计算机科学 ›› 2013, Vol. 40 ›› Issue (8): 210-213.

• 人工智能 • 上一篇    下一篇

改进的蜂群算法及其在CBD选址规划中的应用

张鹏,刘弘,刘鹏   

  1. 山东师范大学信息科学与工程学院 济南250014 山东师范大学山东省分布式计算机软件新技术重点实验室 济南250358;山东师范大学信息科学与工程学院 济南250014 山东师范大学山东省分布式计算机软件新技术重点实验室 济南250358;山东师范大学信息科学与工程学院 济南250014 山东师范大学山东省分布式计算机软件新技术重点实验室 济南250358
  • 出版日期:2018-11-16 发布日期:2018-11-16
  • 基金资助:
    本文受国家自然科学基金(60970004,61272094),国家教育部博士点基金(20093704110002),山东省自然科学基金(ZZ2008G02,ZR2010QL01),山东省高等学校科技计划项目(J11LG32),山东省分布式计算机软件新技术重点实验室基金资助

Improved Artificial Bee Colony Algorithm and its Application in CBD Location Planing

ZHANG Peng,LIU Hong and LIU Peng   

  • Online:2018-11-16 Published:2018-11-16

摘要: 中央商务区(CBD)是城市现代化的象征与标志,是城市的功能核心。选址规划是CBD开发建设的第一步,其合理性关系到开发的成败。手动CBD选址规划不仅效率低,且数据精度差。采用智能优化算法替代手动分析进行选址规划不仅降低了CBD建设的成本,更有助于提高规划建设的效率和精度。针对该问题,对原始蜂群算法进行改进,提出了基于可达性评价的新型算法NABC,并将其应用于CBD选址规划中进行微观实验仿真。实验结果表明,该算法克服了原始算法收敛速度慢的缺陷,提高了CBD选址的智能性和准确性。

关键词: 中央商务区,群体智能,人工蜂群算法,引领蜂,可达性

Abstract: Central Business District is the functional core of the city which reflects its modernization.The development of CBD depends much on the reasonableness of location planning.Location planning for CBD by manual analysis has low efficiency and accuracy of data.Using intelligent optimization algorithm for location planning can not only reduce the costing,but also improve efficiency and accuracy in construction of CBD.Aiming at this question,this paper improved original ABC algorithm and proposed a new method(NABC)based on evaluation of accessibility.Microscopic simulation experiment was made for CBD location planning by taking advantage of this method.This model overcomes the defect of convergence speed compared with original algorithm and improves the intelligence and accuracy of CBD construction according to the simulation experiment.

Key words: Center business district,Swarm intelligence,Artificial bee colony algorithm,Employed foragers,Accessibility

[1] 刘含,罗谦.论我国中央商务区建设发展-以成都为例[J].贵州大学学报,2011,28(2):122-126
[2] 丁成日,谢欣梅.城市中央商务区(CBD)发展的国际比较[J].城市发展研究,2010,17(10):72-82
[3] 余建平,周新民,陈明.群体智能典型算法研究综述[J].计算机工程与应用,2010,46(25):1-4
[4] Meng Xiang-ping.An Improvement to the Coordination Method of Ant Colony Algorithm[C]∥Computer Distributed Control and Intelligent Environmental Monitoring(CDCIEM).Hunan,China,2012
[5] Kuo R J,Akbaria K,Subroto B.Application of particle swarm optimization and perceptual map to tourist market segmentation[J].Expert Systems with Applications,2012,39:8726-8735
[6] Karaboga D.An Idea Based On Honey Bee Swarm For Numerical Optimization[R].Technical Report-TR06.Erciyes University,2005
[7] 胡中华,赵敏.基于人工蜂群算法的TSP仿真[J].北京理工大学学报,2009,29(11):978-982
[8] Karaboga D,Basturk B.On the Performance of Artificial Bee Colony Algorithm[J].Applied Soft Computing,2008,8(1):687-697
[9] 孙玉灵,刘弘,曹杰.基于人工蜂群算法的群体动画路径生成方法[J].计算机工程,2011,37(22):131-133
[10] 谭营.计算群体智能基础[M].北京:清华大学出版社,2009:25-26
[11] 苏绍勇,陈继明,潘金贵.虚拟环境中行为建模技术研究[J].计算机科学,2007,34(2):270-273
[12] 王延红,仇小鹏,杨平利.ACIS/HOOPS几何建模与可视化技术研究与应用[J].计算机仿真,2011(z1):357-360
[13] 刘秀玲,杜欢平,杨国杰.分布式多交互虚拟场景渲染的协同控制[J].计算机工程与应用,2009,45(29):78-81

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!