基于免疫离散差分进化算法的复杂网络社区发现

张英杰; 龚中汉; 陈乾坤

doi:10.16383/j.aas.2015.c140018

基于免疫离散差分进化算法的复杂网络社区发现

doi: 10.16383/j.aas.2015.c140018

1.
湖南大学信息科学与工程学院长沙 410082

基金项目:

国家自然科学基金(61440026),教育部博士点基金(20110161110035),湖南省自然科学基金重点项目(13JJA002)资助

详细信息

作者简介:
龚中汉湖南大学信息科学与工程学院硕士研究生.2011年获得湖南大学信息科学与工程学院学士学位.主要研究方向为进化计算和数据挖掘.E-mail:hncl18@hnu.edu.cn

通讯作者:
张英杰湖南大学信息科学与工程学院副教授.2005年获得湖南大学控制理论与控制工程博士学位.主要研究方向为智能控制,计算智能和节能优化控制.本文通信作者.E-mail:zhangyj@hnu.edu.cn

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出版历程
- 收稿日期: 2014-01-10
- 修回日期: 2014-12-02
- 刊出日期: 2015-04-20

Community Detection in Complex Networks Using Immune Discrete Differential Evolution Algorithm

1.
College of Information Science and Engineering, Hunan University, Changsha 410082

Funds:

Supported by National Natural Science Foundation of China(61440026), Doctoral Fund of Ministry of Education of China(20110161110035), and Hunan Provincial Natural Science Foundation of China(13JJA002)

摘要

摘要: 针对复杂网络社区发现问题,在标准差分进化算法的框架下,提出一种新型免疫离散差分进化算法(Immune discrete differential evolution, IDDE).该算法通过标签传播策略生成初始种群,采用离散差分进化策略来保证种群在问题空间的全局搜索能力,同时对种群中的优秀个体执行针对性的高频克隆变异操作,以提高算法的局部开发能力,改善算法的收敛性能.在计算机生成网络与真实世界网络中的仿真实验结果表明:IDDE算法具有较强的寻优性能与鲁棒性,能够有效探测复杂网络中存在的社区结构.
- 差分进化 /
- 克隆选择 /
- 社区发现 /
- 模块度
Abstract: Aimed at the existing problem of community detection in complex networks, a novel immune discrete differential evolution(IDDE) is proposed in the framework of standard differential evolution. In the proposed method, the initial population is generated through label propagation, and the discrete differential evolution strategy is utilized to ensure the global searching ability of the IDDE; meanwhile, the high-frequency clonal selection mutation operation is applied to excellent individuals of the population to improve the local exploitation ability and the convergence performance of the IDDE. Artificial networks and several real networks are employed to test the performance of the IDDE, and the testing results show that the IDDE achieves better searching ability and stronger robustness, and that it can detect the community structure in complex networks effectively.
- Differential evolution /
- clonal selection /
- community detection /
- modularity

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参考文献(28)

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基于免疫离散差分进化算法的复杂网络社区发现

doi: 10.16383/j.aas.2015.c140018

作者简介:
龚中汉湖南大学信息科学与工程学院硕士研究生.2011年获得湖南大学信息科学与工程学院学士学位.主要研究方向为进化计算和数据挖掘.E-mail:hncl18@hnu.edu.cn

通讯作者:
张英杰湖南大学信息科学与工程学院副教授.2005年获得湖南大学控制理论与控制工程博士学位.主要研究方向为智能控制,计算智能和节能优化控制.本文通信作者.E-mail:zhangyj@hnu.edu.cn

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Community Detection in Complex Networks Using Immune Discrete Differential Evolution Algorithm

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目录

留言板

基于免疫离散差分进化算法的复杂网络社区发现

doi: 10.16383/j.aas.2015.c140018

作者简介: 龚中汉 湖南大学信息科学与工程学院硕士研究生.2011年获得湖南大学信息科学与工程学院学士学位.主要研究方向为进化计算和数据挖掘.E-mail:hncl18@hnu.edu.cn

通讯作者: 张英杰 湖南大学信息科学与工程学院副教授.2005年获得湖南大学控制理论与控制工程博士学位.主要研究方向为智能控制,计算智能和节能优化控制.本文通信作者.E-mail:zhangyj@hnu.edu.cn

计量

出版历程

Community Detection in Complex Networks Using Immune Discrete Differential Evolution Algorithm

计量

出版历程

目录

作者简介:
龚中汉湖南大学信息科学与工程学院硕士研究生.2011年获得湖南大学信息科学与工程学院学士学位.主要研究方向为进化计算和数据挖掘.E-mail:hncl18@hnu.edu.cn

通讯作者:
张英杰湖南大学信息科学与工程学院副教授.2005年获得湖南大学控制理论与控制工程博士学位.主要研究方向为智能控制,计算智能和节能优化控制.本文通信作者.E-mail:zhangyj@hnu.edu.cn