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To enhance the optimization ability of quantum potential well-based particle swarm optimization algorithm, the improved quantum potential well-based particle swarm optimization algorithms are proposed by analyzing the design process of current quantum potential well-based particle swarm optimization algorithms. Firstly, three improved quantum particle swarm optimization algorithms are proposed based on delta potential well, harmonic oscillator and square potential well, respectively, and then a statistic mean-based control parameter design method is presented for the proposed models. Secondly, to highlight the guiding role of the global optimal particle in designing potential well centers, two strategies are presented based on a weighted average of all self-optimal particles and dynamic random variables. The experimental results show that the performances of three improved algorithms are relatively close, the model based delta potential well are slightly better than the other two kinds of model, and the performances of three improved algorithms are superior to that of the original algorithm.
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Keywords:
- quantum computation /
- quantum potential well /
- particle swarm optimization /
- algorithm design
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[20] Zhang Z S 2010 Expert Syst. Appl. 37 1800
[21] [22] Lu S F, Sun S F, Lu Z D 2010 Energy Convers. Manage. 51 561
[23] [24] [25] Gao H, Xu W B, Sun J, Tang Y L 2010 IEEE Trans. Instrum. Meas. 59 934
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[30] Feng B, Xu W B 2004 IEEE Conference on Cybernetics and Intelligent Systems Singapore, December 13, 2004 p291
[31] [32] [33] Feng B, Xu W B 2004 IEEE Conference on Control, Automation, Robotics and Vision Kunming, December 69, 2004, China p1454
[34] Said M M, Ahmed A K 2006 IEEE Trans. Antennas Propagat. 54 2765
[35] [36] [37] Clerc M, Kennedy J 2002 IEEE Trans. Evol. Comput. 6 58
[38] [39] Zhao S Q, Zheng W 1999 Random Signal Analysis (Harbin: Harbin Institute of Technology Press) p54 (in Chinese)[赵淑清, 郑薇 1999 随机信号分析 (哈尔滨: 哈尔滨工业大学出版社) 第54页]
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[1] Kennedy J, Eberhart R C 1995 IEEE International Conference on Neural Networks Perth, Australian, November 27December 1, 1995 p1942
[2] Guo W Z, Chen G L 2011 J. Software 22 833 (in Chinese)[郭文忠, 陈国龙 2011 软件学报 22 833]
[3] [4] [5] Lin S W, Ying K C, Chen S C, Lee Z J 2008 Expert Syst. Appl. 35 1817
[6] [7] Cai X J, Cui Z H, Zeng J C, Tan Y 2008 Inf. Process. Lett. 105 231
[8] [9] Liu Y, Qin Z, Shi Z W, Lu J 2007 Neurocomputing 70 672
[10] Zhang Y J, Shao S F 2011 Pattern Recogni. Artif. Intell. 24 90 (in Chinese)[张英杰, 邵岁锋 2011 模式识别与人工智能 24 90]
[11] [12] Zhu H M, Wu Y P 2010 Control Decis. 25 20 (in Chinese)[朱海梅, 吴永萍 2010 控制与决策 25 20]
[13] [14] [15] Samrat L S, Leandro S C, Ajith A 2009 Microelectron. Reliab. 49 660
[16] Omkara S N, Khandelwala R, Ananthb T S 2009 Expert Syst. Appl. 36 11312
[17] [18] [19] Meng K, Wang H G, Dong Z Y 2010 IEEE Trans. Power Syst. 25 215
[20] Zhang Z S 2010 Expert Syst. Appl. 37 1800
[21] [22] Lu S F, Sun S F, Lu Z D 2010 Energy Convers. Manage. 51 561
[23] [24] [25] Gao H, Xu W B, Sun J, Tang Y L 2010 IEEE Trans. Instrum. Meas. 59 934
[26] Leandro S C 2010 Expert Syst. Appl. 37 1676
[27] [28] [29] Fang W, Sun J, Xie Z P, XuWB 2010 Acta Phys. Sin. 59 3686 (in Chinese)[方伟, 孙俊, 谢振平, 须文波 2010 物理学报 59 3686]
[30] Feng B, Xu W B 2004 IEEE Conference on Cybernetics and Intelligent Systems Singapore, December 13, 2004 p291
[31] [32] [33] Feng B, Xu W B 2004 IEEE Conference on Control, Automation, Robotics and Vision Kunming, December 69, 2004, China p1454
[34] Said M M, Ahmed A K 2006 IEEE Trans. Antennas Propagat. 54 2765
[35] [36] [37] Clerc M, Kennedy J 2002 IEEE Trans. Evol. Comput. 6 58
[38] [39] Zhao S Q, Zheng W 1999 Random Signal Analysis (Harbin: Harbin Institute of Technology Press) p54 (in Chinese)[赵淑清, 郑薇 1999 随机信号分析 (哈尔滨: 哈尔滨工业大学出版社) 第54页]
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