Self-Adaptive Particle Swarm Optimization Algorithm with Mutation Operation Based on K-Means

Xue Mei Wang; Yi Zhuo  Guo; Gui Jun Liu

doi:10.4028/www.scientific.net/AMR.760-762.2194

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 760-762Self-Adaptive Particle Swarm Optimization...

Self-Adaptive Particle Swarm Optimization Algorithm with Mutation Operation Based on K-Means

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.

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Periodical:

Advanced Materials Research (Volumes 760-762)

Pages:

2194-2198

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.760-762.2194

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Online since:

September 2013

Authors:

Xue Mei Wang, Yi Zhuo Guo, Gui Jun Liu

Keywords:

K-Means Cluster Algorithm, Mutation, Particle Swarm Optimization (PSO)

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References

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