Empirical investigation of stochastic local search for maximum satisfiability

doi:10.1007/s11704-018-7107-z

Front. Comput. Sci.

2019, Vol. 13

Issue (1) : 86-98 https://doi.org/10.1007/s11704-018-7107-z

RESEARCH ARTICLE

Empirical investigation of stochastic local search for maximum satisfiability

Yi CHU^1,², Chuan LUO^1,³, Shaowei CAI⁴, Haihang YOU¹(

)

¹. State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
². University of Chinese Academy of Sciences, Beijing 100049, China
³. State Key Laboratory of Mathematical Engineering and Advanced Computing,Wuxi 214125, China
⁴. State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China

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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 MAXSAT 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 MAXSAT 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.

Keywords empirical investigation stochastic local search maximum satisfiability industrial instances additive BMS

Corresponding Author(s): Haihang YOU

Just Accepted Date: 24 November 2017 Online First Date: 04 September 2018 Issue Date: 31 January 2019

Cite this article:

Yi CHU,Chuan LUO,Shaowei CAI, et al. Empirical investigation of stochastic local search for maximum satisfiability[J]. Front. Comput. Sci., 2019, 13(1): 86-98.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-018-7107-z
https://academic.hep.com.cn/fcs/EN/Y2019/V13/I1/86

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