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A multi-strategy enhanced black-winged kite algorithm for UAV path planning
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AI
DOI：10.1007/s11227-025-07905-4.png)
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Abstract
En 中文
To address the problems of insufficient search range and optimization performance of UAVs in 3D path planning, as well as the defects that the existing black-winged kite algorithm has insufficient optimization accuracy and is prone to falling into the local optimum, a multi-strategy augmented black-winged kite algorithm (DBKA) is proposed as a method for UAV 3D path planning, which first establishes the constraints of the 3D topography, the threat zone, and the UAVs themselves; second, the Kent chaotic mapping is introduced to initialize the population to improve the diversity and quality of the population; second, the spiral foraging and tumbling foraging ideas of manta ray foraging algorithms are borrowed in the attack phase, and the position updating method is improved to improve the algorithm’s ability of global search; finally, the mixed strategy mechanism is added in the migration phase to enhance the algorithm’s ability of local optimization search. The results from solving the classical test set and the CEC2017 test set indicate that the DBKA, which incorporates the three strategies, significantly improves search accuracy, search speed, and robustness. Additionally, experiments on UAV path planning confirm that the DBKA outperforms the original BKA algorithm in search ability, producing shorter and smoother paths. The experimental results show that the improved algorithm can effectively solve the UAV path planning problem.
Keywords:
Unmanned aircraft
Path planning
Black-winged kite algorithm
Dynamic attack strategy
Adaptive T distribution
Neighborhood search strategy
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