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Reactivable ripple spreading algorithm for solving post-flood disaster inspection routing problem
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AI
DOI：10.1007/s12065-025-01120-z.png)
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Abstract
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In flood disaster scenarios, efficient post-flood disaster inspection is critical for timely assessment and coordination of rescue operations. Optimizing Unmanned Aerial Vehicle (UAV) inspection paths can significantly improve response efficiency and situational awareness in affected areas. However, existing algorithms for UAV disaster inspection path planning often struggle to balance solution accuracy and computational efficiency, particularly in time-critical post-flood disaster scenarios. To overcome this limitation, this paper establishes the UAV Post-Flood Disaster Inspection Routing Problem (PFDIRP), proposing the Reactivable Ripple Spreading Algorithm (RRSA), a global objective-oriented search method tailored for emergency UAV path optimization. Specifically, by initiating independent ripple relay processes and exploring multiple search paths in parallel, RRSA efficiently determines near-optimal inspection paths. More importantly, this paper conducts experimental simulations on instances of varying scale, including standard benchmark datasets and real-world flood datasets collected from Nanjing and Zhengzhou, China. Results show that RRSA outperforms nine comparative algorithms in both computational efficiency and solution quality, particularly for large-scale and complex scenarios. The algorithm effectively identifies optimal UAV inspection paths, reduces overall inspection time, and enhances the responsiveness of flood disaster management systems, providing a practical tool for post-flood disaster emergency operations.
Keywords:
Reactivable ripple spreading algorithm
Post-flood disaster
Inspection routing problem
Computational efficiency
Solution accuracy
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