Self-adaptive Particle Swarm Optimization Algorithm with Mutation Operation based on K-means

DOI

10.2991/iccsee.2013.748How to use a DOI?

Keywords

k-means cluster algorithm,Particle Swarm Optimization, mutation,

Abstract

Adaptive Particle Swarm Optimization algorithm with mutation operation based on K-means is proposed in this paper, this algorithm Combined the local searching optimization ability of K-means with the gobal searching optimization ability of Particle Swarm Optimization, the algorithm self-adaptively adjusted inertia weight according to fitness variance of population. Mutation operation was peocessed for the poor performative particle in population. The results showed that the algorithm had solved the poblems of slow convergence speed of traditional Particle Swarm Optimization algorithm and easy falling into the local optimum of K-Means, and more effectively improved clustering quality.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Xue-mei Wang
AU  - Yi-zhuo Guo
AU  - Gui-jun Liu
PY  - 2013/03
DA  - 2013/03
TI  - Self-adaptive Particle Swarm Optimization Algorithm with Mutation Operation based on K-means
BT  - Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)
PB  - Atlantis Press
SP  - 3114
EP  - 3117
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccsee.2013.748
DO  - 10.2991/iccsee.2013.748
ID  - Wang2013/03
ER  -