Advancing Multi-UAV Inspection Dispatch Based on Bilevel Optimization and GA-NSGA-II
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| Vydáno v: | Applied Sciences vol. 15, no. 7 (2025), p. 3673 |
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| Další autoři: | , , |
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MDPI AG
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| 100 | 1 | |a Liu, Yujing | |
| 245 | 1 | |a Advancing Multi-UAV Inspection Dispatch Based on Bilevel Optimization and GA-NSGA-II | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a In multi-UAV collaborative power grid inspection, the system efficiency of existing methods is limited by the performance of both task assignment and path planning, which is critical in large-scale task scenarios, resulting in a huge computational cost and a high possibility to local optimality. To address these challenges, a bilevel optimization framework based on GA-NSGA-II and task segmentation is proposed to balance the total inspection distance and the distance standard deviation of UAVs, where the outer optimization employs the NSGA-II to assign task units to each UAV evenly, while the inner optimization deploys an adaptive genetic algorithm with an elite retention strategy to optimize the inspection direction and order in each task domain to obtain a Pareto-optimal solution set under constraints. To avoid the dimensionality disaster, the massive inspection points are combined into task units based on the UAV’s endurance. In scenarios with 284 tower task points, the proposed algorithm has reduced the standard deviation of UAV flight distances by 41.91% to 84.63% and the longest flight distance by 29.41% to 43.98% compared to the GA-GA bilevel optimization. Against task-adaptive clustering optimization, it decreased the standard deviation by 18.25% to 94.93% and the longest flight distance by 15.97% to 37.33%. Applying it to 406 tower task points also confirmed the GA-NSGA-II bilevel optimization’s effectiveness in minimizing the total inspection distance and balancing UAV workloads. | |
| 653 | |a Elitism | ||
| 653 | |a Integer programming | ||
| 653 | |a Adaptability | ||
| 653 | |a Assignment problem | ||
| 653 | |a Planning | ||
| 653 | |a Genetic algorithms | ||
| 653 | |a Neural networks | ||
| 653 | |a Flexibility | ||
| 653 | |a Unmanned aerial vehicles | ||
| 653 | |a Linear programming | ||
| 653 | |a Methods | ||
| 653 | |a Heuristic | ||
| 653 | |a Optimization algorithms | ||
| 653 | |a Traveling salesman problem | ||
| 653 | |a Energy consumption | ||
| 653 | |a Inspections | ||
| 653 | |a Efficiency | ||
| 700 | 1 | |a Chen, Chunmei | |
| 700 | 1 | |a Sun, Yu | |
| 700 | 1 | |a Miao, Shaojie | |
| 773 | 0 | |t Applied Sciences |g vol. 15, no. 7 (2025), p. 3673 | |
| 786 | 0 | |d ProQuest |t Publicly Available Content Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3188782710/abstract/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3188782710/fulltextwithgraphics/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3188782710/fulltextPDF/embedded/H09TXR3UUZB2ISDL?source=fedsrch |