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Unleashing the Potential of Restart by Detecting the Search Stagnation

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Learning and Intelligent Optimization (LION 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14286))

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Abstract

SAT solvers are widely used to solve industrial problems owing to their exceptional performance. One critical aspect of SAT solvers is the implementation of restarts, which aims to enhance performance by diversifying the search. However, it is uncertain whether restarts effectively lead to search diversification. We propose to adapt search similarity index (SSI), a metric designed to quantify the similarity between search processes, to evaluate the impact of restarts. Our experimental findings, which employ SSI, reveal how the impact of restarts varies with respect to the number of restarts, instance categories, and employed restart strategies. In light of these observations, we present a new restart strategy called Break-out Stagnation Restart (BroSt Restart), inspired by a financial market trading technique. This approach identifies stagnant search processes and diversifies the search by shuffling the decision order to leave the stagnant search. The evaluation results demonstrate that BroSt Restart improves the performance of a sequential SAT solver, solving 19 more instances (+3%) than state-of-the-art solvers.

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Authors and Affiliations

  1. Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan

    Yoichiro Iida & Mary Inaba

  2. National Institute of Informatics, Tokyo, Japan

    Tomohiro Sonobe

Authors
  1. Yoichiro Iida
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  2. Tomohiro Sonobe
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  3. Mary Inaba
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Correspondence to Yoichiro Iida .

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Editors and Affiliations

  1. InsideOpt, Dover, DE, USA

    Meinolf Sellmann

  2. Bielefeld University, Bielefeld, Germany

    Kevin Tierney

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Iida, Y., Sonobe, T., Inaba, M. (2023). Unleashing the Potential of Restart by Detecting the Search Stagnation. In: Sellmann, M., Tierney, K. (eds) Learning and Intelligent Optimization. LION 2023. Lecture Notes in Computer Science, vol 14286. Springer, Cham. https://doi.org/10.1007/978-3-031-44505-7_40

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  • DOI: https://doi.org/10.1007/978-3-031-44505-7_40

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