广西师范大学学报(自然科学版) ›› 2013, Vol. 31 ›› Issue (3): 72-80.

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侦察蜂在人工蜂群算法中的作用

张超群1,2, 郑建国1, 李陶深3   

  1. 1.东华大学旭日工商管理学院,上海200051;
    2.广西民族大学信息科学与工程学院,广西南宁530006;
    3.广西大学计算机与电子信息学院,广西南宁530004
  • 收稿日期:2013-04-20 出版日期:2013-09-20 发布日期:2018-11-26
  • 通讯作者: 张超群(1974—),女,广西罗城人,广西民族大学副教授,东华大学博士研究生。E-mail:chaozi0771@163.com
  • 基金资助:
    国家自然科学基金资助项目(70971020);广西混杂计算与集成电路设计分析重点实验室开放基金资助项目(2012HCI09);广西民族大学重点科研资助项目(2012MDZD035)

Effect of Scout Bees on the Performance of Artificial Bee Colony Algorithm

ZHANG Chao-qun1,2, ZHENG Jian-guo1, LI Tao-shen3   

  1. 1.Glorious Sun School of Business and Management,Donghua University,Shanghai 200051,China;
    2.College of Information Science and Engineering,Guangxi University for Nationalities,Guangxi Nanning 530006,China;
    3.College of Computer and Electronics Information,Guangxi University,Guangxi Nanning 530004,China
  • Received:2013-04-20 Online:2013-09-20 Published:2018-11-26

摘要: 人工蜂群算法(artificial bee colony algorithm,ABC)是一种模仿蜜蜂采蜜行为的新兴的群智能优化技术。侦察蜂作为人工蜂群的成员之一,进行随机搜索以找到蜜源。为了弄清楚侦察蜂在ABC中的作用,本文首先分析ABC的生物学机理和主要处理步骤,然后研究当问题维数、种群规模、limit值和最大循环次数等4个控制参数取不同值时对无侦察蜂ABC、单侦察蜂ABC与多侦察蜂ABC性能的影响。实验结果表明,在绝大多数情况下,多侦察蜂ABC求解5个著名的基准函数获得的解优于单侦察蜂ABC和无侦察蜂ABC,而单侦察蜂ABC获得的解优于无侦察蜂ABC。此外,由于这3种算法的搜索复杂度是同阶的,在相同条件下其运行时间相差不大,这充分说明了侦察蜂实施随机勘探过程确实对ABC的性能具有积极意义。

关键词: 人工蜂群算法, 侦察蜂, 勘探过程

Abstract: Artificial Bee Colony (ABC) algorithm is a new swarm intelligence technique inspired by the foraging behavior of a honeybee swarm.As the member of the artificial bee colony,scout bees carry out random search for discovering food sources.In order to investigate the effect of scout bees on the performance of ABC,the biological mechanism and main steps of ABC were analyzed,and then,different problem dimensions,population sizes,limit values and maximum cycle numbers were tested on the performance of ABC under the conditions of no scout bee,single scout bee and multi-scout bees conditions.Almost all the experimental results show that ABC with multi-scout bees outperforms ABC with single scout bee and ABC without scout bee on five well-known benchmark functions,meanwhile,ABC with single scout bee performs better than ABC without scout bee.Besides,the three algorithms have almost the same execution time under the same conditions due to the same order of their search complexity.These fully demonstrate that the random exploration process adopted by scout bees has positive effect on the performance of ABC.

Key words: artificial bee colony algorithm, scout bee, exploration process

中图分类号: 

  • TP301.6
[1] KARABOGA N,LATIFOGLU F.Adaptive filtering noisy transcranial Doppler signal by using artificial bee colony algorithm[J].Engineering Applications of Artificial Intelligence,2013,26(2):677-684.
[2] KARABOGA D.An idea based on honey bee swarm for numerical optimization[R].Turkey:Erciyes University,2005.
[3] KARABOGA D,AKAY B.A comparative study of artificial bee colony algorithm[J].Applied Mathematics and Computation,2009,214(1):108-132.
[4] AKAY B,KARABOGA D.A modified artificial bee colony algorithm for real-parameter optimization[J].Information Sciences,2012,192(1):120-142.
[5] KARABOGA D,BASTURK B.A powerful and efficient algorithm for numerical function optimization:artificial bee colony (ABC) algorithm[J].Journal of Global Optimization,2007,39(3):459-471.
[6] KARABOGA D,BASTURK B.On the performance of artificial bee colony(ABC) algorithm[J].Applied Soft Computing,2008,8(1):687-697.
[7] KARABOGA N.A new design method based on artificial bee colony algorithm for digital IIR filters[J].Journal of the Franklin Institute,2009,346(4):328-348.
[8] SONMEZ M.Artificial bee colony algorithm for optimization of truss structures[J].Applied Soft Computing,2011,11(2):2406-2418.
[9] RAO R V,SAVSANI V J,VAKHARIA D P.Teaching-learning-based optimization:an optimization method for continuous non-linear large scale problems[J].Information Sciences,2012,183(1):1-15.
[10] WU Bin,QIAN Cun-hua,NI Wei-hong,et al.The improvement of glowworm swarm optimization of continuous optimization problems[J].Expert systems with applications,2012,39(7):6335-6342.
[11] CHANG Wei-der.Nonlinear CSTR control system design using an artificial bee colony algorithm[J].Simulation Modelling Practice and Theory,2013,31(2):1-9.
[12] 林晓宇,钟一文,王爱荣.趋药性人工蜂群算法训练神经网络研究[J].广西师范大学学报:自然科学版,2011,29(3):120-124.
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