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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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Solution structure of MOEA/DM algorithm.
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f2: Solution structure of MOEA/DM algorithm.

Mentions: To alleviate the disadvantages of single objective algorithms, multi-objective optimization is applied to the problem. A large number of multi-objective optimization evolutionary algorithms have been developed, which can be potentially effective and helpful for solving the problem181920. Therefore, using multi-objective optimization algorithms to solve the community detection problem has become a significant subject2122. In 2007, Zhang and Li192021 proposed an algorithm, a Multi-Objective Evolutionary Algorithm based on Decomposition MOEA/D). However, research on MOEA/D has revealed that some, but not all, solutions are chosen in several sub-problems (Fig. 1), which may result in loss of population diversity. To address the sub-problems, we designed a new Multi-Objective Evolutionary Algorithm, where each sub-problem has several solutions (Fig. 2).


Complex Network Clustering by a Multi-objective Evolutionary Algorithm Based on Decomposition and Membrane Structure
Solution structure of MOEA/DM algorithm.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f2: Solution structure of MOEA/DM algorithm.
Mentions: To alleviate the disadvantages of single objective algorithms, multi-objective optimization is applied to the problem. A large number of multi-objective optimization evolutionary algorithms have been developed, which can be potentially effective and helpful for solving the problem181920. Therefore, using multi-objective optimization algorithms to solve the community detection problem has become a significant subject2122. In 2007, Zhang and Li192021 proposed an algorithm, a Multi-Objective Evolutionary Algorithm based on Decomposition MOEA/D). However, research on MOEA/D has revealed that some, but not all, solutions are chosen in several sub-problems (Fig. 1), which may result in loss of population diversity. To address the sub-problems, we designed a new Multi-Objective Evolutionary Algorithm, where each sub-problem has several solutions (Fig. 2).

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.