摘要
为解决无人机集群网络结构不稳定以及网络生命周期短的问题,提出一种基于改进灰狼算法的分簇优化方法。根据无人机节点的相对移动性和节点间的相对距离,对无人机网络中所有节点分簇,综合考虑簇内节点的剩余能量、最高节点度、通信情况、任务种类4个影响因素,基于灰狼优化算法选举最佳簇首。仿真结果表明,该分簇算法提升了分簇平衡度、统治集更新次数、节点生存个数等多个性能指标,稳定了网络结构,延长了网络生命周期。
To solve the problems of unstable structure and short life cycle of UAV(unmanned aerial vehicle)cluster network,a grey wolf algorithm based clustering optimization method was proposed.According to the relative mobility of UAV nodes and the relative distance between nodes,all nodes in the UAV network were clustered.The remaining energy of the nodes in the cluster,the highest node degree,the communication situation and the task type were comprehensively considered to select the best cluster head based on gray wolf optimization algorithm.The simulation experimental results show that the balance degree of clustering,the updating times of ruling set and the number of node survival are improved using the proposed clustering algorithm,the network structure is stabilized and the network life cycle is prolonged.
作者
张然
高莹雪
丁元明
ZHANG Ran;GAO Ying-xue;DING Yuan-ming(College of Information Engineering,Dalian University,Dalian 116622,China;Communication and Network Key Laboratory,Dalian University,Dalian 116622,China)
出处
《计算机工程与设计》
北大核心
2022年第7期1848-1855,共8页
Computer Engineering and Design
基金
装发部领域基金一般基金项目(61403110308)。
关键词
节点相似度
联合度量指标
灰狼算法
分簇优化算法
无人机集群网络
node similarity
comprehensive measurement index
grey wolf algorithm
clustering optimization algorithm
UAV cluster network
