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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Bibliographic Details
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
Online Access:Get full text
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Summary: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.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.2972123