K-Means Algorithm Merged with Chaotic Grey Wolf Optimization Algorithm
The K-means clustering algorithm is sensitive to the location of the initial clustering center and is easy to trap into the local optimal.To overcome these disadvantages of the K-means algorithm,a K-means algorithm mer-ged with a chaotic grey wolf optimization algorithm is proposed.The features of chaotic randomness and ergodicity are applied to generate a uniformly distributed Tent chaotic sequence for initializing the grey wolf population,achieving u-niformly distributed initial solutions and high diversity of population,which can enhance the global search ability.In the process of searching cluster centers of the global optimum,elite-based mutation operator is applied to maintain the diversity of the population,which can enhance the local search ability and avoid trapping into the local optimal.Com-pared with K-means,PSO,and GWO,the experiment results show that the proposed algorithm has a better cluster effect and stronger optimization ability.
Grey wolf optimization algorithmTent chaotic sequenceGlobal optimalLocal optimalMutation
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