MSCSO: A Modified Sand Cat Swarm Algorithm for 3D UAV Path Planning in Complex Environments with Multiple Threats
To improve the global search efficiency and dynamic adaptability of the Sand Cat Swarm Optimization (SCSO) algorithm for UAV path planning in complex 3D environments, this study proposes a Modified Sand Cat Swarm Optimization (MSCSO) algorithm by integrating chaotic mapping initialization, Lévy flig...
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| Format: | Article |
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MDPI AG
2025-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/9/2730 |
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| author | Zhengsheng Zhan Dangyue Lai Canjian Huang Zhixiang Zhang Yongle Deng Jian Yang |
| author_facet | Zhengsheng Zhan Dangyue Lai Canjian Huang Zhixiang Zhang Yongle Deng Jian Yang |
| author_sort | Zhengsheng Zhan |
| collection | DOAJ |
| description | To improve the global search efficiency and dynamic adaptability of the Sand Cat Swarm Optimization (SCSO) algorithm for UAV path planning in complex 3D environments, this study proposes a Modified Sand Cat Swarm Optimization (MSCSO) algorithm by integrating chaotic mapping initialization, Lévy flight–Metropolis hybrid exploration mechanisms, simulated annealing–particle swarm hybrid exploitation strategies, and elite mutation techniques. These strategies not only significantly enhance the convergence speed while ensuring algorithmic precision but also provide effective avenues for enhancing the performance of SCSO. We successfully apply these modifications to UAV path planning scenarios in complex environments. Experimental results on 18 benchmark functions demonstrate the enhanced convergence speed and stability of MSCSO. The proposed method has a superior performance in multimodal optimization tasks. The performance of MSCSO in eight complex scenarios that derived from real-world terrain data by comparing MSCSO with three state-of-the-art algorithms, MSCSO generates shorter average path lengths, reduces collision risks by 21–35%, and achieves higher computational efficiency. Its robustness in obstacle-dense and multi-waypoint environments confirms its practicality in engineering contexts. Overall, MSCSO demonstrates substantial potential in low-altitude resource exploration and emergency rescue operations. These innovative strategies offer theoretical and technical foundations for autonomous decision-making in intelligent unmanned systems. |
| format | Article |
| id | doaj-art-c9fff16e6c97481f86300b29a9ee1d2c |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-c9fff16e6c97481f86300b29a9ee1d2c2025-08-20T03:52:57ZengMDPI AGSensors1424-82202025-04-01259273010.3390/s25092730MSCSO: A Modified Sand Cat Swarm Algorithm for 3D UAV Path Planning in Complex Environments with Multiple ThreatsZhengsheng Zhan0Dangyue Lai1Canjian Huang2Zhixiang Zhang3Yongle Deng4Jian Yang5School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, ChinaSchool of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, ChinaSchool of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, ChinaSchool of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, ChinaSchool of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, ChinaSchool of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, ChinaTo improve the global search efficiency and dynamic adaptability of the Sand Cat Swarm Optimization (SCSO) algorithm for UAV path planning in complex 3D environments, this study proposes a Modified Sand Cat Swarm Optimization (MSCSO) algorithm by integrating chaotic mapping initialization, Lévy flight–Metropolis hybrid exploration mechanisms, simulated annealing–particle swarm hybrid exploitation strategies, and elite mutation techniques. These strategies not only significantly enhance the convergence speed while ensuring algorithmic precision but also provide effective avenues for enhancing the performance of SCSO. We successfully apply these modifications to UAV path planning scenarios in complex environments. Experimental results on 18 benchmark functions demonstrate the enhanced convergence speed and stability of MSCSO. The proposed method has a superior performance in multimodal optimization tasks. The performance of MSCSO in eight complex scenarios that derived from real-world terrain data by comparing MSCSO with three state-of-the-art algorithms, MSCSO generates shorter average path lengths, reduces collision risks by 21–35%, and achieves higher computational efficiency. Its robustness in obstacle-dense and multi-waypoint environments confirms its practicality in engineering contexts. Overall, MSCSO demonstrates substantial potential in low-altitude resource exploration and emergency rescue operations. These innovative strategies offer theoretical and technical foundations for autonomous decision-making in intelligent unmanned systems.https://www.mdpi.com/1424-8220/25/9/2730UAV path planningsand cat swarm optimizationchaotic mappingLévy flight long-step perturbationnonlinear particle swarm optimization weightelite mutation mechanism |
| spellingShingle | Zhengsheng Zhan Dangyue Lai Canjian Huang Zhixiang Zhang Yongle Deng Jian Yang MSCSO: A Modified Sand Cat Swarm Algorithm for 3D UAV Path Planning in Complex Environments with Multiple Threats Sensors UAV path planning sand cat swarm optimization chaotic mapping Lévy flight long-step perturbation nonlinear particle swarm optimization weight elite mutation mechanism |
| title | MSCSO: A Modified Sand Cat Swarm Algorithm for 3D UAV Path Planning in Complex Environments with Multiple Threats |
| title_full | MSCSO: A Modified Sand Cat Swarm Algorithm for 3D UAV Path Planning in Complex Environments with Multiple Threats |
| title_fullStr | MSCSO: A Modified Sand Cat Swarm Algorithm for 3D UAV Path Planning in Complex Environments with Multiple Threats |
| title_full_unstemmed | MSCSO: A Modified Sand Cat Swarm Algorithm for 3D UAV Path Planning in Complex Environments with Multiple Threats |
| title_short | MSCSO: A Modified Sand Cat Swarm Algorithm for 3D UAV Path Planning in Complex Environments with Multiple Threats |
| title_sort | mscso a modified sand cat swarm algorithm for 3d uav path planning in complex environments with multiple threats |
| topic | UAV path planning sand cat swarm optimization chaotic mapping Lévy flight long-step perturbation nonlinear particle swarm optimization weight elite mutation mechanism |
| url | https://www.mdpi.com/1424-8220/25/9/2730 |
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