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An Improved Ant Colony Optimization Applied to Attributes Reduction

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Fuzzy Information and Engineering

Part of the book series: Advances in Soft Computing ((AINSC,volume 54))

Abstract

Attribute reduction problem (ARP) in rough set theory is an NP-hard problem, which is difficult to use fast traditional method to solve. In this paper, we discuss about the difference between the traveling salesman problems (TSP) and the ARP, and then we bring up a new state transition probability formula and a new pheromone traps increment formula of ant colony optimization. The results demonstrate that the improved ant colony optimization is better than initial ant colony optimization used in attribute reduction and more suitable for ARP.

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Author information

Authors and Affiliations

  1. College of Science, Harbin Engineering University, Harbin, 150001, P.R. China

    Ting-quan Deng, Cheng-dong Yang, Yue-tong Zhang & Xin-xia Wang

Authors
  1. Ting-quan Deng
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  2. Cheng-dong Yang
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  3. Yue-tong Zhang
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  4. Xin-xia Wang
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Editor information

Editors and Affiliations

  1. Guangzhou Higher Education Mega Center, Guangzhou University, 230Wai Huan Xi Road, 510006, China

    Bing-yuan Cao

  2. Department of Mathematics, Hainan Normal University, Haikou, 571158, Hainan, P.R. China

    Cheng-yi Zhang

  3. Department of Electronic Information Engineering, Chongqing University of Science & Technology, 401331, Chongqing

    Tai-fu Li

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© 2009 Springer-Verlag Berlin Heidelberg

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Deng, Tq., Yang, Cd., Zhang, Yt., Wang, Xx. (2009). An Improved Ant Colony Optimization Applied to Attributes Reduction. In: Cao, By., Zhang, Cy., Li, Tf. (eds) Fuzzy Information and Engineering. Advances in Soft Computing, vol 54. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88914-4_1

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  • DOI: https://doi.org/10.1007/978-3-540-88914-4_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-88913-7

  • Online ISBN: 978-3-540-88914-4

  • eBook Packages: EngineeringEngineering (R0)

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