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Halton-type sequences from global function fields 被引量：1

Halton-type sequences from global function fields
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摘要 For any prime power q and any dimension s≥1, a new construction of (t, s)-sequences in base q using global function fields is presented. The construction yields an analog of Halton sequences for global function fields. It is the first general construction of (t, s)-sequences that is not directly based on the digital method. The construction can also be put into the framework of the theory of (u, e, s)-sequences that was recently introduced by Tezuka and leads in this way to better discrepancy bounds for the constructed sequences. For any prime power q and any dimension s ≥ 1, a new construction of （t, s）-sequences in base q using global function fields is presented. The construction yields an analog of Halton sequences for global function fields. It is the first general construction of （t, s）-sequences that is not directly based on the digital method. The construction can also be put into the framework of the theory of （u, e, s）-sequences that was recently introduced by Tezuka and leads in this way to better discrepancy bounds for the constructed sequences.
作者 NIED ERREITER Harald YEO Anderson SiangJing
机构地区 Johann Radon Institute for Computational and Applied Mathematics Department of Mathematics School of Physical and Mathematical Sciences
出处 《Science China Mathematics》 SCIE 2013年第7期1467-1476,共10页 中国科学：数学（英文版）
关键词 low-discrepancy sequence （t s）-sequence Halton sequence global function field 构造序列 函数域 素数幂 数字化 体结构
分类号 O174 [理学—基础数学]
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参考文献19

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  • 1刘逸.粒子群优化算法的改进及应用研究[D].西安:西安电子科技大学,2013.
  • 2GARNIER S, GAUTRAIS J, THERAULAZ G. The biological principles of swarm intelligence [ J ]. Swarm intelligence, 2007, 1(1) :3 -31.
  • 3FENG M Y, PAN H. A modified PSO algorithm based on cache replacement algorithm[ C]//Tenth International Conference on Computational Intelligence and Security. Kunming, 2014:558 - 562.
  • 4LANGDON W B, POLI R. Evolving problems to learn about particle swarm and other optimisers [ C]//IEEE Congress on Evo- lutionary Computation. Edinburgh,2005:561 - 578.
  • 5HIGASHI N, IBA H. Particle swarm optimization with Gaussian mutation [ C ]//Proceedings of the IEEE Swarm Intelligence Symposium. Indianapolis, 2003 : 72 - 79.
  • 6MIRANDA V, FONSECA N. EPSO: evolutionary particle swarm optimization, a new algorithm with applications in power sys- tems[ C ]//Transmission and Distribution Conference and Exhibition. Yokohama, 2002:2491 -2505.
  • 7YANG H F, YANG Z Y, YANG Y, et al. An improved particle swarm optimization algorithm based on simulated annealing [ C l//10th International Conference on Natural Computation. Xiamen, 2014:529 -533.
  • 8BERGH F, ENGELBRECHT A P. A cooperative approach to particle swarm optimization [ J ]. IEEE transaction on evolutionary computation, 2004,8 ( 3 ) : 225 - 239.
  • 9HANG J J, QIN A K, SUGANTHAN P N, et al. Comprehensive learning particle swarm optimizer for global optimization of mul- timodal functions [ J]. IEEE transaction on evolutionary computation, 2006, 10(3) : 281 - 295.
  • 10吕丹,童创明,钟卫军.基于粒子群和模拟退火算法的混合算法研究[J].计算机工程与设计,2011,32(2):663-666. 被引量：12

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Science China Mathematics
2013年 第7期

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