An Asynchronous P System Using Branch and Bound for the Satisfiability Problem

An asynchronous P system with branch and bound for solving the satisfiability problem

International Journal of Networking and Computing ◽

10.15803/ijnc.8.2_141 ◽

2018 ◽

Vol 8 (2) ◽

pp. 141-152 ◽

Cited By ~ 2

Author(s):

Yuki Jimen ◽

Akihiro Fujiwara

Keyword(s):

Branch And Bound ◽

Satisfiability Problem ◽

P System

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Boosting branch-and-bound MaxSAT solvers with clause learning

AI Communications ◽

10.3233/aic-210178 ◽

2021 ◽

pp. 1-21

Author(s):

Chu-Min Li ◽

Zhenxing Xu ◽

Jordi Coll ◽

Felip Manyà ◽

Djamal Habet ◽

...

Keyword(s):

Experimental Investigation ◽

Combinatorial Optimization ◽

Problem Solving ◽

Branch And Bound ◽

Real World ◽

Optimization Problems ◽

Satisfiability Problem ◽

Maximum Satisfiability ◽

Combinatorial Optimization Problems ◽

Clause Learning

The Maximum Satisfiability Problem, or MaxSAT, offers a suitable problem solving formalism for combinatorial optimization problems. Nevertheless, MaxSAT solvers implementing the Branch-and-Bound (BnB) scheme have not succeeded in solving challenging real-world optimization problems. It is widely believed that BnB MaxSAT solvers are only superior on random and some specific crafted instances. At the same time, SAT-based MaxSAT solvers perform particularly well on real-world instances. To overcome this shortcoming of BnB MaxSAT solvers, this paper proposes a new BnB MaxSAT solver called MaxCDCL. The main feature of MaxCDCL is the combination of clause learning of soft conflicts and an efficient bounding procedure. Moreover, the paper reports on an experimental investigation showing that MaxCDCL is competitive when compared with the best performing solvers of the 2020 MaxSAT Evaluation. MaxCDCL performs very well on real-world instances, and solves a number of instances that other solvers cannot solve. Furthermore, MaxCDCL, when combined with the best performing MaxSAT solvers, solves the highest number of instances of a collection from all the MaxSAT evaluations held so far.

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