Journal Article10.1109/TEVC.2013.2260862
Complex Network Clustering by Multiobjective Discrete Particle Swarm Optimization Based on Decomposition
TL;DR: Based on the proposed discrete framework, a multiobjective discrete particle swarm optimization algorithm is proposed to solve the network clustering problem and the decomposition mechanism is adopted.
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Abstract: The field of complex network clustering has been very active in the past several years. In this paper, a discrete framework of the particle swarm optimization algorithm is proposed. Based on the proposed discrete framework, a multiobjective discrete particle swarm optimization algorithm is proposed to solve the network clustering problem. The decomposition mechanism is adopted. A problem-specific population initialization method based on label propagation and a turbulence operator are introduced. In the proposed method, two evaluation objectives termed as kernel k-means and ratio cut are to be minimized. However, the two objectives can only be used to handle unsigned networks. In order to deal with signed networks, they have been extended to the signed version. The clustering performances of the proposed algorithm have been validated on signed networks and unsigned networks. Extensive experimental studies compared with ten state-of-the-art approaches prove that the proposed algorithm is effective and promising.
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Citations
A Comprehensive Review of Swarm Optimization Algorithms
TL;DR: In this paper, the authors provide an in-depth survey of well-known swarm optimization algorithms and compare them with each other comprehensively through experiments conducted using thirty wellknown benchmark functions and a number of statistical tests are then carried out to determine the significant performances.
A comprehensive review on swarm optimization algorithms
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TL;DR: A novel MOPSO algorithm using multiple search strategies (MMOPSO), where decomposition approach is exploited for transforming MOPs into a set of aggregation problems and then each particle is assigned accordingly to optimize each aggregation problem.
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