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Advancing Multi-UAV Inspection Dispatch Based on Bilevel Optimization and GA-NSGA-II

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Pubblicato in:Applied Sciences vol. 15, no. 7 (2025), p. 3673
Autore principale: Liu, Yujing
Altri autori: Chen, Chunmei, Sun, Yu, Miao, Shaojie
Pubblicazione:
MDPI AG
Soggetti:
Elitism
Integer programming
Adaptability
Assignment problem
Planning
Genetic algorithms
Neural networks
Flexibility
Unmanned aerial vehicles
Linear programming
Methods
Heuristic
Optimization algorithms
Traveling salesman problem
Energy consumption
Inspections
Efficiency
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Abstract: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.
ISSN:2076-3417
DOI:10.3390/app15073673
Fonte:Publicly Available Content Database