计算机应用研究2017，Vol.34Issue(3)：858-861,4.DOI:10.3969/j.issn.1001-3695.2017.03.051
基于自适应Memetic算法的多目标复杂网络社区检测
Multi-objective community detection in complex networks based on adaptive Memetic algorithm
摘要
Abstract
In order to improve the accuracy of the community detection in complex networks,this paper proposed a multiobjective community detection based on adaptive memetic algorithm.In global search,the algorithm applied the Logistic function to set the corresponding crossover probability and mutation probability,and turned the multi-objective optimization problem into minimal optimization of two objectives called kernel K-means(KKM) and ratio cut(RC) at the same time.In local search,it constituted the local optimization target of weights of two objective functions and used a hill-climbing strategy to find the best individual.Experiments on synthetic and real life networks show that,compared with five algorithms based on GAs (genetic algorithms) and Fast Modularity algorithm,the proposed algorithm can effectively improve the accuracy of the community detection and has certain advantages in solving community detection problems in complex networks.关键词
复杂网络/社区检测/多目标/Memetic算法/自适应Key words
complex networks/community detection/multi-objective/Memetic algorithm/adaptive分类
信息技术与安全科学引用本文复制引用
姚莹,周井泉..基于自适应Memetic算法的多目标复杂网络社区检测[J].计算机应用研究,2017,34(3):858-861,4.基金项目
江苏省普通高校研究生科研创新计划项目(SJLX15_0377) （SJLX15_0377）
