Partial maximum satisfiability problem method combined with structure characteristics for diagnostic problems

Partial MaxSAT (PMS) is a generalized version of the maximum satisfiability problem (MaxSAT), which has important applications in many fields. At present, the PMS algorithm requires improvements in solving the industrial problems. Based on deep research on the PMS algorithm using random search, this paper presents a structure characteristics partial MaxSAT (SCPMS) method that combines structural feature exploration. First, according to the unit propagation rules and structural features of the problem, we gradually divide the PMS clause into two parts to construct a subproblem that can satisfy the problem due to the absence of the hard unit clause. In this paper, a random search guidance strategy is proposed. The new subproblem is reused to identify the hard-blocking variable in the hard unit clause of the original problem using unit propagation. Then, the corresponding soft-blocking variable is reversed with the feature of the clause to improve the efficiency of the random search. The experimental results show that in comparison with the two latest algorithms, DeciDist and DistUp, the proposed SCPMS greatly reduces the number of unsatisfied soft clauses in solving model-based diagnosis instances.