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Title of Journal: Soft Comput

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Abbravation: Soft Computing

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Springer Berlin Heidelberg

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DOI

10.1002/app.28494

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1433-7479

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A multiobjective discrete cuckoo search algorithm

Authors: Xu Zhou Yanheng Liu Bin Li Han Li
Publish Date: 2016/06/09
Volume: 21, Issue: 22, Pages: 6641-6652
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Abstract

Evolutionary clustering is a popular method for community detection in dynamic networks by introducing the concept of temporal smoothness Some evolutionary based clustering approaches need an input parameter to control the preference degree of snapshot and temporal cost To break the limitation of parameter selection and increase accuracy of detecting communities we propose a multiobjective discrete cuckoo search algorithm to discover communities in dynamic networks Firstly ordered neighbor list method is used to encode the location of nest for population initialization Secondly a discrete framework of cuckoo search algorithm is proposed with a modified nest location updating strategy and abandon operator Finally based on the proposed discrete framework a multiobjective discrete cuckoo search algorithm is proposed by integrating the nondominated sorting method and the crowding distance method Experimental results on synthetic and real networks demonstrate that the proposed algorithm is effective and has higher accuracy than other compared algorithmsWe would like to thank the anonymous referees for their many valuable suggestions and comments This work is supported by the National Natural Science Foundation of China Grant No 61373123 Key Development Program for Science and Technology of Jilin Province China Grant No 20150414004GH

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