Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)

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

Authors
Xue-mei Wang, Yi-zhuo Guo, Gui-jun Liu
Corresponding Author
Xue-mei Wang
Available Online March 2013.
DOI
https://doi.org/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.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Cite this article

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  - https://doi.org/10.2991/iccsee.2013.748
ID  - Wang2013/03
ER  -