Multiobjective Complex Network Clustering Based on Dynamical Decomposition Particle Swarm Optimization

Clustering is a basic tool applied to complex networks. However, the clustering of complex networks is often based on a single objective function, which can obtain insufficient clustering effects. To address the insufficiencies of single objective complex network clustering, multiobjective complex n...

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Published in:	IEEE access Vol. 8; pp. 32341 - 32352
Main Authors:	Gao, Tiaokang, Cao, Bin, Zhang, Mengxuan
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Clustering Clustering algorithms complex network clustering Complex networks Decomposition discrete particle swarm dynamic decomposition Heuristic algorithms Linear programming Mathematical analysis Multiobjective optimization Optimization Particle swarm optimization
ISSN:	2169-3536, 2169-3536
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Abstract	Clustering is a basic tool applied to complex networks. However, the clustering of complex networks is often based on a single objective function, which can obtain insufficient clustering effects. To address the insufficiencies of single objective complex network clustering, multiobjective complex network clustering was proposed. In this article, to improve multiobjective complex network clustering, we prove the superiority of dynamic decomposition mathematically and propose a parallel discrete particle swarm optimization algorithm based on dynamic decomposition (DDDPSO). First, solutions are obtained at different levels by optimizing the objective functions of parallel subpopulations. Second, the decomposition space is divided dynamically by the reference vector of dynamic decomposition. Particle swarms are used to search for optimal solutions in the partitioned dynamic spaces. Finally, the individuals in the particle swarm are sorted according to the obtained solutions to obtain individuals with good convergence and diversity. We conduct comparisons with many state-of-the-art algorithms on many widely used test datasets to test the DDDPSO. The experimental results show the effectiveness of the proposed approach for complex network clustering.
AbstractList	Clustering is a basic tool applied to complex networks. However, the clustering of complex networks is often based on a single objective function, which can obtain insufficient clustering effects. To address the insufficiencies of single objective complex network clustering, multiobjective complex network clustering was proposed. In this article, to improve multiobjective complex network clustering, we prove the superiority of dynamic decomposition mathematically and propose a parallel discrete particle swarm optimization algorithm based on dynamic decomposition (DDDPSO). First, solutions are obtained at different levels by optimizing the objective functions of parallel subpopulations. Second, the decomposition space is divided dynamically by the reference vector of dynamic decomposition. Particle swarms are used to search for optimal solutions in the partitioned dynamic spaces. Finally, the individuals in the particle swarm are sorted according to the obtained solutions to obtain individuals with good convergence and diversity. We conduct comparisons with many state-of-the-art algorithms on many widely used test datasets to test the DDDPSO. The experimental results show the effectiveness of the proposed approach for complex network clustering.
Author	Gao, Tiaokang Cao, Bin Zhang, Mengxuan
Author_xml	– sequence: 1 givenname: Tiaokang orcidid: 0000-0002-4394-2001 surname: Gao fullname: Gao, Tiaokang email: 201722102013@stu.hebut.edu.cn organization: School of Artificial Intelligence, Hebei University of Technology, Tianjin, China – sequence: 2 givenname: Bin orcidid: 0000-0003-4558-9501 surname: Cao fullname: Cao, Bin email: caobin@scse.hebut.edu.cn organization: School of Artificial Intelligence, Hebei University of Technology, Tianjin, China – sequence: 3 givenname: Mengxuan orcidid: 0000-0002-5794-0779 surname: Zhang fullname: Zhang, Mengxuan organization: Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi'an, China
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SubjectTerms	Algorithms Clustering Clustering algorithms complex network clustering Complex networks Decomposition discrete particle swarm dynamic decomposition Heuristic algorithms Linear programming Mathematical analysis Multiobjective optimization Optimization Particle swarm optimization
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Title	Multiobjective Complex Network Clustering Based on Dynamical Decomposition Particle Swarm Optimization
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