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Hybridizing Evolutionary Negative Selection Algorithm and Local Search for Large-Scale Satisfiability Problems

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Advances in Computation and Intelligence (ISICA 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5821))

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Abstract

This paper introduces a hybrid algorithm called as the HENSA-SAT for the large-scale Satisfiability (SAT) problems. The HENSA-SAT is the hybrid of Evolutionary Negative Selection Algorithm (ENSA), the Flip Heuristic, the BackForwardFlipHeuristic procedure and the VerticalClimbing procedure. The Negative Selection (NS) is called twice for different purposes. One is used to make the search start in as many different areas as possible. The other is used to restrict the times of calling the BackForwardFlipHeuristic for local search. The Flip Heuristic, the BackForwardFlipHeuristic procedure and the VerticalClimbing procedure are used to enhance the local search. Experiment results show that the proposed algorithm is competitive with the GASAT that is the state-of-the-art algorithm for the large-scale SAT problems.

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Author information

Authors and Affiliations

  1. Nature Inspired Computation and Applications Laboratory (NICAL), School of Computer Science and Technology, University of Science and Technology of China, Hefei, 230027, China

    Peng Guo, Wenjian Luo, Zhifang Li, Houjun Liang & Xufa Wang

  2. Anhui Key Laboratory of Software in Computing and Communication, University of Science and Technology of China, Hefei, 230027, China

    Peng Guo, Wenjian Luo, Zhifang Li, Houjun Liang & Xufa Wang

Authors
  1. Peng Guo
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  2. Wenjian Luo
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  3. Zhifang Li
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  4. Houjun Liang
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  5. Xufa Wang
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Editor information

Editors and Affiliations

  1. Faculty of Computer Science, China University of Geosciences, 430074, Wuhan, Hubei, P.R. China

    Zhihua Cai

  2. School of Computer Science, China University of Geosciences, 430074, Wuhan, Hubei, China

    Zhenhua Li

  3. Computation Center, Wuhan University, 430072, Wuhan, Hubei, China

    Zhuo Kang

  4. School of Computer Science and Engineering, The University of Aizu, 965-8580, i, Aizu-Wakamatsu City, Fukushima, Japan

    Yong Liu

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© 2009 Springer-Verlag Berlin Heidelberg

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Guo, P., Luo, W., Li, Z., Liang, H., Wang, X. (2009). Hybridizing Evolutionary Negative Selection Algorithm and Local Search for Large-Scale Satisfiability Problems. In: Cai, Z., Li, Z., Kang, Z., Liu, Y. (eds) Advances in Computation and Intelligence. ISICA 2009. Lecture Notes in Computer Science, vol 5821. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04843-2_27

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  • DOI: https://doi.org/10.1007/978-3-642-04843-2_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04842-5

  • Online ISBN: 978-3-642-04843-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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