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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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8985308/ |
| _version_ | 1818910129689460736 |
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| author | Tiaokang Gao Bin Cao Mengxuan Zhang |
| author_facet | Tiaokang Gao Bin Cao Mengxuan Zhang |
| author_sort | Tiaokang Gao |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-19T22:37:54Z |
| format | Article |
| id | doaj.art-a4b009a6d34040ce8cabb869f405dd80 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T22:37:54Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a4b009a6d34040ce8cabb869f405dd802022-12-21T20:03:09ZengIEEEIEEE Access2169-35362020-01-018323413235210.1109/ACCESS.2020.29721238985308Multiobjective Complex Network Clustering Based on Dynamical Decomposition Particle Swarm OptimizationTiaokang Gao0https://orcid.org/0000-0002-4394-2001Bin Cao1https://orcid.org/0000-0003-4558-9501Mengxuan Zhang2https://orcid.org/0000-0002-5794-0779School of Artificial Intelligence, Hebei University of Technology, Tianjin, ChinaSchool of Artificial Intelligence, Hebei University of Technology, Tianjin, ChinaKey Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi’an, ChinaClustering 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.https://ieeexplore.ieee.org/document/8985308/Multiobjective optimizationcomplex network clusteringdiscrete particle swarmdynamic decomposition |
| spellingShingle | Tiaokang Gao Bin Cao Mengxuan Zhang Multiobjective Complex Network Clustering Based on Dynamical Decomposition Particle Swarm Optimization IEEE Access Multiobjective optimization complex network clustering discrete particle swarm dynamic decomposition |
| title | Multiobjective Complex Network Clustering Based on Dynamical Decomposition Particle Swarm Optimization |
| title_full | Multiobjective Complex Network Clustering Based on Dynamical Decomposition Particle Swarm Optimization |
| title_fullStr | Multiobjective Complex Network Clustering Based on Dynamical Decomposition Particle Swarm Optimization |
| title_full_unstemmed | Multiobjective Complex Network Clustering Based on Dynamical Decomposition Particle Swarm Optimization |
| title_short | Multiobjective Complex Network Clustering Based on Dynamical Decomposition Particle Swarm Optimization |
| title_sort | multiobjective complex network clustering based on dynamical decomposition particle swarm optimization |
| topic | Multiobjective optimization complex network clustering discrete particle swarm dynamic decomposition |
| url | https://ieeexplore.ieee.org/document/8985308/ |
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