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

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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.

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Crossover of solution.
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f4: Crossover of solution.

Mentions: We choose the two-point crossover, in favor of uniform crossover, because the two-point crossover better maintains effective node connections in the network. Given two parents, A and B, we first randomly select two points i and j (1 ≤ i ≤ j ≤ N), and then everything between the two points is swapped between the parents (i.e. ,  ∀ k ∈ {k/i ≤ k ≤ j). An example of the operation of two-point crossover on encoding is shown in Fig. 4.


Complex Network Clustering by a Multi-objective Evolutionary Algorithm Based on Decomposition and Membrane Structure
Crossover of solution.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f4: Crossover of solution.
Mentions: We choose the two-point crossover, in favor of uniform crossover, because the two-point crossover better maintains effective node connections in the network. Given two parents, A and B, we first randomly select two points i and j (1 ≤ i ≤ j ≤ N), and then everything between the two points is swapped between the parents (i.e. ,  ∀ k ∈ {k/i ≤ k ≤ j). An example of the operation of two-point crossover on encoding is shown in Fig. 4.

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.