已定型的含汉字数字的词语

第4期杨莉，等：基于T S模糊推理的自适应量子行为粒子优化算法45

中的参数做进一步的灵敏度分析，以便能够更加改进该算法性能。算法的机理与理论分析以及算法在其它优化问题中的应用等将是下一步深人研究的内容。

三自由度平台参考文献：

[1] Kennedy J, Eberhart R C, Shi Yuhui. Swarm Intelligence[M]. San Francisco：Morgan Kaufmann Publishers, CA, 2001.

[2] Kennedy J, Eberhart R C. Particle Swarmo Ptimization[C]// Proceedings of IEEE International Conference on Neural Net

北京市人口与计划生育条例修正案works. Piseataway, N J；IEEE Service Center, 1995；1942 - 1948.

[3] Frans V D B. An Analysis of Particle Swarm Optimizers[D]. Pretoria：University of Pretoria, 2001.

用心去工作读后感

[4] Riget J, Vesterstrom J S. A Diversity-Guided Particle Swarm Optimizer-the ARPSO[R]. Aarhus? Denmark：University of

Aarhus Department of Computer Science, 2002.

[5] Sun Jun, Feng Bin, Xu Wenbo. Particle Swarm Optimization with Particles Having Quantum Behavior[C]//Proceedings of

IEEE International Congress on Evolutionary Computation, 2004, 70(3) ：1571 - 1580.

[6] Shi Yuhui, Eberhart R. A Modified Particle Swarm Optimizer[C]//Proceedings of the IEEE International Conference on Evo

lutionary Computation, Piscataway, NJ ：IEEE Press, 1998 ：69 - 73.

[7]孙俊.量子行为粒子优化算法研究[D].无锡：江南大学，2009.

[8] Derrac J, Garcia S, Molina D, et al. A Practical Tutorial on the Use of Nonparametric Statistical Tests as a Methodology for

Comparing Evolutionary and Swarm Intelligence Algorithm s[J]. Swarm and Evolutionary Computation, 2011, 1(1)：3 - 18.

An Adaptive Quantum-Behaved Particle Swarm Optimization

Algorithm Using TS Fuzzy Inference Engine

YANG Li, ZHANG Jihui, ZHENG Weibo

金梅子

(Institute of Complexity Science, Qingdao University, Qingdao 266071, China)

Abstract：Particle Swarm Optimization (PSO) is a kind of Swarm Intelligence Optimization algorithm. Just as other intelligent optimization algorithms, a reasonable balance between exploration and exploitation is the key to guarantee the global search ability, and it is a focus of this field during recent years. In order to ensure the diversity of particle swarm in the search process, a new TS inference engine based Adaptive Quantum-behaved Particle Swarm Optimization (AQPSO) algorithm is proposed. The algorithm based on population diversity and the number of iterations for TS fuzzy inferencecan dynamically adjust its parameter and iteration method to ensure that the population search in a larger space. It also can reduce the probability of falling into a local optima. Then we simulate several test functions, and adopt the Wilcoxon signed rank test to show that AQPSO improved both QPSO algorithm and traditional PSO algorithm to some extent, especially for complex high-dimensional function optimization problems.

Key words：particle swarm optimization；TS fuzzy inference；population diversity；swarm intelligence酸类

g：cc C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C

保安服务条例J/小(j j n I 已定型的含汉字数字的词语

u n u^

;; 汉语中长期使用已经稳定下来的包含汉字数字形式的词语，应采用汉字数字。g ；! 示例：万一一律一旦三叶虫四书五经星期五相差十万八千里八国联军ii I I七上八下一心一意不管三七二十一一方面二百五半斤八两五省一市§!! 五讲四美五四运动八九不离十白发三千丈不二法门二八年华九死一生3

I “一 .二八”事变“一.二九”运动四氧化三铁i U^ y f l\ (jj n

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