基于蚁群优化算法的多无人机侦察打击任务仿真系统设计与实现

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中图分类号：TN911-34；TP273.2；TP391.9 文献标识码：A 文章编号：1004-373X（2025）15-0018-09

Design and implementation of multi-UAV search-attackmission simulation systembasedonantcolonyoptimizationalgorithm

ZHANG Yongjin，QU Chongxiao，FAN Changjun，CHU Jinqi， LIU Shuo （The52ndResearch InstituteofChinaElectronics TechnologyGroupCorporation，Hangzhou 31oo12，China）

Abstract：Integrated search-atack UAVclusters exhibitsignificant potentialinmodern warfareapplications.However，their large-scaledeploymentandpracticalexercisesarecomplexprocessesrequiringsubstantialresoues.Inspiredbytheforaging behaviorof antcolonies，this paperdesignsandimplementsamulti-UAVsearch-atack missionsimulationsystemutlizingthe antcolonyoptimization（ACO）algorithm.Thesystemaims toprovidearealistic，flexible，anduser-friendlybenchmark platformto support the simulationandevaluationof multi-UAVcollborative tasks.First，thebasic principlesof theACOalgorithmare introduced，uponhichhesimulationprocessforUAVclustersperforingsearch-atackmissionsisdesignedNext，theoveal architectureofthesimulationsystemisconstructed，andacollborativeintellgencealgorithmfortheUAVcusterisdeveloped tooptimizepathplanningduringthesearch-atackprocess.Furthermore，aninteractivesimulationsystemisimplementedwith theLOVE 2Dframework.Finaly，simulationresults forthreerepresentative scenariosarepresented，accompanied bya systematicquantitativeanalysis.Theresultsshowthatthesystemofersusersanaccessbleandeficientplatformforsearchatack missionsimulation，therebyfacilitating theevaluationandoptimizationofcombat strategiesacross various scenarios.

Keywords：ACOalgorithm；UAVcluster;search-attck mission；pathplanning；interactivesimulation；colaborative intelligence

0 引言

无人机集群凭借其独有的灵活性、强大的鲁棒性以及显著的涌现效应能够以有限的成本实现显著的侦察与打击效能[1-2]，并可降低人员伤亡及物资损耗的风险[3]。（剩余14121字）