期刊文献+

工作集划分算法求解多维函数极小值

Minimum of Multidimensional Function Solved by Working Set Partitioning Algorithm
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摘要 通过对工作集元素贡献度算法研究,对工作集中个体元素按照其贡献度的不同将工作集划分3个子工作集即优、良、劣工作集。采用保留优工作集中个体元素,将其作为下一次迭代的个体元素。根据优工作集元素产生新的良工作集元素和良工作集元素产生新的劣工作集元素,保障置换后的新工作集能够快速收敛。通过对4个标准集多维测试函数仿真,表明算法能有效、快速、准确收敛。 Excellent,good and bad subsets of working set were divided according to the contribution of individual element.Elements in the excellent subset were retained as the individual elements of the next iteration.Successively,elements of the newly-built good working set produced the excellent working set and those of the original good working set compose the new bad working set,which can ensure the fast convergence of the replaced working set.The simulation result of four standard test functions show that the algorithm can converge effectively,quickly and accurately.
出处 《辽东学院学报(自然科学版)》 CAS 2012年第4期264-267,共4页 Journal of Eastern Liaoning University:Natural Science Edition
基金 安徽省教育厅优秀青年基金重点项目(2011SQRL117ZD) 安徽科技学院青年科学研究基金项目(ZRC2011273) 安徽科技学院引进人才基金资助项目(ZRC2010255)
关键词 优工作集 良工作集 劣工作集 置换 择优保留 贡献度 excellent working set good working set bad working set replacement preferential retainment contribution
作者简介 葛华(1976-),男,江苏沭阳人,硕士,讲师,研究方向:智能计算、Web数据挖掘。
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参考文献8

  • 1STALLINGS W. Operating systems:internals and designprinciples[M].New Jersey:Prentice Hall Inter-national Inc,2005.
  • 2SRIIVAS N,NAFPLIOTIS N. Multiobjective optimizationusing nodminated sorting in genetic algorithms[J].Evolu-tionary Computation,1994,(03):221-248.
  • 3DEB K,AGRAWAL S,PRATAP A. A fast elitistnondominated sorting genetic algorithm for muti-objectoptimization.NSGA-II[A].Berlin:Berlin Springer,2000.
  • 4ZITZLER E,THIELE L. Muliobjective evolutionary algo-rithms:a comparative case study and the strength paretoapproach[J].IEEE Transactions on Evolutionary Compu-tation,1999,(04):257-271.
  • 5曾志强,吴群,廖备水,朱顺痣.改进工作集选择策略的序贯最小优化算法[J].计算机研究与发展,2009,46(11):1925-1933. 被引量:5
  • 6贾丙静,王传安,王亚军,吴长勤.Web日志挖掘中模糊C均值聚类研究[J].辽东学院学报(自然科学版),2011,18(1):28-30. 被引量:2
  • 7段海滨;张祥银;徐春芳.仿生智能计算[M]北京:科学出版社,2011.
  • 8毕晓君,王艳娇.改进人工蜂群算法[J].哈尔滨工程大学学报,2012,33(1):117-123. 被引量:48

二级参考文献22

  • 1李颖新,阮晓钢.基于支持向量机的肿瘤分类特征基因选取[J].计算机研究与发展,2005,42(10):1796-1801. 被引量:51
  • 2MOBASHER B.Web usage mining and personalization[M].Baton Rouge:Chapman Hall & CRC Press,2003:456-510.
  • 3KARABOGA D, BASTURK B. On the performance of arti- ficial bee colony (ABC) algorithm [ J ]. Applied Soft Com- puting, 2008,8 ( 1 ) :687-697.
  • 4PENEV K, LITTLEFAIR G , Free Search--a comparative analysis[ J ]. Information Sciences, 2005,172 ( 1 ) : 173- 193.
  • 5RAHNAMAYAN S, TIZHOOSH H R, SALAMA M M A.opposition-Based differential evolution [ J ]. IEEE Transac- tions on Evolutionary Computation , 2008,12 ( 1 ) :64-79.
  • 6KARBOGA D, BASTURK B. A powerful and efficient algo- rithm for numerical function optimization: artificial bee colo- ny (ABC) algorithm [ J ]. Journal of Global Optimization, 2007,39 ( 3 ) :459 -471.
  • 7TIAHOOSH H R. Opposition-based learning: a new scheme for machine intelligence [ C ]//Proceedings of International Computational Intelligence for Modeling Control and Auto- mation. Sydney, Australia, 2005 : 695-701.
  • 8LIU Xingbao, CAI Zixing. Artificial bee colony program- ming made faster[ J ]. Natural Computation, 2009 (8) : 14- 16.
  • 9JANEZ B, SASO G, BORKO B. Self-adapting control pa- rameters in differential evolution: a comparative study on numerical benchmark pro-blems[ J]. IEEE Transactions on Evolutionary Comp-utation, 2006,12 (10) :646-657.
  • 10RAHNAMAYAN S, TIZItOOSH H R, SALAMA M M A. Opposition versus randomness in soft computing techniques[J]. Applied Soft Computing, 2008, 8 (2) : 906-918.

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