Keeping the stability of solutions to dynamic fuzzy CSps

A fuzzy constraint satisfaction problem is an extension of the classical CSP, a powerful tool for modeling various problems based on constraints among variables, and a dynamic CSP is a framework for modelling the transformation of problems. These schemes are the technique to formulate real world problems as CSPs more easily. The CSP model that combines these is already has splendid researches. The fuzzy local change algorithm is practicable enough in small-scale problems, but larger problems require the use of approximate methods. The algorithms for solving CSPs are classified into two categories: systematic searches (complete methods based on search trees), and local searches (approximate methods based on iterative improvement). Both have advantages and disadvantages. In the work reported in this paper we tested a hybrid approximate method, called the spread-repair-shrink algorithm, on dynamic, large-scale problems. The algorithm repairs local constraints by repeatedly spreading and shrinking a set of search trees until the degree to which the worst constraints (the roots of the trees) are satisfied is improved. In this process, the ldquostabilityrdquo of solutions can be maintained because the reassignment is locally limited. Additionaly, we innovate SRSD filter as after filtering. We empirically show that spread-repair-shrink and SRSD algorithm keep the stability of solutions rather than other algorithms. It is able to quickly get a good-quality approximate and stabile solution to a large problem.