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Quick Convergence Algorithm of ACO Based on Convergence Grads Expectation

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Computational Intelligence and Intelligent Systems (ISICA 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 575))

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Abstract

While the ACO can find the optimal path of network, there are too many iterative times and too slow the convergence speed is also very slow. This paper proposes the Q-ACO QoSR based on convergence expectation with the real-time and the high efficiency of network. This algorithm defines index expectation function of link, and proposes convergence expectation and convergence grads. This algorithm can find the optimal path by comparing the convergence grads in a faster and bigger probability. This algorithm improves the ability of routing and convergence speed.

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Acknowledgment

This study was supported by Open project of Guangdong Provincial Key Laboratory of Petrochemical Equipment Fault Diagnosis (GDUPTKLAB201322), a Science and Technology Project of Special fund for High-tech development by Guangdong Provincial Department of Finance in 2013(2013B010401036). Guangdong Provincial Department of Education Science and Technology Innovation Project (2013KJCX0178).

Author information

Authors and Affiliations

  1. Computer Engineering Technical College, Guangdong Institute of Science and Technology, Zhuhai, Guangdong, China

    Zhongming Yang

  2. Guangdong Provincial Key Laboratory of Petrochemical Equipment Fault Diagnosis, Maoming, Guangdong, China

    Zhongming Yang

  3. College of Computer Science, Dongguan University of Technology, Dongguan, Guangdong, China

    Yong Qin

  4. College of Computer Science and Engineering, South China University of Technology, Guangzhou, Guangdong, China

    Huang Han

  5. Department of Information Technology, Zhuhai Technician College, Zhuhai, Guangdong, China

    Yunfu Jia

Authors
  1. Zhongming Yang
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  2. Yong Qin
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  3. Huang Han
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  4. Yunfu Jia
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Corresponding author

Correspondence to Zhongming Yang .

Editor information

Editors and Affiliations

  1. College of Mathematics and Informatics, The South China Agricultural University, Guangzhou, China

    Kangshun Li

  2. School of Computer Science, Guangzhou University, Guangzhou, China

    Jin Li

  3. School of Computer Science and Engineeri, The University of Aizu, Aizu-Wakamatsu, Fukushima, Japan

    Yong Liu

  4. Dept. of Informatics, University of Salerno, Fisciano, Italy

    Aniello Castiglione

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© 2016 Springer Science+Business Media Singapore

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Yang, Z., Qin, Y., Han, H., Jia, Y. (2016). Quick Convergence Algorithm of ACO Based on Convergence Grads Expectation. In: Li, K., Li, J., Liu, Y., Castiglione, A. (eds) Computational Intelligence and Intelligent Systems. ISICA 2015. Communications in Computer and Information Science, vol 575. Springer, Singapore. https://doi.org/10.1007/978-981-10-0356-1_13

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  • DOI: https://doi.org/10.1007/978-981-10-0356-1_13

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-0355-4

  • Online ISBN: 978-981-10-0356-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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