A sensor network charging path planning method based on an improved black-winged kite algorithm

By improving the Blackwing Kite algorithm and utilizing the optimal point set theory and quantum particle swarm optimization mechanism to optimize the UAV charging path, the problems of insufficient population diversity and insufficient global exploration capability are solved, achieving more efficient UAV charging path planning and improving the operational performance and lifespan of the sensor network.

CN121916924BActive Publication Date: 2026-05-26NORTHEASTERN UNIV AT QINHUANGDAO
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
NORTHEASTERN UNIV AT QINHUANGDAO
Filing Date
2026-03-25
Publication Date
2026-05-26

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Abstract

This invention discloses a sensor network charging path planning method based on an improved black-winged kite algorithm, comprising: constructing a task space model of a wireless rechargeable sensor network; constructing a flight cost function for the target; initializing a black-winged kite population using optimal point set theory to generate an initial population; iteratively optimizing the black-winged kite population by simulating its attack and migration behaviors; calculating the fitness values ​​of all individual black-winged kites after each iteration, and updating the individual optimal positions and the global optimal positions; determining whether the current iteration count has reached the maximum iteration count; if so, ending the optimization and outputting the UAV charging path corresponding to the global optimal position; otherwise, continuing the iterative search. This invention can generate more rationally structured flight paths, effectively reducing the total energy consumption of the UAV, while also reducing sensor failure time, improving network operational stability and charging efficiency, and is suitable for mobile charging scheduling scenarios under complex constraints.
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