Anzeige
Erschienen in:
2011 | OriginalPaper | Buchkapitel
An Improved Genetic Algorithm for Network Nodes Clustering
verfasst von : Yong Li, Zhenwei Yu
Erschienen in: Information Computing and Applications
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Nodes clustering is a useful way to construct an effective network infrastructure for large-scale distributed network applications. In this paper, network nodes are clustered by the K-medoids clustering algorithm according to their coordinates. The coordinates of network nodes are gained by Vivaldi which is a simple and lightweight network coordinates system. But K-medoids algorithm is sensitive to the initial cluster centers and easy to get stuck at the local optimal solutions. In order to improve the performance of K-medoids algorithm, KCIGA(K-medoids clustering based on improved genetic algorithm) is presented in this paper. The improved genetic algorithm that uses self-adaptive genetic operator, dynamically adjusting the crossover rate and mutation rate, can avoid premature and slow convergence phenomenon in SGA(standard genetic algorithm). Experimental results show KCIGA has good reliability and expansibility, and it is effective for clustering network nodes.