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Complex Network Clustering by a Multi-objective Evolutionary Algorithm Based on Decomposition and Membrane Structure

View Article: PubMed Central - PubMed

ABSTRACT

The field of complex network clustering is gaining considerable attention in recent years. In this study, a multi-objective evolutionary algorithm based on membranes is proposed to solve the network clustering problem. Population are divided into different membrane structures on average. The evolutionary algorithm is carried out in the membrane structures. The population are eliminated by the vector of membranes. In the proposed method, two evaluation objectives termed as Kernel J-means and Ratio Cut are to be minimized. Extensive experimental studies comparison with state-of-the-art algorithms proves that the proposed algorithm is effective and promising.

No MeSH data available.


Comparison of three algorithms in time cost.
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f8: Comparison of three algorithms in time cost.

Mentions: Table 3 shows that, in terms of the Q value, MOEA/DM’s performance is the same as that for MOPSO, and both (MOEA/DM and MOPSO), in terms of Q value, perform better than MOEA/D. However, as indicated in Table 3 and shown graphically in Fig. 8, in terms of running time, MOEA/DM has the advantage that its running time is substantially less than half that of MOPSO.


Complex Network Clustering by a Multi-objective Evolutionary Algorithm Based on Decomposition and Membrane Structure
Comparison of three algorithms in time cost.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5037381&req=5

f8: Comparison of three algorithms in time cost.
Mentions: Table 3 shows that, in terms of the Q value, MOEA/DM’s performance is the same as that for MOPSO, and both (MOEA/DM and MOPSO), in terms of Q value, perform better than MOEA/D. However, as indicated in Table 3 and shown graphically in Fig. 8, in terms of running time, MOEA/DM has the advantage that its running time is substantially less than half that of MOPSO.

View Article: PubMed Central - PubMed

ABSTRACT

The field of complex network clustering is gaining considerable attention in recent years. In this study, a multi-objective evolutionary algorithm based on membranes is proposed to solve the network clustering problem. Population are divided into different membrane structures on average. The evolutionary algorithm is carried out in the membrane structures. The population are eliminated by the vector of membranes. In the proposed method, two evaluation objectives termed as Kernel J-means and Ratio Cut are to be minimized. Extensive experimental studies comparison with state-of-the-art algorithms proves that the proposed algorithm is effective and promising.

No MeSH data available.