基于飞蛾扑火算法的关键节点挖掘方法  

Enhanced moth-flame optimization algorithm for critical node detection

作　　者：许钦钧 徐龙琴 刘双印 赵学华[4] Xu Qinjun;Xu Longqin;Liu Shuangyin;Zhao Xuehua(College of Information Science&Technology,Zhongkai University of Agriculture&Engineering,Guangzhou 510225,China;Intelligent Agriculture Engineering Research Center of Guangdong Higher Education Institutes,Guangzhou 510225,China;Guangzhou Key Laboratory of Agricultural Products Quality&Safety Traceability Information Technology,Guangzhou 510225,China;School of Digital Media,Shen-zhen Institute of Information Technology,Shenzhen Guangdong 518172,China)

机构地区：[1]仲恺农业工程学院信息科学与技术学院,广州510225 [2]广东省高校智慧农业工程技术研究中心,广州510225 [3]广州市农产品质量安全溯源信息技术重点实验室,广州510225 [4]深圳信息职业技术学院数字媒体学院,广东深圳518172

出　　处：《计算机应用研究》2023年第9期2713-2719,2728,共8页Application Research of Computers

基　　金：国家自然科学基金资助项目(61871475);广东省自然科学基金资助项目(2021A1515011994);广州市重点研发计划资助项目(202103000033,201903010043);广东省科技计划资助项目(2020A1414050060,2020B0202080002,2016A020210122,2015A040405014);广东省普通高校创新团队项目(2021KCXTD019,2020KCXTD040,2022KCXTD057);广东省普通高校特色创新项目(KA190578826);梅州市科技计划资助项目(2021A0305010);广州市增城区农村科技特派员资助项目(2021B42121631);广东省教育科学规划课题(2020GXJK102,2018GXJK072);广东省研究生教育创新计划资助项目(2022XSLT056,2022JGXM115)。

摘　　要：关键节点挖掘在理解和控制复杂网络系统方面具有重要作用和巨大潜力。提出了一种基于飞蛾扑火优化算法的关键节点挖掘算法,解决关键节点问题。该算法引入了反向学习等策略,以提高解集的质量和加快收敛。同时,设计了快速种群演化和复合高斯进化等方法,以优化解集并增强解空间探索能力,从而克服局部最优陷阱。在多个合成网络和真实网络数据集上进行的对比实验结果表明,提出的算法相较以于其他先进的对比算法具有更高的鲁棒性,并验证了该算法部件的有效性。The detection of critical nodes plays an important role with significant potential in understanding and controlling complex network systems.This paper proposed critical node mining algorithm based on the moth-flame optimization to address the critical node problem.It introduced strategies such as opposition-based learning to improve the quality of the solution set and accelerate convergence.Additionally,it designed fast population evolution and hybrid Gaussian evolution methods to optimize the solution set and enhance the exploration capability of the solution space,overcoming local optima traps.Comparative experimental results conducted on multiple synthetic and real network datasets demonstrate that the proposed algorithm exhibits higher robustness compared to other advanced comparative algorithms.Furthermore,the effectiveness of the algorithm components is validated.

关 键 词：关键节点 网络连通性 群智能算法 飞蛾扑火优化算法 

分 类 号：TP393.02[自动化与计算机技术—计算机应用技术]