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群体智能优化中的虚拟碰撞:雨林算法

高维尚 邵诚 高琴

群体智能优化中的虚拟碰撞:雨林算法

高维尚, 邵诚, 高琴
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  • 启发式优化算法中寻优代理过早收敛易陷入局部最优. 本文对此进行机理分析并发现, 虚拟碰撞作为一种隐性过早收敛现象将直接影响群体智能优化算法的准确性与快速性, 而采样过程的无约束性和样本分布信息的缺失是导致虚拟碰撞的根本原因. 为解决上述问题, 本文提出雨林优化算法. 该算法仿照植物生长模式, 利用规模可变种群代替规模限定种群进行分区分级寻优采样, 并结合均匀与非均匀采样原则来权衡优化算法的探索与挖掘, 可以有效减少虚拟碰撞的发生, 在提高寻优效率的同时, 获取精准性和稳定性较高的全局最优解. 与遗传算法、粒子群算法对标称函数的寻优对比实验表明, 雨林算法在快速性、准确性以及泛化能力等方面均具有优势.
    • 基金项目: 国家自然科学基金(批准号:61074020)资助的课题
    [1]

    Beni G, Wang J 1989 Proceedings of NATO Advanced Workshop on Robots and Biological Systems Tuscany, Italy, June 26-30, 1989 p703

    [2]

    Engelbrecht A, Li X, Middendorf M, Gambardella L M 2009 IEEE Transactions on Evolutionary Computation 13 677

    [3]

    Smith A E 2000 IEEE Transactions on Evolutionary Computation 4 192

    [4]

    Zu Y X, Zhou J, Zeng C C 2010 Chin. Phys. B 19 119501

    [5]

    Wang T T, Li W L, Chen Z H, Miao L 2010 Chin. Phys. B 19 76401

    [6]

    Gao F, Li Z Q, Tong H Q 2008 Chin. Phys. B 17 1196

    [7]

    Zhao Z J, Zheng S L, Xu C Y, Kong X Z 2007 Chin. Phys. 16 1619

    [8]

    Liu X M, Li Y H 2005 Chin. Phys. Lett. 22 1927

    [9]

    Zhu S F, Liu F, Chai Z Y, Qi Y T, Wu J S 2012 Acta Phys. Sin. 61 96401 (in Chinese) [朱思峰, 刘芳, 柴争义, 戚玉涛, 吴建设 2012 物理学报 61 96401]

    [10]

    Kennedy J, Eberhart R 1995 Proceedings of IEEE International Conference on Neural Networks Perth, WA, November 27-December 01, 1995 p1942

    [11]

    Guo Y C, Hu L L, Ding Y 2012 Acta Phys. Sin. 61 54304 (in Chinese) [郭业才, 胡苓苓, 丁锐 2012 物理学报 61 54304]

    [12]

    Wang D F, Han P 2006 Acta Phys. Sin. 55 1644 [王东风, 韩璞 2006 物理学报 55 1644]

    [13]

    Gao F, Tong H Q 2006 Acta Phys. Sin. 55 577 [高飞, 童恒庆 2006 物理学报 55 577]

    [14]

    Chen W N, Zhang J, Lin Y, Chen N, Zhan Z H, Chung H, Li Y, Shi Y 2012 IEEE Transactions on Evolutionary Computation 17 241

    [15]

    Blackwell T 2012 IEEE Transactions on Evolutionary Computation 16 354

    [16]

    Li X, Yao X 2012 IEEE Transactions on Evolutionary Computation 16 210

    [17]

    Zhan Z H, Zhang J, Li Y, Shi Y H 2011 IEEE Transactions on Evolutionary Computation 15 832

    [18]

    van den Bergh F, Engelbrecht A P 2004 IEEE Transactions on Evolutionary Computation 8 225

    [19]

    Li X 2010 IEEE Transactions on Evolutionary Computation 14 150

    [20]

    Shi Y, Eberhart R C 1998 Proceedings of Evolutionary Programming San Diego, California, USA, March 25-27, 1998 p591

    [21]

    Trelea I C 2003 Information Processing Letters 85 317

    [22]

    Montes De Oca M A, Stutzle T, Birattari M, Dorigo M 2009 IEEE Transactions on Evolutionary Computation 13 1120

    [23]

    Poli R 2008 Journal of Artificial Evolution and Applications 2008 1

    [24]

    Iqbal M, Montes De Oca M A 2006 Proceedings of the fifth international workshop on ant colony optimization and swarm intelligence Brussels, Belgium, September 4-7, 2006 p72

    [25]

    Xie X F, Zhang W J, Yang Z L 2002 IEEE 2002 International Conference on Communications, Circuits and Systems and West Sino Expositions Chengdu, China, June 29-July 1, 2002 p1170

    [26]

    Zhang J, Hung H S, Lo W L 2007 IEEE Transactions on Evolutionary Computation 11 326

    [27]

    Naznin F, Sarker R, Essam D 2012 IEEE Transactions on Evolutionary Computation 16 615

    [28]

    Poli R, Kennedy J, Blackwell T 2007 Swarm Intelligence Journal 1 33

    [29]

    Poli R 2009 IEEE Transactions on Evolutionary Computation 13 712

    [30]

    Macnish C 2007 Connection Science 19 361

    [31]

    Yang Q, Ding S C 2007 Computer Engineering and Applications 43 80

    [32]

    Ronkkonen J, Li X D, Kyrki V, Lampinen J 2011 Soft Computing 15 1689

    [33]

    Vural R A, Yildirim T, Kadioglu T, Basargan A 2012 IEEE Transactions on Evolutionary Computation 16 135

  • [1]

    Beni G, Wang J 1989 Proceedings of NATO Advanced Workshop on Robots and Biological Systems Tuscany, Italy, June 26-30, 1989 p703

    [2]

    Engelbrecht A, Li X, Middendorf M, Gambardella L M 2009 IEEE Transactions on Evolutionary Computation 13 677

    [3]

    Smith A E 2000 IEEE Transactions on Evolutionary Computation 4 192

    [4]

    Zu Y X, Zhou J, Zeng C C 2010 Chin. Phys. B 19 119501

    [5]

    Wang T T, Li W L, Chen Z H, Miao L 2010 Chin. Phys. B 19 76401

    [6]

    Gao F, Li Z Q, Tong H Q 2008 Chin. Phys. B 17 1196

    [7]

    Zhao Z J, Zheng S L, Xu C Y, Kong X Z 2007 Chin. Phys. 16 1619

    [8]

    Liu X M, Li Y H 2005 Chin. Phys. Lett. 22 1927

    [9]

    Zhu S F, Liu F, Chai Z Y, Qi Y T, Wu J S 2012 Acta Phys. Sin. 61 96401 (in Chinese) [朱思峰, 刘芳, 柴争义, 戚玉涛, 吴建设 2012 物理学报 61 96401]

    [10]

    Kennedy J, Eberhart R 1995 Proceedings of IEEE International Conference on Neural Networks Perth, WA, November 27-December 01, 1995 p1942

    [11]

    Guo Y C, Hu L L, Ding Y 2012 Acta Phys. Sin. 61 54304 (in Chinese) [郭业才, 胡苓苓, 丁锐 2012 物理学报 61 54304]

    [12]

    Wang D F, Han P 2006 Acta Phys. Sin. 55 1644 [王东风, 韩璞 2006 物理学报 55 1644]

    [13]

    Gao F, Tong H Q 2006 Acta Phys. Sin. 55 577 [高飞, 童恒庆 2006 物理学报 55 577]

    [14]

    Chen W N, Zhang J, Lin Y, Chen N, Zhan Z H, Chung H, Li Y, Shi Y 2012 IEEE Transactions on Evolutionary Computation 17 241

    [15]

    Blackwell T 2012 IEEE Transactions on Evolutionary Computation 16 354

    [16]

    Li X, Yao X 2012 IEEE Transactions on Evolutionary Computation 16 210

    [17]

    Zhan Z H, Zhang J, Li Y, Shi Y H 2011 IEEE Transactions on Evolutionary Computation 15 832

    [18]

    van den Bergh F, Engelbrecht A P 2004 IEEE Transactions on Evolutionary Computation 8 225

    [19]

    Li X 2010 IEEE Transactions on Evolutionary Computation 14 150

    [20]

    Shi Y, Eberhart R C 1998 Proceedings of Evolutionary Programming San Diego, California, USA, March 25-27, 1998 p591

    [21]

    Trelea I C 2003 Information Processing Letters 85 317

    [22]

    Montes De Oca M A, Stutzle T, Birattari M, Dorigo M 2009 IEEE Transactions on Evolutionary Computation 13 1120

    [23]

    Poli R 2008 Journal of Artificial Evolution and Applications 2008 1

    [24]

    Iqbal M, Montes De Oca M A 2006 Proceedings of the fifth international workshop on ant colony optimization and swarm intelligence Brussels, Belgium, September 4-7, 2006 p72

    [25]

    Xie X F, Zhang W J, Yang Z L 2002 IEEE 2002 International Conference on Communications, Circuits and Systems and West Sino Expositions Chengdu, China, June 29-July 1, 2002 p1170

    [26]

    Zhang J, Hung H S, Lo W L 2007 IEEE Transactions on Evolutionary Computation 11 326

    [27]

    Naznin F, Sarker R, Essam D 2012 IEEE Transactions on Evolutionary Computation 16 615

    [28]

    Poli R, Kennedy J, Blackwell T 2007 Swarm Intelligence Journal 1 33

    [29]

    Poli R 2009 IEEE Transactions on Evolutionary Computation 13 712

    [30]

    Macnish C 2007 Connection Science 19 361

    [31]

    Yang Q, Ding S C 2007 Computer Engineering and Applications 43 80

    [32]

    Ronkkonen J, Li X D, Kyrki V, Lampinen J 2011 Soft Computing 15 1689

    [33]

    Vural R A, Yildirim T, Kadioglu T, Basargan A 2012 IEEE Transactions on Evolutionary Computation 16 135

  • 引用本文:
    Citation:
计量
  • 文章访问数:  1779
  • PDF下载量:  529
  • 被引次数: 0
出版历程
  • 收稿日期:  2013-01-04
  • 修回日期:  2013-07-02
  • 刊出日期:  2013-10-05

群体智能优化中的虚拟碰撞:雨林算法

  • 1. 大连理工大学控制科学与工程学院, 大连 116024;
  • 2. 大连理工大学先进控制技术研究所, 大连 116024
    基金项目: 

    国家自然科学基金(批准号:61074020)资助的课题

摘要: 启发式优化算法中寻优代理过早收敛易陷入局部最优. 本文对此进行机理分析并发现, 虚拟碰撞作为一种隐性过早收敛现象将直接影响群体智能优化算法的准确性与快速性, 而采样过程的无约束性和样本分布信息的缺失是导致虚拟碰撞的根本原因. 为解决上述问题, 本文提出雨林优化算法. 该算法仿照植物生长模式, 利用规模可变种群代替规模限定种群进行分区分级寻优采样, 并结合均匀与非均匀采样原则来权衡优化算法的探索与挖掘, 可以有效减少虚拟碰撞的发生, 在提高寻优效率的同时, 获取精准性和稳定性较高的全局最优解. 与遗传算法、粒子群算法对标称函数的寻优对比实验表明, 雨林算法在快速性、准确性以及泛化能力等方面均具有优势.

English Abstract

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