Chaotic Random Opposition-Based Learning and Cauchy Mutation Improved Moth-Flame Optimization Algorithm for Intelligent Route Planning of Multiple UAVs
UAV route planning is the key issue for application of UAV in real-world scenarios. Compared with the traditional route planning methods, although the intelligent optimization algorithm has stronger applicability and optimization performance, it also has the problem of poor convergence accuracy and...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9768832/ |
| _version_ | 1811256388623532032 |
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| author | Mingxi Ma Jun Wu Yue Shi Longfei Yue Cheng Yang Xuyi Chen |
| author_facet | Mingxi Ma Jun Wu Yue Shi Longfei Yue Cheng Yang Xuyi Chen |
| author_sort | Mingxi Ma |
| collection | DOAJ |
| description | UAV route planning is the key issue for application of UAV in real-world scenarios. Compared with the traditional route planning methods, although the intelligent optimization algorithm has stronger applicability and optimization performance, it also has the problem of poor convergence accuracy and easy to fall into local optimization. Therefore, an intelligent route planning method for UAV based on chaotic random opposition-based learning and cauchy mutation improved Moth-flame optimization algorithm (OLTC-MFO) is proposed. First, the terrain environment is constructed by digital elevation map, and the threat model is established to realize the equivalent three-dimensional (3D) environment. Then, the opposite population is introduced to increase the diversity of solutions and improve the search speed of the algorithm. Then, the Logistic-Tent chaos map is introduced to realize random perturbation of flame position, which improves the global search capability of the algorithm. Finally, the probability operator and Cauchy mutation operator are introduced, which makes the algorithm not only accept the current solution with a certain probability, but also jump out of the current sub-optimal solution, thus enhancing the global search capability of the algorithm. The simulation results show that when the number of iterations is 1000, the length of route planning based on OLTC-MFO algorithm is 45.3716km shorter than MFO algorithm, and the convergence result of this method is stable and more accurate, which achieves the purpose of assisting UAV combat decision-making. |
| first_indexed | 2024-04-12T17:39:42Z |
| format | Article |
| id | doaj.art-75bb3f353fbe46a7920233c480c856a5 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T17:39:42Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-75bb3f353fbe46a7920233c480c856a52022-12-22T03:22:50ZengIEEEIEEE Access2169-35362022-01-0110493854939710.1109/ACCESS.2022.31727109768832Chaotic Random Opposition-Based Learning and Cauchy Mutation Improved Moth-Flame Optimization Algorithm for Intelligent Route Planning of Multiple UAVsMingxi Ma0https://orcid.org/0000-0003-2463-8300Jun Wu1Yue Shi2https://orcid.org/0000-0003-3387-9683Longfei Yue3Cheng Yang4Xuyi Chen5Air Control and Navigation College, Air Force Engineering University, Xi’an, ChinaAir Control and Navigation College, Air Force Engineering University, Xi’an, ChinaEquipment Management and UAV Engineering College, Air Force Engineering University, Xi’an, ChinaAir Control and Navigation College, Air Force Engineering University, Xi’an, China93787 Army, Beijing, China93787 Army, Beijing, ChinaUAV route planning is the key issue for application of UAV in real-world scenarios. Compared with the traditional route planning methods, although the intelligent optimization algorithm has stronger applicability and optimization performance, it also has the problem of poor convergence accuracy and easy to fall into local optimization. Therefore, an intelligent route planning method for UAV based on chaotic random opposition-based learning and cauchy mutation improved Moth-flame optimization algorithm (OLTC-MFO) is proposed. First, the terrain environment is constructed by digital elevation map, and the threat model is established to realize the equivalent three-dimensional (3D) environment. Then, the opposite population is introduced to increase the diversity of solutions and improve the search speed of the algorithm. Then, the Logistic-Tent chaos map is introduced to realize random perturbation of flame position, which improves the global search capability of the algorithm. Finally, the probability operator and Cauchy mutation operator are introduced, which makes the algorithm not only accept the current solution with a certain probability, but also jump out of the current sub-optimal solution, thus enhancing the global search capability of the algorithm. The simulation results show that when the number of iterations is 1000, the length of route planning based on OLTC-MFO algorithm is 45.3716km shorter than MFO algorithm, and the convergence result of this method is stable and more accurate, which achieves the purpose of assisting UAV combat decision-making.https://ieeexplore.ieee.org/document/9768832/Route planningmoth-flame optimization algorithmcauchy mutationrandom opposition-based learningchaotic mapping |
| spellingShingle | Mingxi Ma Jun Wu Yue Shi Longfei Yue Cheng Yang Xuyi Chen Chaotic Random Opposition-Based Learning and Cauchy Mutation Improved Moth-Flame Optimization Algorithm for Intelligent Route Planning of Multiple UAVs IEEE Access Route planning moth-flame optimization algorithm cauchy mutation random opposition-based learning chaotic mapping |
| title | Chaotic Random Opposition-Based Learning and Cauchy Mutation Improved Moth-Flame Optimization Algorithm for Intelligent Route Planning of Multiple UAVs |
| title_full | Chaotic Random Opposition-Based Learning and Cauchy Mutation Improved Moth-Flame Optimization Algorithm for Intelligent Route Planning of Multiple UAVs |
| title_fullStr | Chaotic Random Opposition-Based Learning and Cauchy Mutation Improved Moth-Flame Optimization Algorithm for Intelligent Route Planning of Multiple UAVs |
| title_full_unstemmed | Chaotic Random Opposition-Based Learning and Cauchy Mutation Improved Moth-Flame Optimization Algorithm for Intelligent Route Planning of Multiple UAVs |
| title_short | Chaotic Random Opposition-Based Learning and Cauchy Mutation Improved Moth-Flame Optimization Algorithm for Intelligent Route Planning of Multiple UAVs |
| title_sort | chaotic random opposition based learning and cauchy mutation improved moth flame optimization algorithm for intelligent route planning of multiple uavs |
| topic | Route planning moth-flame optimization algorithm cauchy mutation random opposition-based learning chaotic mapping |
| url | https://ieeexplore.ieee.org/document/9768832/ |
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