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Empirical investigation of stochastic local search for maximum satisfiability

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Abstract

The maximum satisfiability (MAX-SAT) problem is an important NP-hard problem in theory, and has a broad range of applications in practice. Stochastic local search (SLS) is becoming an increasingly popular method for solving MAX-SAT. Recently, a powerful SLS algorithm called CCLS shows efficiency on solving random and crafted MAX-SAT instances. However, the performance of CCLS on solving industrial MAX-SAT instances lags far behind. In this paper, we focus on experimentally analyzing the performance of SLS algorithms for solving industrial MAX-SAT instances. First, we conduct experiments to analyze why CCLS performs poor on industrial instances. Then we propose a new strategy called additive BMS (Best from Multiple Selections) to ease the serious issue. By integrating CCLS and additive BMS, we develop a new SLS algorithm for MAX-SAT called CCABMS, and related experiments indicate the efficiency of CCABMS. Also, we experimentally analyze the effectiveness of initialization methods on SLS algorithms for MAX-SAT, and combine an effective initialization method with CCABMS, resulting in an enhanced algorithm. Experimental results show that our enhanced algorithm performs better than its state-of-the-art SLS competitors on a large number of industrial MAX-SAT instances.

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Acknowledgements

This work was partially supported by the National Key Research and Development Program of China (2016YFE0100300, 2017YFB02025), partially supported by the 100 Talents Program of the Chinese Academy of Sciences (2920154070), partially supported by the Knowledge Innovation Project of the Chinese Academy of Sciences (5120146040), partially supported by the Open Project Program of the State Key Laboratory of Mathematical Engineering and Advanced Computing (2016A06), partially supported by the National Natural Science Foundation of China (Grant No. 61502464).

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Correspondence to Haihang You.

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Yi Chu received the BSc degree in computer science and technology from Jilin University, China in 2011, and the ME degree in computer technology from Beijing University of Posts and Telecommunications in 2014. She is currently working toward the Phd Degree in computer science at Institute of Computing Technology, Chinese Academy of Sciences, China. Her research interest is combinatorial optimization and heuristic search.

Chuan Luo received the BE degree in computer science from the South China University of Technology in 2011, and the PhD degree from School of Electronics Engineering and Computer Science, Peking University, China in 2016. He is currently a postdoctoral researcher in the ADA Research Group at Leiden University. His current research interest include heuristic search, programming by optimization and automated machine learning.

Shaowei Cai received the BE degree in computer science from the South China University of Technology in 2008, and a PhD degree from School of Electronics Engineering and Computer Science, Peking University, China in 2012, and another PhD degree from Institute for Integrated and Intelligent Systems, Griffith University, Australia in 2014. He is currently a professor in State Key Laboratory of Computer Science in Institute of Software, Chinese Academy of Sciences, China. He has broad interest in artificial intelligence and algorithm design, and have particular interest in combinatorial optimization, heuristics, randomized algorithms, and automated reasoning.

Haihang You is a professor at Institute of Computing Technology, Chinese Academy of Sciences, China. Prior joining ICT, Dr. You was research scientist at National Institute of Computational Sciences at Oak Ridge National Laboratory and Innovative Computing Laboratory at University of Tennessee, America. Dr. You’s research interest is in the field of high performance computing and high throughput computing, specifically parallel algorithm, numerical algorithm, performance optimization and autotuning.

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Chu, Y., Luo, C., Cai, S. et al. Empirical investigation of stochastic local search for maximum satisfiability. Front. Comput. Sci. 13, 86–98 (2019). https://doi.org/10.1007/s11704-018-7107-z

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