Mountain Clustering Based on Improved PSO Algorithm
SHEN HongYuan1,2, PENG XiaoQi1, WANG JunNian2,3, HU ZhiKun3
1.Institute of Energy and Power Engineering, Central South University, Changsha 410083 2.Institute of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan 411201 3.Institute of Information Science and Engineering, Central South University, Changsha 410083
摘要 对微粒群算法进行改进,使之具有多峰寻优能力,并将其与山峰聚类法相结合,提出了基于改进的微粒群优化算法的山峰聚类算法(A Mountain Clustering Based on Improved PSO,MCBIPSO).文中给出了该方法的原理和计算步骤.仿真结果表明,该算法物理意义明确,用于基于密度的样本聚类时,计算简单快捷,能有效搜寻到数据样本空间的各个聚类中心,从而实现对数据样本的准确聚类.
Abstract：The PSO (particle swarm optimization) algorithm is reformed so that it can be used in multimodel function optimization. The improved PSO is combined with a mountain clustering method. A mountain clustering based on improved PSO (MCBIPSO) algorithm is presented. The principle and steps are supplied in this paper. The simulation results show that the mechanism of the MCBIPSO algorithm is definite. When the MCBIPSO algorithm is used in clustering based on density, the calculation is easier and more efficient in deciding the clustering centers of data samples. The MCBIPSO algorithm can realize accuracy clustering based on the samples density.
[1] Hand D J, et al. Principles of Data Mining. Boston, USA: MIT Press, 2001 (Hand D J, et al,著;张银奎,廖 丽,宋 俊,等,译.数据挖掘原理.北京:机械工业出版社,2003) [2] Jain A K, Dubes R C. A1gorithms for Clustering Data. Englewood Cliffs, USA: Prentice Hall, 1988 [3] Xing X S, Jiao L C. Clustering Method in the Field of Data Mining. Journal of Circuits and Systems, 2003, 8(1): 59-67 (in Chinese) (行小帅,焦李成.数据挖掘的聚类方法.电路与系统学报,2003, 8(1): 59-67) [4] Yager R R, Filve D P. Generation of Fuzzy Rules by Mountain Clustering. Journal of Intelligent and Fuzzy Systems, 1994, 2(3): 209-219 [5] Yager R R, Filve D P. Essentials of Fuzzy Modeling and Control. New York, USA: John Wiley & Sons, 1994 [6] Chiu S L. Fuzzy Model Identification Based on Cluster Estimation. Journal of Intelligent and Fuzzy System, 1994, 2(3): 267-278 [7] Vose M D. The Simple Genetic Algorithm: Foundations and Theory. Boston, USA: MIT Press, 1999 [8] Li M Q, Kou J S, Ling D, et al. The Foundations Theory and Applications of Genetic Algorithms. Beijing, China: Science Press, 2002 (in Chinese) (李敏强,寇纪淞,林 丹,等.遗传算法的基本理论与应用.北京:科学出版社,2002) [9] Kennedy J, Eberhart R. Particle Swarm Optimization. In: Proc of the IEEE International Conference on Neural Networks. Perth, Australia, 1995, 1942-1948 [10] Eberhart R C, Kennedy J. A New Optimizer Using Particle Swarm Theory. In: Proc of the 6th International Symposium on Micro Machine and Human Science. Nagoya, Japan, 1995, 39-43 [11] Xie X F, Zhang W J, Yang Z L. Overview of Particle Swarm Optimization. Control and Decision, 2003, 18(2): 129-134 (in Chinese) (谢晓锋,张文俊,杨之廉.微粒群算法综述.控制与决策,2003, 18(2):129-134) [12] Peng Y, Peng X Y, Liu Z Q. Static Analysis on Parameter Efficiency of Particle Swarm Optimization. Acta Electronica Sinica, 2004, 32(2): 209-213 (in Chinese) (彭 宇,彭喜元,刘兆庆.微粒群算法参数效能的统计分析.电子学报, 2004, 32(2): 209-213) [13] Zhang J H, Gao Q S, Xu X H. A Self-Adaptive Ant Colony Algorithm. Control Theory and Applications, 2000, 17(1): 1-3 (in Chinese) (张纪会,高齐圣,徐心和.自适应蚁群算法.控制理论与应用, 2000, 17(1): 1-3) [14] Ma L. Ant Algorithm Based Function Optimization. Control and Decision, 2002, 17(Supplement): 719-726 (in Chinese) (马 良.基于蚂蚁算法的函数优化.控制与决策, 2002, 17(增刊): 719-726) [15] DasGupta D. Artificial Immune Systems and Their Applications. Berlin, Germany: Springer-Verlag, 1999 [16] Ge H, Mao Z Y. Research on Parameters of Immune Algorithm. Journal of South China University of Technology (Natural Science Edition), 2002, 30(12): 15-18 (in Chinese) (葛 红,毛宗源.免疫算法几个参数的研究.华南理工大学学报(自然科学版), 2002, 30(12): 15-18) [17] Ge H, Mao Z Y. Realization of Immune Algorithm. Computer Engineering, 2003, 29(5): 62-63 (in Chinese) (葛 红,毛宗源,免疫算法的实现.计算机工程, 2003, 29(5): 62-63) [18] Tmmis J. Artificial Immune Systems: A Novel Data Analysis Technique Inspired by the Immune Network Theory. Ph.D Dissertation. Department of Computer Science, University of Walse, Aberystwyth, UK, 2000 [19] van den Berg F. An Analysis of Particle Swarm Optimizers. Ph.D Dissertation. Department of Natural and Agricultural Science, University of Pretoria, Pretoria, South Africa, 2001 [20] Brits R, Engelbrecht A P, van den Bergh F. A Niching Particle Swarm Optimizer. In: Proc of the Asia-Pacific Conerence on Simulated Evolution and Learning. Sigapore, Sigapore, 2002, 692-696 [21] Trelea I C. The Particle Swarm Optimization Algorithm: Convergence Analysis and Parameter Selection. Information Processing Letters, 2003, 85(6): 317-325
2006, Vol. 19 
