Abstract
Moth Swarm Algorithm (MSA) is a new swarm intelligent algorithm, it is inspired by the moth looking for food, phototaxis and celestial navigation in the dark environment, proposed a moth search algorithm. Because the algorithm has good convergence speed and high convergence precision, it is applied in many fields. Cluster analysis, as an effective tool in data mining, has attracted widespread attention and has been developed rapidly and has been successfully applied in recent years. Among the many clustering algorithms, the K-means clustering algorithm is easy to implement, so it is widely used. However, the K-means algorithm also has the disadvantages of large computational complexity and clustering effect depending on the selection of the initial clustering center, which seriously affects the clustering effect, and the algorithm is easy to fall into the local optimum. To solve these problems, The MSA is applied to cluster analysis, the results show that the MSA not only achieves superior accuracy, but also exhibits a higher level of stability.
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This work is supported by National Science Foundation of China under Grant Nos. 61563008, and 61463007, and Project of Guangxi Natural Science Foundation under Grant No. 2016GXNSFAA380264.
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Yang, X., Luo, Q., Zhang, J., Wu, X., Zhou, Y. (2017). Moth Swarm Algorithm for Clustering Analysis. In: Huang, DS., Hussain, A., Han, K., Gromiha, M. (eds) Intelligent Computing Methodologies. ICIC 2017. Lecture Notes in Computer Science(), vol 10363. Springer, Cham. https://doi.org/10.1007/978-3-319-63315-2_44
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DOI: https://doi.org/10.1007/978-3-319-63315-2_44
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