Deployment method for air defense countermeasure systems against drone swarms based on water wave optimization and A*algorithm
LI Xiang
LUO Wangchun
ZHANG Fu
ZHANG Xinghua
LIU Hongyi
Abstract：[Objective]Given the widespread use of drone swarms in reconnaissance missions,optimizing the deployment of air defense countermeasure systems has become a critical issue for enhancing defensive capabilities.Drone swarms,with their high flexibility,robust survivability,and cost-effectiveness,pose a significant threat to traditional air defense frameworks.A single air defense system struggles to effectively address the coordinated multi-target nature of drone swarms,necessitating the collaborative deployment of multiple systems to maximize the flight cost of the swarm,thereby forcing path alterations or mission abandonment.This study aims to develop an efficient deployment method for air defense countermeasure systems to mitigate the security challenges posed by drone swarm reconnaissance.[Methods]This study introduced a deployment method for air defense countermeasure systems against drone swarms,based on water wave optimization(WWO)and the A*algorithm,termed the water wave and A*deployment(WAD)algorithm.The approach integrated two key sub-models:first,an optimal path planning model for drone swarms,which calculated the minimum flight cost under specified air defense countermeasure system positions;second,an air defense countermeasure system location optimization model that adjusted system positions to maximize the expected flight cost of the swarm.The WAD algorithm leveraged WWO's balanced global and local search capabilities alongside the A* algorithm's efficiency in path planning,enhanced by an improved encoding-decoding scheme to boost search efficiency and avoid suboptimal solution spaces.[Results]The effectiveness of the WAD algorithm is confirmed through simulation experiments.The experimental scenario includes 4 flight starting points,39 waypoints,and 3 air defense countermeasure systems.Results demonstrate that the WAD algorithm is able to obtain the maximum expected flight cost for the drone swarm and output optimized deployment positions for the air defense countermeasure systems and the swarm's flight paths.The population's best fitness converges rapidly with the increase of iteration counts,stabilizing within an average of 30 iterations,highlighting the algorithm's high precision and computational efficiency,and significantly shortening the optimization time compared with traditional methods.[Conclusions]The WAD algorithm provides an efficient solution for optimizing the deployment of air defense countermeasure systems against drone swarms.By integrating the strengths of WWO and the A*algorithm,it achieves an effective balance between global exploration and local exploitation,markedly improving convergence speed and optimization quality.The findings indicate that this method is applicable to defense requirements in complex reconnaissance scenarios.Future work may further investigates multi-objective optimization strategies in dynamic environments,explores coordination mechanisms among air defense countermeasure systems,and incorporates real-time threat assessment to adapt to the rapid evolution of drone swarm technologies.
Keywords：drone swarmair defense countermeasure systemdeployment optimizationwater wave optimizationA*algorithmpath planningfacility locationevolutionary algorithm
Publication Date：2026-01-25
Online Publishing Date：2026-03-17(First online date of this platform, not the publication date of the document)
Pages：9( 74-82 )