文献类型：期刊文献

英文题名：A multi-objective adaptive evolutionary algorithm to extract communities in networks

作者：Li, Qi[1];Cao, Zehong[2,4];Ding, Weiping[3,4];Li, Qing[5]

机构：[1]Shaoxing Univ, Dept Comp Sci & Engn, Shaoxing, Zhejiang, Peoples R China;[2]Univ Tasmania, Discipline ICT, Hobart, Tas, Australia;[3]Nantong Univ, Sch Informat Sci & Technol, Nantong, Jiangsu, Peoples R China;[4]Univ Technol Sydney, Sch Comp Sci, Ultimo, NSW, Australia;[5]Chongqing Univ, Coll Comp Sci, Chongqing, Peoples R China

年份：2020

卷号：52

外文期刊名：SWARM AND EVOLUTIONARY COMPUTATION

收录：SCI-EXPANDED(收录号：WOS:000508744100013)、、EI(收录号：20195007837638)、Scopus(收录号：2-s2.0-85076303310)、WOS

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 61976120, the Natural Science Foundation of Jiangsu Province under Grant BK20191445, Qing Lan Project of Jiangsu Province, and the Six Talent Peaks Project of Jiangsu Province under Grant XYDXXJS-048.

语种：英文

外文关键词：Community detection; Genetic algorithm; Multi-objective; Complex networks; Adaptive

外文摘要：Community structure is one of the most important attributes of complex networks, which reveals the hidden rules and behavior characteristics of complex networks. Existing works need to pre-set weight parameters to control the different emphasis on the objective function, and cannot automatically identify the number of communities. In the process of optimization, there will be some challenges, such as premature and inefficiency. This paper presents a multi-objective adaptive fast evolutionary algorithm (F-SGCD) for community detection in complex networks. Firstly, it transforms the problem of community detection into a multi-objective optimization problem and constructs two objective functions of community score and community fitness. Secondly, an external elite gene pool is introduced to store non-inferior solutions with high fitness. At the same time, an adaptive genetic operator is executed to return a set of non-dominant solutions compromised between the two objective functions. Finally, a Pareto optimal solution with the highest modularity is selected and decoded to generate a set of independent subnetworks. Experiments show that the multi-objective adaptive fast evolutionary algorithm greatly improves the accuracy of community detection in complex networks, and can discover the hierarchical structure of complex networks better.