UAV Path Planning Using a State Transition Simulated Annealing Algorithm Based on Integrated Destruction Operators and Backward Learning Strategies
This study introduces a state transition simulated annealing algorithm that incorporates integrated destruction operators and backward learning strategies (DRSTASA) to address complex challenges in UAV path planning within multidimensional environments. UAV path planning is a critical optimization p...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/11/6064 |
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| author | Jianping Liu Xiaoxia Han Fengyi Liu Jinde Wu Wenjie Zhang |
| author_facet | Jianping Liu Xiaoxia Han Fengyi Liu Jinde Wu Wenjie Zhang |
| author_sort | Jianping Liu |
| collection | DOAJ |
| description | This study introduces a state transition simulated annealing algorithm that incorporates integrated destruction operators and backward learning strategies (DRSTASA) to address complex challenges in UAV path planning within multidimensional environments. UAV path planning is a critical optimization problem that requires smooth flight paths, obstacle avoidance, moderate angle changes, and minimized flight distance to conserve fuel and reduce travel time. Traditional algorithms often become trapped in local optima, preventing them from finding globally optimal solutions. DRSTASA improves global search capabilities by initializing the population with Latin hypercube sampling, combined with destruction operators and backward learning strategies. Testing on 23 benchmark functions demonstrates that the algorithm outperforms both traditional and advanced metaheuristic algorithms in solving single and multimodal problems. Furthermore, in eight engineering design optimization scenarios, DRSTASA exhibits superior performance compared to the STASA and SNS algorithms, highlighting the significant advantages of this method. DRSTASA is also successfully applied to UAV path planning, identifying optimal paths and proving the practical value of the algorithm. |
| format | Article |
| id | doaj-art-e6d8d40b91194dcdace35dd3d0413e0a |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-e6d8d40b91194dcdace35dd3d0413e0a2025-08-20T02:33:01ZengMDPI AGApplied Sciences2076-34172025-05-011511606410.3390/app15116064UAV Path Planning Using a State Transition Simulated Annealing Algorithm Based on Integrated Destruction Operators and Backward Learning StrategiesJianping Liu0Xiaoxia Han1Fengyi Liu2Jinde Wu3Wenjie Zhang4School of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, ChinaSchool of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, ChinaSchool of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, ChinaSchool of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, ChinaSchool of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, ChinaThis study introduces a state transition simulated annealing algorithm that incorporates integrated destruction operators and backward learning strategies (DRSTASA) to address complex challenges in UAV path planning within multidimensional environments. UAV path planning is a critical optimization problem that requires smooth flight paths, obstacle avoidance, moderate angle changes, and minimized flight distance to conserve fuel and reduce travel time. Traditional algorithms often become trapped in local optima, preventing them from finding globally optimal solutions. DRSTASA improves global search capabilities by initializing the population with Latin hypercube sampling, combined with destruction operators and backward learning strategies. Testing on 23 benchmark functions demonstrates that the algorithm outperforms both traditional and advanced metaheuristic algorithms in solving single and multimodal problems. Furthermore, in eight engineering design optimization scenarios, DRSTASA exhibits superior performance compared to the STASA and SNS algorithms, highlighting the significant advantages of this method. DRSTASA is also successfully applied to UAV path planning, identifying optimal paths and proving the practical value of the algorithm.https://www.mdpi.com/2076-3417/15/11/6064STASAdestruction operatorsbackward learning strategiesUAV path planning |
| spellingShingle | Jianping Liu Xiaoxia Han Fengyi Liu Jinde Wu Wenjie Zhang UAV Path Planning Using a State Transition Simulated Annealing Algorithm Based on Integrated Destruction Operators and Backward Learning Strategies Applied Sciences STASA destruction operators backward learning strategies UAV path planning |
| title | UAV Path Planning Using a State Transition Simulated Annealing Algorithm Based on Integrated Destruction Operators and Backward Learning Strategies |
| title_full | UAV Path Planning Using a State Transition Simulated Annealing Algorithm Based on Integrated Destruction Operators and Backward Learning Strategies |
| title_fullStr | UAV Path Planning Using a State Transition Simulated Annealing Algorithm Based on Integrated Destruction Operators and Backward Learning Strategies |
| title_full_unstemmed | UAV Path Planning Using a State Transition Simulated Annealing Algorithm Based on Integrated Destruction Operators and Backward Learning Strategies |
| title_short | UAV Path Planning Using a State Transition Simulated Annealing Algorithm Based on Integrated Destruction Operators and Backward Learning Strategies |
| title_sort | uav path planning using a state transition simulated annealing algorithm based on integrated destruction operators and backward learning strategies |
| topic | STASA destruction operators backward learning strategies UAV path planning |
| url | https://www.mdpi.com/2076-3417/15/11/6064 |
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