. |
| format | Article |
| id | doaj-art-679bafe7fefa4563b97084c2ae25a562 |
| institution | Kabale University |
| issn | 2313-7673 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomimetics |
| spelling | doaj-art-679bafe7fefa4563b97084c2ae25a5622025-01-24T13:24:39ZengMDPI AGBiomimetics2313-76732025-01-011013110.3390/biomimetics10010031Multi-Strategy Improved Red-Tailed Hawk Algorithm for Real-Environment Unmanned Aerial Vehicle Path PlanningMingen Wang0Panliang Yuan1Pengfei Hu2Zhengrong Yang3Shuai Ke4Longliang Huang5Pai Zhang6Laboratory for Robot Mobility Localization and Scene Deep Learning Technology, Guizhou Equipment Manufacturing Polytechnic, Guiyang 550025, ChinaState Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, ChinaLaboratory for Robot Mobility Localization and Scene Deep Learning Technology, Guizhou Equipment Manufacturing Polytechnic, Guiyang 550025, ChinaLaboratory for Robot Mobility Localization and Scene Deep Learning Technology, Guizhou Equipment Manufacturing Polytechnic, Guiyang 550025, ChinaLaboratory for Robot Mobility Localization and Scene Deep Learning Technology, Guizhou Equipment Manufacturing Polytechnic, Guiyang 550025, ChinaLaboratory for Robot Mobility Localization and Scene Deep Learning Technology, Guizhou Equipment Manufacturing Polytechnic, Guiyang 550025, ChinaLaboratory for Robot Mobility Localization and Scene Deep Learning Technology, Guizhou Equipment Manufacturing Polytechnic, Guiyang 550025, ChinaIn recent years, unmanned aerial vehicle (UAV) technology has advanced significantly, enabling its widespread use in critical applications such as surveillance, search and rescue, and environmental monitoring. However, planning reliable, safe, and economical paths for UAVs in real-world environments remains a significant challenge. In this paper, we propose a multi-strategy improved red-tailed hawk (IRTH) algorithm for UAV path planning in real environments. First, we enhance the quality of the initial population in the algorithm by using a stochastic reverse learning strategy based on Bernoulli mapping. Then, the quality of the initial population is further improved through a dynamic position update optimization strategy based on stochastic mean fusion, which enhances the exploration capabilities of the algorithm and helps it explore promising solution spaces more effectively. Additionally, we proposed an optimization method for frontier position updates based on a trust domain, which better balances exploration and exploitation. To evaluate the effectiveness of the proposed algorithm, we compare it with 11 other algorithms using the IEEE CEC2017 test set and perform statistical analysis to assess differences. The experimental results demonstrate that the IRTH algorithm yields competitive performance. Finally, to validate its applicability in real-world scenarios, we apply the IRTH algorithm to the UAV path-planning problem in practical environments, achieving improved results and successfully performing path planning for UAVs.https://www.mdpi.com/2313-7673/10/1/31UAV path planningintelligent optimization algorithmred-tailed hawk algorithmIEEE CEC2017trust domain |
| spellingShingle | Mingen Wang Panliang Yuan Pengfei Hu Zhengrong Yang Shuai Ke Longliang Huang Pai Zhang Multi-Strategy Improved Red-Tailed Hawk Algorithm for Real-Environment Unmanned Aerial Vehicle Path Planning Biomimetics UAV path planning intelligent optimization algorithm red-tailed hawk algorithm IEEE CEC2017 trust domain |
| title | Multi-Strategy Improved Red-Tailed Hawk Algorithm for Real-Environment Unmanned Aerial Vehicle Path Planning |
| title_full | Multi-Strategy Improved Red-Tailed Hawk Algorithm for Real-Environment Unmanned Aerial Vehicle Path Planning |
| title_fullStr | Multi-Strategy Improved Red-Tailed Hawk Algorithm for Real-Environment Unmanned Aerial Vehicle Path Planning |
| title_full_unstemmed | Multi-Strategy Improved Red-Tailed Hawk Algorithm for Real-Environment Unmanned Aerial Vehicle Path Planning |
| title_short | Multi-Strategy Improved Red-Tailed Hawk Algorithm for Real-Environment Unmanned Aerial Vehicle Path Planning |
| title_sort | multi strategy improved red tailed hawk algorithm for real environment unmanned aerial vehicle path planning |
| topic | UAV path planning intelligent optimization algorithm red-tailed hawk algorithm IEEE CEC2017 trust domain |
| url | https://www.mdpi.com/2313-7673/10/1/31 |
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