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Improving PAWS by the Island Confinement Method

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Artificial Intelligence and Soft Computing (ICAISC 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7268))

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Abstract

The propositional satisfiability problem (SAT) is one of the most studied NP-complete problems in computer science [1]. Some of the best known methods for solving certain types of SAT instances are stochastic local search algorithms [6].

Pure Additive Weighting Scheme (PAWS) is now one of the best dynamic local search algorithms in the additive weighting category [7]. Fang et. al [3] introduce the island confinement method to speed up the local search algorithms. In this paper, we incorporate the island confinement method into PAWS to speed up PAWS. We show through experiments that, the resulted algorithm, PAWSI, betters PAWS in solving the hard graph coloring and AIS problems.

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References

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Author information

Authors and Affiliations

  1. Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology, Hashemite University, Jordan

    Yousef Kilani, Mohammad Bsoul, Ayoub Alsarhan & Ibrahim Obeidat

Authors
  1. Yousef Kilani
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  2. Mohammad Bsoul
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  3. Ayoub Alsarhan
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  4. Ibrahim Obeidat
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Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Armii Krajowej 36, 42-200, Częstochowa, Poland

    Leszek Rutkowski , Marcin Korytkowski  & Rafał Scherer ,  & 

  2. AGH University of Science and Technology, Mickiewicza 30, 30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  3. Department of Electrical Engineering and Computer Sciences, Computer Science Division, University of California Berkeley, 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

  4. Computational Intelligence Laboratory, Electrical and Computer Engineering, University of Louisville, 405 Lutz Hall, 40292, Louisville, KY, USA

    Jacek M. Zurada

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© 2012 Springer-Verlag Berlin Heidelberg

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Kilani, Y., Bsoul, M., Alsarhan, A., Obeidat, I. (2012). Improving PAWS by the Island Confinement Method. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2012. Lecture Notes in Computer Science(), vol 7268. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29350-4_78

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  • DOI: https://doi.org/10.1007/978-3-642-29350-4_78

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29349-8

  • Online ISBN: 978-3-642-29350-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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