Self-adaptive Particle Swarm Optimization Algorithm with Mutation Operation based on K-means
- 10.2991/iccsee.2013.748How to use a DOI?
- k-means cluster algorithm,Particle Swarm Optimization, mutation,
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.
- © 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/).
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 - 10.2991/iccsee.2013.748 ID - Wang2013/03 ER -