Abstract
A Fuzzy Constraint Satisfaction Problem (FCSP) is an extension of the classical CSP, a powerful tool for modeling various problems based on constraints among variables. Meanwhile, a Dynamic CSP (DCSP) is a framework for modeling the transformation of problems, and the differences between past solutions and new solutions should be as small as possible. These schemes are the techniques to formulate real world problems as CSPs more easily. The CSP model that combines these (Dynamic Fuzzy CSP) has already existing researches. However, as CSPs are NP-hard problems in general, no efficient and complete algorithms for solving CSPs exist and the increase in the worst-case computation time is exponential in the size of the problems. In the work reported in this paper we tested a hybrid approximate method, called the SRS algorithm. Moreover, we introduce a post-filtering method, called the SRSD algorithm. We empirically show that SRS and SRSD algorithms keep the stability of solutions better than other algorithms. They are able to quickly get good-quality approximate and stabile solutions to large problems.
