Fuzzy Clustering Algorithm Based on Improved Particle Swarm Optimization and its Application

Xue Yong Li; Jia Xia Sun; Jun Hui Fu; Guo Hong Gao

doi:10.4028/www.scientific.net/AMM.44-47.4067

Paper Titles

Education of Master of Engineering via Constructivist Perspective
p.4050

Study of Control Algorithm for Indirect Stabilized Optical Seeker
p.4053

A Novel Cyber Physical Systems Model
p.4058

Review on Research and Progress of Corrosion Inhibitors
p.4063

Fuzzy Clustering Algorithm Based on Improved Particle Swarm Optimization and its Application
p.4067

A Low Power Monitoring and Tracking System for Multi-Container Based on the Improved LEACH
p.4072

On the Evaluation Method of Wavelet Analysis for Surface Roughness
p.4077

The Think Aloud Experiment of the Designing Process of an Expert
p.4081

Testing Study on Wear Mechanism of Fluororubber by 45# Steel under Different Speed in Oil Medium
p.4084

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 44-47Fuzzy Clustering Algorithm Based on Improved...

Fuzzy Clustering Algorithm Based on Improved Particle Swarm Optimization and its Application

Abstract:

A fuzzy clustering algorithm based on improved particle swarm optimization was proposed in this paper. First reduce dimension of solution space, separate it into smaller solution space. In separated solution space, use of improved particle swarm optimization algorithm to search the sub-optimal solution as a chromosome of whole particle,use improved PSO to search global optimal solution. The particle solve the problem that swarm algorithm easy to fall into local optimal solution in high dimensional space, and the problem that the fuzzy clustering algorithm is sensitive to initial value problems. Simulation results show the effectiveness of this algorithm.

You might also be interested in these eBooks

Frontiers of Manufacturing and Design Science

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 44-47)

Pages:

4067-4071

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.44-47.4067

Citation:

Cite this paper

Online since:

December 2010

Authors:

Xue Yong Li, Jia Xia Sun, Jun Hui Fu, Guo Hong Gao

Keywords:

Clustering, Fuzzy C-Means (FCM), Particle Swarm Optimization Algorithm (PSO)

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Eberhart RC, Shi Y. Particle swarm optimization: developments, applications and resources[C]/ Proc. Congress on Evolutionary Computation 2001. Piscataway , NJ: IEEEE Press, 2001: 81-86.

DOI: 10.1109/cec.2001.934374

Google Scholar

[2] Xu Gang, Qu Jinping, Yang Zhitao. An Improved Adaptive Particle Swarm Optimization Algorithm [J]. Journal of South China University of Technology(Natural Science Edition). 2008, (36)9, 6-10.

Google Scholar

[3] Nobuhiro Iwasaki, Keiichiro Yasuda. Dynamic parametertuning of particle swarm op timization [ J ]. IEEJ Trans onElectrical and Electronic Engineering, 2006, 1 (4) : 353-363.

Google Scholar

[4] Li Dan, Gu Hong, Zhang Liyong. A fuzzy c-means clustering algorithm based on nearest-neighbor intervals for incomplete data [J]. Expert Systems with Apllications. 2010 (37)6942-6947.

DOI: 10.1016/j.eswa.2010.03.028

Google Scholar

[5] XIE X L, BEN I G. A validity measure for fuzzy clustering[J ]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1991, 13 (8) : 841 - 847.

DOI: 10.1109/34.85677

Google Scholar

[6] Ye Jian, Wu Yuexian, Ge Lindong. Study on efficient adaptive clustering algorithm based on modulation identification [J]. Computer Engineering Design, 2007, 28 (3): 506 - 508.

Google Scholar

[7] KDD Cup 1999 Data [EB/OL]. (1999). http: /www. kdd. ics. uci. edu/databases/kddcup. html.

Google Scholar