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Multiple Ant Colony Optimization for Load Balancing

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Intelligent Data Engineering and Automated Learning (IDEAL 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2690))

Abstract

This paper presents a Multiple Ant Colony Optimization (MACO) approach for load balancing in circuit-switched networks. Based on the problem-solving approach of ants in nature, Ant Colony Optimization (ACO) has been applied to solve problems in optimization, network routing and load balancing by modeling ants as a society of mobile agents. While traditional ACO approaches employed one ant colony for routing, MACO uses multiple ant colonies to search for alternatives to an optimal path. One of the impetuses of MACO is to optimize the performance of a congested network by routing calls via several alternatives paths to prevent possible congestion along an optimal path. Ideas of applying MACO for load-balancing in circuit-switched networks have been implemented in a testbed. Using fairness ratio as a performance measure, experimental results show that MACO is (1) effective in balancing the load, and (2) more effective than traditional ACO for load balancing.

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References

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Author information

Authors and Affiliations

  1. Department of Information Engineering, The Chinese University of Hong Kong, Shatin, NT, Hong Kong

    Kwang Mong Sim

  2. Business Information System, Faculty of Business Administration, University of Macau,  

    Weng Hong Sun

Authors
  1. Kwang Mong Sim
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  2. Weng Hong Sun
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Editor information

Editors and Affiliations

  1. Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong

    Jiming Liu

  2. Department of Computer Science, Hong Kong Baptist University, Hong Kong

    Yiu-ming Cheung

  3. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

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© 2003 Springer-Verlag Berlin Heidelberg

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Sim, K.M., Sun, W.H. (2003). Multiple Ant Colony Optimization for Load Balancing. In: Liu, J., Cheung, Ym., Yin, H. (eds) Intelligent Data Engineering and Automated Learning. IDEAL 2003. Lecture Notes in Computer Science, vol 2690. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45080-1_64

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  • DOI: https://doi.org/10.1007/978-3-540-45080-1_64

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40550-4

  • Online ISBN: 978-3-540-45080-1

  • eBook Packages: Springer Book Archive

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