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Review on the Improvement and Application of Ant Colony Algorithm

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Bio-inspired Computing: Theories and Applications (BIC-TA 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1159))

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Abstract

The ant colony optimization algorithm is an approximation algorithm and it is also a probabilistic algorithm for finding optimized paths. Many combinatorial optimization problems have been solved by the ant colony optimization algorithm. Firstly, the basic principles of ant colony algorithm are first introduced by this reference. Secondly, it briefs several improvements method of ant colony algorithm and the application in solving practical problems, including the improvement of ant colony algorithm, parameter combination tuning and the application of ant colony algorithm in combination optimization problem. Finally, the problems existing in the ant colony algorithm are summarized and forecasted in this article.

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Author information

Authors and Affiliations

  1. Henan Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou, 450002, China

    Dongping Qiao, Wentong Bai, Kanghong Wang & Yajing Wang

Authors
  1. Dongping Qiao
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  2. Wentong Bai
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  3. Kanghong Wang
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  4. Yajing Wang
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Corresponding author

Correspondence to Wentong Bai .

Editor information

Editors and Affiliations

  1. Huazhong University of Science and Technology, Wuhan, China

    Linqiang Pan

  2. Zhengzhou University, Zhengzhou, China

    Jing Liang

  3. Zhongyuan University of Technology, Zhengzhou, China

    Boyang Qu

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Qiao, D., Bai, W., Wang, K., Wang, Y. (2020). Review on the Improvement and Application of Ant Colony Algorithm. In: Pan, L., Liang, J., Qu, B. (eds) Bio-inspired Computing: Theories and Applications. BIC-TA 2019. Communications in Computer and Information Science, vol 1159. Springer, Singapore. https://doi.org/10.1007/978-981-15-3425-6_1

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  • DOI: https://doi.org/10.1007/978-981-15-3425-6_1

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-3424-9

  • Online ISBN: 978-981-15-3425-6

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