CAJ | 학술논문

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。
침대복잡망락사구발현문제,재표준차분진화산법적광가하,제출일충신형면역리산차분진화산법(Immune discrete differential evolution, IDDE)。해산법통과표첨전파책략생성초시충군,채용리산차분진화책략래보증충군재문제공간적전국수색능력,동시대충군중적우수개체집행침대성적고빈극륭변이조작,이제고산법적국부개발능력,개선산법적수렴성능。재계산궤생성망락여진실세계망락중적방진실험결과표명： IDDE 산법구유교강적심우성능여로봉성,능구유효탐측복잡망락중존재적사구결구。
Aimed at the existing problem of community detection in complex networks, a novel immune discrete differen-tial 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.