Abstract
The Partial Maximum Satisfiability Problem (PMS) is an optimization variant of the satisfiability problem. It involves separating constraints into hard and soft categories, making it useful for modeling complex problems such as scheduling, vehicle routing, and circuit design automation. Because PMS serves both verification and optimization functions, studying fast and efficient solving methods for it has significant theoretical and practical value. The Stochastic Local Search (SLS) algorithm is widely recognized as an effective method for solving PMS, providing high-quality solutions within reasonable timeframe. While recent research has focused on overcoming the challenge of getting stuck in local optima, this paper proposes a novel approach to improve the initial solution construction process in order to solve more PMS instances. Specifically, we adjust the initial weights of clauses based on contradictory information generated in building an initial solution. Experimental results on the MaxSAT Evaluation (MSE) benchmarks demonstrate that our resulting method, SATLC, outperforms the state-of-the-art PMS SLS, SATLike3.0, in terms of both solution quantity and quality. (The source code can be found at https://github.com/whyte-yhy/SATLC).
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Notes
- 1.
The detailed description is available at https://maxsat-evaluations.github.io.
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Yu, H., Jiang, M., Chen, Y. (2023). Heavy Weighting for Potential Important Clauses. In: Jin, Z., Jiang, Y., Buchmann, R.A., Bi, Y., Ghiran, AM., Ma, W. (eds) Knowledge Science, Engineering and Management. KSEM 2023. Lecture Notes in Computer Science(), vol 14119. Springer, Cham. https://doi.org/10.1007/978-3-031-40289-0_21
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