一种基于改进差分进化的K-均值聚类算法研究
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摘要
针对K-均值算法的差异与缺点,对初始值敏感,易于落入局部最优解,差异进化算法具有强大的全局收敛能力和鲁棒性,但其收敛速度较慢。鉴于上述问题和缺陷,论文首先详细介绍了进化算法关键操作和差分进化算法的步骤和具体流程。然后,阐述了基于差分进化的K-均值聚类算法的描述,步骤和具体流程。最后,提出基于改进差分进化的K均值聚类算法,详细介绍改进方案,改进算法的步骤和具体流程。基于差分进化和改进算法的K均值聚类算法进行仿真实验,实验结果表明,该算法具有较好的搜索能力,算法收敛速度更快,鲁棒性更强。
The difference and the shortcomings of K-means algorithm are sensitive to the initial value and easily fall into the local optimal solution. The difference evolution algorithm has strong global convergence ability and robustness,but its convergence rate is slow. In view of the above problems and defects,this paper first introduces the steps and the concrete process of evolutionary algorithm key operation and differential evolution algorithm. Then,the description,steps and concrete flow of K-means clustering algorithm based on differential evolution are described. Finally,a K-means clustering algorithm based on improved differential evolution is proposed,and the improvement scheme,the steps and the concrete flow of the algorithm are introduced in detail. The K-means clustering algorithm based on differential evolution and improved algorithm is used to simulate the experiment. The experimental results show that the algorithm has better searching ability,and the algorithm is faster and more robust.
The difference and the shortcomings of K-means algorithm are sensitive to the initial value and easily fall into the local optimal solution. The difference evolution algorithm has strong global convergence ability and robustness,but its convergence rate is slow. In view of the above problems and defects,this paper first introduces the steps and the concrete process of evolutionary algorithm key operation and differential evolution algorithm. Then,the description,steps and concrete flow of K-means clustering algorithm based on differential evolution are described. Finally,a K-means clustering algorithm based on improved differential evolution is proposed,and the improvement scheme,the steps and the concrete flow of the algorithm are introduced in detail. The K-means clustering algorithm based on differential evolution and improved algorithm is used to simulate the experiment. The experimental results show that the algorithm has better searching ability,and the algorithm is faster and more robust.
引文
[1]高平,毛力,宋宜春.基于改进差分进化的K-均值聚类算法[J].电脑知识与技术,2013,9(22):5064-5067.GAO Ping,MAO Li,SONG Yichun.K-means clustering algorithm based on improved differential evolution[J].Computer Knowledge and Technology,2013,9(22):5064-5067.
[2]董明刚,王宁,程小辉.改进的组合差分进化优化算法[J].计算机仿真,2013,30(1):389-392.DONG Minggang,WANG Ning,CHENG Xiaohui.Improved combinatorial differential evolution optimization algorithm[J].Computer Simulation,2013,30(1):389-392.
[3]欧陈委.K-均值聚类算法的研究与改进[D].长沙:长沙理工大学,2011:34-45.OU Chenwei.Research and Improvement of K-Means Clustering Algorithm[D].Changsha:Changsha University of Science and Technology,2011:34-45.
[4]王雪梅,李晓峰,高巍巍.一种改进的K-means聚类算法研究[J].计算机与数字工程,2013,41(11):1717-1719,1759.WANG Xuemei,LI Xiaofeng,GAO Weiwei.An Improved K-means Clustering Algorithm[J].Computer and digital engineering,2013,41(11):1717-1719,1759.
[5]Stom R,Price K.Differential Evolution:A simple and efficient heuristic for global optimization over continuous spaces[J].Journal of Global Optimization,1997(11):341-359.
[6]Paterlini S,Krink T.High performance clustering with differential evolution[C]//Proc.of Evolutionary Computation,2004.CEC2004.Congress on Volume 2,19-23 June 2004Page(s):2004-2011 Vo1.2.
[7]Sudhakar G.Effective image clustering with differential evolution technique[J].International Journal of Computer and Communication Technology,2010,2(1):11-19.
[8]刘莉莉.K_均值聚类算法的研究与改进[D].曲阜:曲阜师范大学,2015:35-46.LIU Lili.Study and Improvement of K_Mean Clustering Algorithm[D].Qufu:Qufu Normal University,2015:35-46.
[9]姜立强,强洪夫.带基向量种群的改进差分进化算法[J].计算机工程,2012,38(3):9-11.JIANG Liqiang,QIANG Hongfu.Improved differential evolution algorithm with base vector population[J].Computer Engineering,2012,38(3):9-11.
[10]唐亚.差分进化算法的改进及其在聚类中的应用[D].广州:广东工业大学,2016:23-35.TANG Ya.Improvement of differential evolution algorithm and its application in clustering[D].Guangzhou:Guangdong University of Technology,2016:23-35.
[11]Sudhakar G.Effective image clustering with differential evolution technique[J].International Journal of Computer and Communication Technology,2010,2(1):11-19.
[12]Chou C H,Su M C,Lai E.A new cluster validity measuer and its application to image compression[J].Pattern Analysis and Applications,2004,7(2):205-220.
[13]Sudhakar G.Effective image clustering with differential evolution technique[J].International Journal of Computer and Communication Technology,2010,2(1):11-19.
[14]Storn R,Price K.Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces[J].Journal of Global Optimization,1997,11(4):341-359.
[15]Sheng W,Chen S,Fairhurst M,et al.Multilocal Search and Adaptive Niching Based Memetic Algorithm With a Consensus Criterion for Data Clustering[J].Evolutionary Computation,IEEE Transactions on,2014,18(5):721-741.
[2]董明刚,王宁,程小辉.改进的组合差分进化优化算法[J].计算机仿真,2013,30(1):389-392.DONG Minggang,WANG Ning,CHENG Xiaohui.Improved combinatorial differential evolution optimization algorithm[J].Computer Simulation,2013,30(1):389-392.
[3]欧陈委.K-均值聚类算法的研究与改进[D].长沙:长沙理工大学,2011:34-45.OU Chenwei.Research and Improvement of K-Means Clustering Algorithm[D].Changsha:Changsha University of Science and Technology,2011:34-45.
[4]王雪梅,李晓峰,高巍巍.一种改进的K-means聚类算法研究[J].计算机与数字工程,2013,41(11):1717-1719,1759.WANG Xuemei,LI Xiaofeng,GAO Weiwei.An Improved K-means Clustering Algorithm[J].Computer and digital engineering,2013,41(11):1717-1719,1759.
[5]Stom R,Price K.Differential Evolution:A simple and efficient heuristic for global optimization over continuous spaces[J].Journal of Global Optimization,1997(11):341-359.
[6]Paterlini S,Krink T.High performance clustering with differential evolution[C]//Proc.of Evolutionary Computation,2004.CEC2004.Congress on Volume 2,19-23 June 2004Page(s):2004-2011 Vo1.2.
[7]Sudhakar G.Effective image clustering with differential evolution technique[J].International Journal of Computer and Communication Technology,2010,2(1):11-19.
[8]刘莉莉.K_均值聚类算法的研究与改进[D].曲阜:曲阜师范大学,2015:35-46.LIU Lili.Study and Improvement of K_Mean Clustering Algorithm[D].Qufu:Qufu Normal University,2015:35-46.
[9]姜立强,强洪夫.带基向量种群的改进差分进化算法[J].计算机工程,2012,38(3):9-11.JIANG Liqiang,QIANG Hongfu.Improved differential evolution algorithm with base vector population[J].Computer Engineering,2012,38(3):9-11.
[10]唐亚.差分进化算法的改进及其在聚类中的应用[D].广州:广东工业大学,2016:23-35.TANG Ya.Improvement of differential evolution algorithm and its application in clustering[D].Guangzhou:Guangdong University of Technology,2016:23-35.
[11]Sudhakar G.Effective image clustering with differential evolution technique[J].International Journal of Computer and Communication Technology,2010,2(1):11-19.
[12]Chou C H,Su M C,Lai E.A new cluster validity measuer and its application to image compression[J].Pattern Analysis and Applications,2004,7(2):205-220.
[13]Sudhakar G.Effective image clustering with differential evolution technique[J].International Journal of Computer and Communication Technology,2010,2(1):11-19.
[14]Storn R,Price K.Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces[J].Journal of Global Optimization,1997,11(4):341-359.
[15]Sheng W,Chen S,Fairhurst M,et al.Multilocal Search and Adaptive Niching Based Memetic Algorithm With a Consensus Criterion for Data Clustering[J].Evolutionary Computation,IEEE Transactions on,2014,18(5):721-741.
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