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A Hybrid Particle Swarm Optimization for Binary CSPs

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Computational Intelligence and Bioinformatics (ICIC 2006)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 4115))

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Abstract

The target of solving constraint satisfaction problems(CSP) is to satisfy all constraints simultaneously. The CSP model is transformed into a discrete optimization problem with boundary constraints and is solved by particle swarm optimization(PSO) in this paper. To improve the performance of the proposed PSO algorithm, ERA(Environment, Reactive rules, Agent) model is used to proceed with local search after the process of boundary constraints. Further improvement including nohope and tabu list are also combined with PSO. When particles can not explore more search space, nohope is introduced to improve the activities of particles. Tabu list is used to avoid cycling in the global best particle. We experiment with random constraint satisfaction problem instances based on phase transition theory. Experimental results indicate that the hybrid algorithm has advantages on the search capability and the iterative number.

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Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Jilin University, Changchun, 130012, China

    Qingyun Yang, Jigui Sun & Juyang Zhang

  2. Key Laboratory for Symbolic Computation and Knowledge, Engineering of Ministry of Education, Jilin University, Changchun, 130012, China

    Qingyun Yang, Jigui Sun & Juyang Zhang

  3. Open Laboratory for Intelligence Information Processing, Fudan University, Shanghai, 200433, China

    Jigui Sun

  4. Basic Sciences of ChangChun University of Technology, ChangChun University of Technology, Changchun, 130012, China

    Chunjie Wang

Authors
  1. Qingyun Yang
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  2. Jigui Sun
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  3. Juyang Zhang
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  4. Chunjie Wang
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Editor information

Editors and Affiliations

  1. Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui, China

    De-Shuang Huang

  2. Queen’s University, Belfast, UK

    Kang Li  & George William Irwin  & 

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© 2006 Springer-Verlag Berlin Heidelberg

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Yang, Q., Sun, J., Zhang, J., Wang, C. (2006). A Hybrid Particle Swarm Optimization for Binary CSPs. In: Huang, DS., Li, K., Irwin, G.W. (eds) Computational Intelligence and Bioinformatics. ICIC 2006. Lecture Notes in Computer Science(), vol 4115. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816102_5

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  • DOI: https://doi.org/10.1007/11816102_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-37277-6

  • Online ISBN: 978-3-540-37282-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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