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.
Highlights
Network analysis is an important topic in computer science and bioinformatics researches[1,2,3]
We propose a novel algorithm, Multi-Objective Evolutionary Algorithm based on Decomposition and Membrane structure (MOEA/DM), for community detection based on an evolutionary algorithm
This study introduced an algorithm that combines membrane structure and an evolutionary algorithm, MOEA/ DM
Summary
Network Community detection based on the graph. A network is usually expressed as a graph structure, G = G (N, V), where N represents nodes and V represents the relationships between the network’s nodes. If there is no point X ∈Ω such that X dominates X*, X* is a Pareto optimal solution. We assume the reason for this is there may be a number of sub problems corresponding to the same non-dominated solutions. We propose a MOEA/DM algorithm to reduce the number of sub problems and improve the probability that the solution is not the same for each sub-problem and corresponding optimal. A value of j, assigned to the ith gene, is interpreted as a link between the nodes i and j, and, in the resulting clustering solution, the nodes are in the same cluster The decoding of this representation requires the identification of all connected components.
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