全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用：99美元
投递稿件

查看量下载量

相关文章

更多...

Tent混沌人工蜂群与粒子群混合算法

DOI: 10.13195/j.kzyjc.2014.0750, PP. 839-847

Keywords: Tent,混沌搜索,人工蜂群算法,粒子群优化算法,混沌反向学习,重组算子

Full-Text   Cite this paper   Add to My Lib

Abstract:

针对人工蜂群和粒子群算法的优势与缺陷,提出一种Tent混沌人工蜂群粒子群混合算法.首先利用Tent混沌反向学习策略初始化种群;然后划分双子群,利用Tent混沌人工蜂群算法和粒子群算法协同进化;最后应用重组算子选择最优个体作为跟随蜂的邻域蜜源和粒子群的全局极值.仿真结果表明,该算法不仅能有效避免早熟收敛,而且能有效跳出局部极值,与其他最新人工蜂群和粒子群算法相比具有较强的全局搜索能力和局部搜索能力.

 References

[1]  Karaboga D. An idea based on honey bee swarm for numerical optimization[R]. Kayseri: Erciyes University, 2005.
[2]  Akay B. Performance analysis of artificial bee colony algorithm on numerical optimization problems[D]. Kayseri: Graduate School of Natural and Applied Sciences, Erciyes University, 2009.
[3]  Akay B, Karaboga D. A modified artificial bee colony algorithm for real-parameter optimization[J]. Information Sciences, 2012, 192(6): 120-142.
[4]  高卫峰, 刘三阳, 黄玲玲. 受启发的人工蜂群算法在全局优化问题中的应用[J]. 电子学报, 2012, 40(12): 2396-2403.
[5]  (Gao W F, Liu S Y, Huang L L. Inspired artificial bee colony algorithm for global optimization problems[J]. Acta Electronica Sinica, 2012, 40(12): 2396-2403.)
[6]  Zhu G P, Sam K. Gbestguided artificial bee colony algorithm for numerical function optimization[J]. Applied Mathematics and Computation, 2010, 217(7): 3166-3173.
[7]  暴励, 曾建潮. 自适应搜索空间的混沌蜂群算法[J]. 计算机应用研究, 2010, 27(4): 1330-1334.
[8]  (Bao L, Zeng J C. Self-adapting search space chaos artificial bee colony algorithm[J]. Application Research of Computers, 2010, 27(4): 1330-1334.)
[9]  Alatas B. Chaotic bee colony algorithms for global numerical optimization[J]. Expert Systems with Applications, 2010, 37(8): 5682-5687.
[10]  Kennedy J, Eberhart R C. Particle swarm optimization[C]. Proc of IEEE Int Conf on Neural Networks. Piscataway, 1995: 1942-1948.
[11]  陶新民, 刘福荣, 刘玉, 等. 一种多尺度协同变异的粒子群优化算法[J]. 软件学报, 2012, 23(7): 1805-1815.
[12]  (Tao X M, Liu F R, Liu Y, et al. Multi-scale cooperative mutation particle swarm optimization algorithm[J]. J of Software, 2012, 23(7): 1805-1815.)
[13]  吴晓军, 杨战中, 赵明. 均匀搜索粒子群算法[J]. 电子学报, 2011, 39(6): 1261-1266.
[14]  (Wu X J, Yang Z Z, Zhao M. A uniform searching particle swarm optimization algorithm[J]. Acta Electronica Sinica, 2011, 39(6): 1261-1266.)
[15]  周新宇, 吴志健, 王晖, 等. 一种精英反向学习的粒子群优化算法[J]. 电子学报, 2013, 41(8): 1647-1652.
[16]  (Zhou X Y, Wu Z J, Wang H, et al. Elite opposition-based particle swarm optimization[J]. Acta Electronica Sinica, 2013, 41(8): 1647-1652.)
[17]  Jia D L, Zheng G X, Qu B Y, et al. A hybrid particle swarm optimization algorithm for high-dimensional problems[J]. Computers and Industrial Engineering, 2011, 61(4): 1117-1122.
[18]  Shi X H, Li Y W, Li H J, et al. An integrated algorithm based on artificial bee colony and particle swarm optimization[C]. Int Conf on Natural Computation. Yantai: IEEE Press, 2010: 2586-2590.
[19]  El-Abd M. A hybrid ABC-SPSO algorithm for continuous function optimization[C]. IEEE Symposium on Swarm Intelligence(SIS). Paris: IEEE Press, 2011: 1-6.
[20]  单梁, 强浩, 李军, 等. 基于Tent 映射的混沌优化算法[J].控制与决策, 2005, 20(2): 179-182.
[21]  (Shan L, Qiang H, Li J, et al. Chaotic optimization algorithm based on Tent map[J]. Control and Decision, 2005, 20(2): 179-182.)
[22]  Bao L, Zeng J C. Comparison and analysis of the selection mechanism in the artificial bee colony algorithm[C]. Int Conf on Hybrid Intelligent Systems. Shenyang: IEEE Press, 2009: 411-416.
[23]  Gao W F, Liu S Y. Improved artificial bee colony for global optimization[J]. Information Processing Letters, 2011, 111(17): 871-882.
[24]  Yan X H, Zhu Y L, ZouWP, et al. A new approach for data clustering using hybrid artificial bee colony algorithm[J]. Neurocomputing, 2012, 97(11): 241-250.
[25]  刘三阳, 张平, 朱明敏. 基于局部搜索的人工蜂群算法[J]. 控制与决策, 2014, 29(1): 123-128.
[26]  (Liu S Y, Zhang P, Zhu M M. Artificial bee colony algorithm based on local search[J]. Control and Decision, 2014, 29(1): 123-128.)

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133