一种基于改进差分进化的K-Means聚类算法研究OA北大核心CSTPCD

In order to improve the instability and low efficiency of clustering results caused by randomly selecting initial cluster centers in traditional K-Means clustering algorithms,a K-Means clustering algorithm based on adaptive guided differential evolution(AGDE-KM)is proposed.The adaptive operation operator is designed to improve the global search capability of the algorithm in the early stage and the convergence speed in the later stage.The multi-variation strategy is designed and the weight coefficient is introduced to play the advantages of different variation strategies in different evolutionary stages of the algorithm,balance the global and local search ability of the algorithm,and accelerate the convergence speed of the algorithm.A Gaussian perturbation crossover operation based on the best individual of the current population is proposed to provide a better evolutionary direction for the individual while maintaining the population diversity in"dimension",so as to avoid the algorithm from falling into local optimal.The optimal solution when the algorithm stops execution is used as the initial cluster center to replace the randomly selected cluster center of traditional K-Means.The comparative experiment of the proposed algorithm on the Vowel,Iris,and Glass datasets and the synthetic dataset Jcdx in the UCI public database are conducted.The results show that,in comparison with traditional K-Means,the sum of squared errors(SSE)is reduced by 5.65%,19.59%,13.31%,and 6.1%,respectively,and the clustering time is reduced by 83.03%,76.48%,77.47%,and 92.63%,respectively.The experimental results show that the proposed improved algorithm has faster convergence speed and better optimization finding ability,which can significantly improve the effectiveness,efficiency and stability of clustering.