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Research on Improved Ant Colony Algorithm for TSP Problem

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Book cover Computer, Informatics, Cybernetics and Applications

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 107))

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Abstract

A bionic optimization algorithm called ant colony optimization was introduced in this chapter. Based on the basic ant colony algorithm, this paper improves ant colony algorithms as follows: (1) increase the local pheromone updating link and change the original algorithm in the state transition principle; (2) select the next city by pseudo-random proportional rule instead of selection directly by probability. The simulation experiments show that the improved algorithm is better than the traditional one.

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References

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Acknowledgments

This research is supported by Research Fund for the Doctoral Program of Higher Education of China (Grant No. 20103326120001), Zhejiang Provincial Natural Science Foundation of China (No. Z1091224, Y7100673 and Y1091164), Zhejiang Provincial Social Science Foundation of China (Grant No. 10JDSM03YB), the Scientific Research Fund of Zhejiang Province (No. 2010C11062), Research Project of Department of Education of Zhejiang Province (No. Y200907458 and Y201016434), the Contemporary Business and Trade Research Center of Zhejiang Gongshang University (No. 1130KUSM09013 and 1130KU110021). We also gratefully acknowledge the support of Science and Technology Innovative project (No. 1130XJ1710214).

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Authors and Affiliations

  1. College of Computer Science and Information Engineering, Zhejiang Gongshang University, Hangzhou, 310018, Zhejiang, China

    Xiu-juan Qiu & Ting-gui Chen

  2. Contemporary Business and Trade Research Center, Zhejiang Gongshang University, Hangzhou, 310018, Zhejiang, China

    Ting-gui Chen

Authors
  1. Xiu-juan Qiu
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  2. Ting-gui Chen
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Corresponding author

Correspondence to Xiu-juan Qiu .

Editor information

Editors and Affiliations

  1. Chinese Academy of Engineering, Beijing, China, People's Republic

    Xingui He

  2. Zhejiang Gongshang University, Beijing, China, People's Republic

    Ertian Hua

  3. Zhejiang Gongshang University, Hangzhou, China, People's Republic

    Yun Lin

  4. , School of Automation, Wuhan University of Technology, East District of Wuhan University, Luoshi Road 122, Wuhan, 4300, China, People's Republic

    Xiaozhu Liu

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© 2012 Springer Science+Business Media B.V.

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Qiu, Xj., Chen, Tg. (2012). Research on Improved Ant Colony Algorithm for TSP Problem. In: He, X., Hua, E., Lin, Y., Liu, X. (eds) Computer, Informatics, Cybernetics and Applications. Lecture Notes in Electrical Engineering, vol 107. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-1839-5_26

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  • DOI: https://doi.org/10.1007/978-94-007-1839-5_26

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  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-007-1838-8

  • Online ISBN: 978-94-007-1839-5

  • eBook Packages: EngineeringEngineering (R0)

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