Mobile Clustering Agents based on Differential Evolution

Author

Xiyu Liu ; Ma, Yinghong ; Jiang, Liandi

Author_Institution

Sch. of Manage. & Econ., Shandong Normal Univ., Jinan

Volume

1

fYear

2008

fDate

6-8 Oct. 2008

Firstpage

9

Lastpage

12

Abstract

Searching is an important procedure in optimization problems. As is an effective clustering method especially in spatial data mining, the role of searching is essential. While many searching methods focus themselves on particle swarm optimization and genetic algorithms, we propose a new searching algorithm based differential evolution (DE). It proves that DE is a simple optimization algorithm effective for real-valued problems. A simple convergence analysis with a design of experimental model are presented.

Keywords

data mining; genetic algorithms; mobile agents; particle swarm optimisation; pattern clustering; search problems; differential evolution; genetic algorithm; mobile clustering agent; particle swarm optimization; searching algorithm; spatial data mining; Clustering algorithms; Clustering methods; Convergence; Data mining; Genetic mutations; Inspection; Mobile agents; Partitioning algorithms; Power generation economics; Topology; Differential evolution; cluster analysis; mobile Agent; tangent space;

fLanguage

English

Publisher

ieee

Conference_Titel

Pervasive Computing and Applications, 2008. ICPCA 2008. Third International Conference on

Conference_Location

Alexandria

Print_ISBN

978-1-4244-2020-9

Electronic_ISBN

978-1-4244-2021-6

Type

conf

DOI

10.1109/ICPCA.2008.4783656

Filename

4783656