Empirical investigation of stochastic local search for maximum satisfiability

2018 ◽  
Vol 13 (1) ◽  
pp. 86-98 ◽  
Author(s):  
Yi Chu ◽  
Chuan Luo ◽  
Shaowei Cai ◽  
Haihang You
Keyword(s):  
Local Search ◽  
Empirical Investigation ◽  
Stochastic Local Search ◽  
Maximum Satisfiability

scholarly journals CCEHC: An Efficient Local Search Algorithm for Weighted Partial Maximum Satisfiability (Extended Abstract)

2017 ◽  
Author(s):  
Chuan Luo ◽  
Shaowei Cai ◽  
Kaile Su ◽  
Wenxuan Huang
Keyword(s):  
Local Search ◽  
State Of The Art ◽  
Search Algorithm ◽  
Stochastic Local Search ◽  
Local Search Algorithm ◽  
Maximum Satisfiability ◽  
Biased Random Walk ◽  
Configuration Checking ◽  
Significant Generalization ◽  
Max Sat

Weighted partial maximum satisfiability (WPMS) is a significant generalization of maximum satisfiability (MAX-SAT), with many important applications. Recently, breakthroughs have been made on stochastic local search (SLS) for weighted MAX-SAT and (unweighted) partial MAX-SAT (PMS). However, the performance of SLS for WPMS lags far behind. In this work, we present a new SLS algorithm named CCEHC for WPMS. CCEHC is mainly based on a heuristic emphasizing hard clauses, which has three components: a variable selection mechanism focusing on configuration checking based only on hard clauses, a weighting scheme for hard clauses, and a biased random walk component. Experiments show that CCEHC significantly outperforms its state-of-the-art SLS competitors. Experiments comparing CCEHC with a state-of-the-art complete solver indicate the effectiveness of CCEHC on a number of application WPMS instances.

Stochastic local search with learning automaton for the swap-body vehicle routing problem

2018 ◽  
Vol 89 ◽  
pp. 68-81 ◽  
Author(s):  
Túlio A.M. Toffolo ◽  
Jan Christiaens ◽  
Sam Van Malderen ◽  
Tony Wauters ◽  
Greet Vanden Berghe
Keyword(s):  
Local Search ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Stochastic Local Search ◽  
Routing Problem ◽  
Learning Automaton

scholarly journals Circumspect descent prevails in solving random constraint satisfaction problems

2008 ◽  
Vol 105 (40) ◽  
pp. 15253-15257 ◽  
Author(s):  
Mikko Alava ◽  
John Ardelius ◽  
Erik Aurell ◽  
Petteri Kaski ◽  
Supriya Krishnamurthy ◽  
...  
Keyword(s):  
Local Search ◽  
Linear Time ◽  
Search Algorithm ◽  
Solution Space ◽  
Search Algorithms ◽  
Constraint Satisfaction Problems ◽  
Problem Instance ◽  
Stochastic Local Search ◽  
Local Search Algorithms ◽  
Local Energy Minimum

We study the performance of stochastic local search algorithms for random instances of the K-satisfiability (K-SAT) problem. We present a stochastic local search algorithm, ChainSAT, which moves in the energy landscape of a problem instance by never going upwards in energy. ChainSAT is a focused algorithm in the sense that it focuses on variables occurring in unsatisfied clauses. We show by extensive numerical investigations that ChainSAT and other focused algorithms solve large K-SAT instances almost surely in linear time, up to high clause-to-variable ratios α; for example, for K = 4 we observe linear-time performance well beyond the recently postulated clustering and condensation transitions in the solution space. The performance of ChainSAT is a surprise given that by design the algorithm gets trapped into the first local energy minimum it encounters, yet no such minima are encountered. We also study the geometry of the solution space as accessed by stochastic local search algorithms.

An FPGA Solver for Partial MaxSAT Problems Based on Stochastic Local Search

2017 ◽  
Vol 44 (4) ◽  
pp. 32-37
Author(s):  
Shohei Sassa ◽  
Kenji Kanazawa ◽  
Shaowei Cai ◽  
Moritoshi Yasunaga
Keyword(s):  
Local Search ◽  
Stochastic Local Search
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