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An Improved Parallel Ant Colony Optimization Based on Message Passing Interface

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Advances in Swarm Intelligence (ICSI 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6145))

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Abstract

Ant Colony Optimization (ACO) is recently proposed metaheuristic approach for solving hard combinatorial optimization problems. Parallel implementation of ACO can reduce the computational time obviously. An improved parallel ACO algorithm is proposed in this paper, which use dynamic transition probability to enlarge the search space by stimulating ants choosing new path at early stage; use polymorphic ant colony to improve convergence speed by local search and global search; use partially asynchronous parallel implementation, interactive multi-colony parallel and new information exchange strategy to improve the parallel efficiency. We implement the algorithm on the Dawn 4000L parallel computer using MPI and C language. The Numerical result indicates the algorithm proposed in this paper can improve convergence speed effectively with the fine solution quality.

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Author information

Authors and Affiliations

  1. Key Laboratory of Geo-detection (China University of Geosciences, Beijing), Ministry of Education, 100083, Beijing, China

    Jie Xiong & Xiaohong Meng

  2. Freshman Education Department of Yangtze University, 434023, Jingzhou, Hubei, China

    Caiyun Liu

Authors
  1. Jie Xiong
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  2. Xiaohong Meng
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  3. Caiyun Liu
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Editor information

Editors and Affiliations

  1. Key Laboratory of Machine Perception (MOE), Department of Machine Intelligence, Peking University, 100871, Beijing, China

    Ying Tan

  2. Research and Postgraduate Office, Xi’an Jiaotong-Liverpool University, 215123, Suzhoum, China

    Yuhui Shi

  3. Department of Electrical and Computer Engineering, National University of Singapore, Engineering Drive 3, 4 Engineering Drive 3, 117576, Singapore

    Kay Chen Tan

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© 2010 Springer-Verlag Berlin Heidelberg

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Xiong, J., Meng, X., Liu, C. (2010). An Improved Parallel Ant Colony Optimization Based on Message Passing Interface. In: Tan, Y., Shi, Y., Tan, K.C. (eds) Advances in Swarm Intelligence. ICSI 2010. Lecture Notes in Computer Science, vol 6145. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13495-1_31

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  • DOI: https://doi.org/10.1007/978-3-642-13495-1_31

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13494-4

  • Online ISBN: 978-3-642-13495-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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