厦门大学学报（自然科学版）2018，Vol.57Issue(1)：104-111,8.DOI:10.6043/j.issn.0438-0479.201703040
一种高效的基于教与学的社区发现算法
An Efficient Multi-population Community Detection Algorithm Using Teaching-learning-based Optimization
摘要
Abstract
Community structure is an important feature of complex networks,and community detection aims at mining the commu-nity structure of complex networks.In order to improve the multi-objective optimization of community detection using discrete tea-ching-learning-based optimization with decomposition(MODTLBO/D),and decrease time complexity,we propose an efficient tea-ching-learning-based optimization algorithm combined with multi-population evolutionary strategy for community detection.In this study,we adopt adaptive learning factor in teacher phase to enhance the ability of exploration and search.In learner phase,each learner employs the random learning strategy or modified quantum-behaved learning strategy in corresponding subpopulation.After each generation,subpopulations exchange information to maintain the diversity and discourage premature convergence.The experi-ments results demonstrate that our proposed algorithm has an advantage of time complexity and is highly efficient at discovering quality community structure.关键词
社区发现/教与学/多目标/多种群进化算法Key words
community detection/teaching-learning/multi-objective/multi-population evolutionary algorithm分类
信息技术与安全科学引用本文复制引用
李佩茜,冯少荣..一种高效的基于教与学的社区发现算法[J].厦门大学学报（自然科学版）,2018,57(1):104-111,8.基金项目
国家社会科学基金重大项目(13&ZD148) （13&ZD148）
