期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

改进的小生境粒子群优化算法 被引量：2

Improved Niche Particle Swarm Optimization
下载PDF
导出
摘要 传统的小生境粒子群优化算法(NPSO)需要两个参数的输入,一个是判断子群合并的阈值,另一个是子群产生的阈值。参数设置的不当,将直接影响计算结果。引入一个函数判断两个点是否在同一座山峰上,以克服NPSO算法需要输入参数的弊端。在程序运行时,无须严格限定小生境的半径,也不需太多的先验知识。实验结果证明,该算法合理有效,能够能快速有效地找到多峰函数的全局最优点。 This algorithm overcomes the other existing algorithm defects ,which is dependent on the initialization parame‐ters and the slow convergence .The new algorithm uses the same hill function to judge whether the niching subswarms are merged and the particles are absorbed by the subswarms or not .It improves searching ability of multiple solutions and makes significant improvement in searching efficiency and convergence speed .
作者 李娜 黄治国
机构地区 中南民族大学计算机科学学院 河南工程学院软件学院
出处 《软件导刊》 2015年第2期45-47,共3页 Software Guide
基金 中央高校基本科研业务费专项资金项目(CZY13007)
关键词 小生境 NPSO算法 粒子群优化算法 多峰值函数 Niche Particle Swarm Optimization Multiple Solution
分类号 TP312 [自动化与计算机技术—计算机软件与理论]
  • 相关文献

参考文献7

  • 1CAVICCHIO D J. Adaptive search using simulated evolution[D]. Michigan, Arbor Illinois : University of Michigan, 1970.
  • 2DeJong K A. An analysis of the behavior of a class of genetic adap- tive systems[D]. Michigan: University of Michigan, 1975.
  • 3GOLDBERG D E, RicharDson J J. Genetic algorithms with sharing for muhimodal function optimization[C]. Proc of the 2rid Interna- tional Conference on Genetic Algorithms, 1987 : 27-36.
  • 4BRITS R, ENGELBRECHT A P, VAN DEN BERGH F. A nic- hing particle swarm optimizer[C]. Proc of the 4th Asia-Pacific Conf On Simulated Evolution and learning,2002:692-696.
  • 5王俊年,申群太,沈洪远,周鲜成.一种基于聚类的小生境微粒群算法[J].信息与控制,2005,34(6):680-684. 被引量：9
  • 6R K URSEM. Multinational evolutionary algorithms[C]. Washing- ton:Proceedings of Congress of Evolutionary Computation, 1999 (3) : 1633-1640.
  • 7S W. MAHFOUD. A comparison of parallel and sequential niching methods[C]. California:Proceedings of the Sixth International Con- ference on Genetic Algorithms, 1995 : 136-143.

二级参考文献14

  • 1Trelea l C. The particle swarm optimization: convergence analysis and parameter selection [ J]. Information Processing Letters,2003, 85(6) : 317 -325.
  • 2Clerc M, Kennedy J. The particle swarm : explosion, stability,and convergence in a multi-dimensional complex space [ J ].IEEE Transactions on Evolutionary Computation, 2004, 8 ( 6 ) :58-73.
  • 3Bergh F V D. Particle swarm weight initialization in multi-layer neural networks [ A ]. Development and Practice of Artificial Intelligence Techniques [ M]. Durban, South Africa: IAAMSAD,1999. 41-45.
  • 4Bergh F V D, Engelbrecht A P. Training product unit networks using cooperative particle swarm optimizers [ A]. Proceedings of the International Joint Conference on Neural Networks [ C]. Piscaraway, USA: IEEE, 2001. 126-131.
  • 5Yoshida H, Kawata K. Fukuyama Y, et al. A particle swarm optimization for reactive power and voltage control considering voltage security assessment [J], IEEE Transactions on Power Systents, 2000, 15(4): 1232-1239.
  • 6Parsopoulos K E, Plagianakos V P, Magoulas G D, et al. Stretching technique for obtaining global minimizers through particle swarm optimization [ A]. Proceedings of the Workshop on Particle Swarm Optimization [ C], Indianapolis: IUPUI Press, 2001.22 - 29.
  • 7Parsopoulos K E,Vrahatis M N. On the computation of all global minimizers through particle swarm optimization [ J ]. IEEE Transactions on EvolutionalT Computation, 2004, 8(6) : 211-224.
  • 8Brits R, Engelbrcbt A P, van den Bergh F. Solving systems of unconstrained equations using particle swarm optimization [ A ].Proceedings of the IEEE International Conference on Systems,Man and Cybernetics[C]. Piscataway, USA: IEEE, 2002. 100-105.
  • 9Ester M, Kfiegel H P, Sander J, et al. Density-based clustering in spatial databases: the algorithm GDBSCAN and its applications [J]. Data Mining and Knowledge Discovery, 1998, 2(2) :169 - 194.
  • 10Streichert F, Stein G, Ulmer H, et al. A clustering based niching EA for mulfimodal search spaces [A]. Proceedings of the 6th International Conference on Artificial Evolution [ C ]. Berlin,C, ennany: Spfinger-Verlag, 2003. 293 - 304.

共引文献8

同被引文献31

  • 1刘洪波,王秀坤,谭国真.粒子群优化算法的收敛性分析及其混沌改进算法[J].控制与决策,2006,21(6):636-640. 被引量：62
  • 2胡旺,李志蜀.一种更简化而高效的粒子群优化算法[J].软件学报,2007,18(4):861-868. 被引量：334
  • 3向长城,黄席樾,杨祖元,杨欣.小生境粒子群优化算法[J].计算机工程与应用,2007,43(15):41-43. 被引量：24
  • 4J Kennedy,R C Eberhart.Particle swarm optimization[J].Proc IEEE Int Conf Neural Networks,1995(4):1942-1948.
  • 5SETAYESHh M,ZHANG Mengjie,JOHNSTON M.A novel particle swarm optimization approach to detecting continuous,thin and smooth edges in noisy images[J].Information Sciences,2013,246:28-51.
  • 6CALAZAN R M,NEDJAH N,MOURELLE L M.A hardware accelerator for particle swarm optimization[J].Applied Soft Computing,2014,14:347-356.
  • 7ECHEVARR L C,SANTIAGO O L,FAJARDO J A H,et al.A variant of the particle swarm optimization for the improvement of fault diagnosis in industrial systems via faults estimation[J].Engineering Applications of Artificial Intelligence,2014,212:1-16.
  • 8SHI Y,EBERHART R C.Fuzzy Adaptive Particle Swarm Optimization[C]//Proceedings of the Congress on Evolutionary Computation,Seoul,Korea,2001:101-106.
  • 9SHI Y,EBERHART R C.Empirical Study of Particle Swarm Optimization[C]//Proc IEEE Congr Evol Comput,1999:1945-1950.
  • 10CHATTERJEE A,SIARRY P.Nonlinear Inertia Weight Variation for Dynamic Adaption in Particle Swarm Optimization[J].Comput Oper Res,2006,33:859-871.

引证文献2

二级引证文献9

软件导刊
2015年 第2期

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部