UAV Cluster Task Assignment Algorithm Based on Improved Artificial Gorilla Troops Optimizer
Efficient task execution and optimized combat effectiveness can be achieved when a cluster of Unmanned Aerial Vehicles (UAVs) work collaboratively by assigning various tasks to each UAV reasonably. This paper suggests an algorithm for assigning tasks to a cluster of UAVs using an improved version of...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10320328/ |
| _version_ | 1797401016318033920 |
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| author | Ran Zhang Honghong Ren Xingda Li Yuanming Ding |
| author_facet | Ran Zhang Honghong Ren Xingda Li Yuanming Ding |
| author_sort | Ran Zhang |
| collection | DOAJ |
| description | Efficient task execution and optimized combat effectiveness can be achieved when a cluster of Unmanned Aerial Vehicles (UAVs) work collaboratively by assigning various tasks to each UAV reasonably. This paper suggests an algorithm for assigning tasks to a cluster of UAVs using an improved version of the Artificial Gorilla Troops Optimizer (GTO) that incorporates multiple strategies. The proposed algorithm adopts the Halton sequence to generate the initial population to ensure diversity. It uses an information sharing search strategy to enhance communication between the silverback gorilla and the population, effectively jumping out of the local optimal solution. In addition, the problem of inadequate solution accuracy caused by rapid convergence in the middle and later stages of iteration is improved using the golden sine strategy to coordinate GTO’s global searching and local mining capabilities. Based on the experimental results, it has been determined that the proposed algorithm outperforms other swarm intelligence algorithms in terms of convergence value and rate across various test functions. Additionally, it has been found to achieve faster and more stable task assignment results. |
| first_indexed | 2024-03-09T02:03:45Z |
| format | Article |
| id | doaj.art-650c566244f74276b6f3b57a87ad589b |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-09T02:03:45Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-650c566244f74276b6f3b57a87ad589b2023-12-08T00:04:22ZengIEEEIEEE Access2169-35362023-01-011113513313514610.1109/ACCESS.2023.333391210320328UAV Cluster Task Assignment Algorithm Based on Improved Artificial Gorilla Troops OptimizerRan Zhang0https://orcid.org/0000-0001-7643-3935Honghong Ren1https://orcid.org/0009-0005-1076-982XXingda Li2https://orcid.org/0009-0007-0567-5065Yuanming Ding3https://orcid.org/0000-0002-8958-1176School of Information Engineering, Dalian University, Dalian, ChinaSchool of Information Engineering, Dalian University, Dalian, ChinaSchool of Information Engineering, Dalian University, Dalian, ChinaSchool of Information Engineering, Dalian University, Dalian, ChinaEfficient task execution and optimized combat effectiveness can be achieved when a cluster of Unmanned Aerial Vehicles (UAVs) work collaboratively by assigning various tasks to each UAV reasonably. This paper suggests an algorithm for assigning tasks to a cluster of UAVs using an improved version of the Artificial Gorilla Troops Optimizer (GTO) that incorporates multiple strategies. The proposed algorithm adopts the Halton sequence to generate the initial population to ensure diversity. It uses an information sharing search strategy to enhance communication between the silverback gorilla and the population, effectively jumping out of the local optimal solution. In addition, the problem of inadequate solution accuracy caused by rapid convergence in the middle and later stages of iteration is improved using the golden sine strategy to coordinate GTO’s global searching and local mining capabilities. Based on the experimental results, it has been determined that the proposed algorithm outperforms other swarm intelligence algorithms in terms of convergence value and rate across various test functions. Additionally, it has been found to achieve faster and more stable task assignment results.https://ieeexplore.ieee.org/document/10320328/UAV cluster task assignmentartificial gorilla troops optimizerHalton sequenceinformation sharing search strategygolden sine strategy |
| spellingShingle | Ran Zhang Honghong Ren Xingda Li Yuanming Ding UAV Cluster Task Assignment Algorithm Based on Improved Artificial Gorilla Troops Optimizer IEEE Access UAV cluster task assignment artificial gorilla troops optimizer Halton sequence information sharing search strategy golden sine strategy |
| title | UAV Cluster Task Assignment Algorithm Based on Improved Artificial Gorilla Troops Optimizer |
| title_full | UAV Cluster Task Assignment Algorithm Based on Improved Artificial Gorilla Troops Optimizer |
| title_fullStr | UAV Cluster Task Assignment Algorithm Based on Improved Artificial Gorilla Troops Optimizer |
| title_full_unstemmed | UAV Cluster Task Assignment Algorithm Based on Improved Artificial Gorilla Troops Optimizer |
| title_short | UAV Cluster Task Assignment Algorithm Based on Improved Artificial Gorilla Troops Optimizer |
| title_sort | uav cluster task assignment algorithm based on improved artificial gorilla troops optimizer |
| topic | UAV cluster task assignment artificial gorilla troops optimizer Halton sequence information sharing search strategy golden sine strategy |
| url | https://ieeexplore.ieee.org/document/10320328/ |
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