基于自适应果蝇优化算法的加权分簇算法  

Weighted clustering algorithm based on adaptive fruit fly optimization algorithm

作　　者：王翔宇 张艳语[1] 李龙 菅春晓 崔维嘉[1] WANG Xiangyu;ZHANG Yanyu;LI Long;JIAN Chunxiao;CUI Weijia(School of Systems Engineering,Information Engineering University,Zhengzhou 450001,China)

机构地区：[1]信息工程大学信息系统工程学院,河南郑州450001

出　　处：《系统工程与电子技术》2023年第7期2259-2268,共10页Systems Engineering and Electronics

基　　金：国家自然科学基金(61701536,62171468)资助课题。

摘　　要：针对无人机编队网络管理问题,提出了一种基于自适应果蝇优化算法的加权分簇算法,利用分簇结构进行网络优化。该算法使用了基于离差标准化的数据归一化方法对各性能指标进行处理,并根据整体能耗改变权值分配规则,共同提高了簇头选举的客观性;分析了未定节点调整准则,提出了应用自适应果蝇优化算法进行簇的规模优化,消除了孤立节点和小规模簇;引入了剩余能量阈值和安全距离阈值约束维护条件,并分析了阈值的最优取值,减少了簇的维护次数。仿真结果表明,所提算法能够有效提高无人机编队各方面的性能,与现有算法相比,能够获得更好的网络管理效果。To improve the network management of unmanned aerial vehicle formation,the weighted clustering algorithm based on the adaptive fruit fly optimization algorithm is proposed to optimize the network through cluster structure.The proposed algorithm employs a data normalization method based on min-max standardization for each performance index and changes the weight assignment rules according to the overall energy consumption,which jointly improves the objectivity of cluster head election.This paper analyzes the adjustment criterion of undefined nodes,and proposes an adaptive fruit fly optimization algorithm for cluster scale optimization,and then eliminats isolated nodes and small-scale clusters and introduces a residual energy threshold and a safe distance threshold to constrain the maintenance conditions and analyzes the optimal values of the thresholds to reduce the number of cluster maintenance.Simulation results show that the proposed algorithm in this paper can effectively improve the performance of all aspects of the unmanned aerial vehicle formation and obtain better network management results comparing with the existing algorithms.

关 键 词：加权分簇算法 离差标准化 自适应果蝇优化算法 剩余能量阈值 安全距离阈值 

分 类 号：TN929.5[电子电信—通信与信息系统]