Abstract: The DLMI Algorithm is a local search algorithm for solving constraint satisfaction problems that incorporates the use of Island confinement method. Local search starts the search for a solution from a random assignment. It then examines the neighbours of this assignment, using the penalty function to determine a better neighbour valuations to move to. It repeats this process until it finds a solution that satisfies all constraints. The island confinement method considers some of the constraints as hard constraints that are always satisfied. In this way, the constraints reduce the possible neighbours in each move and hence the overall search space. We choose the hard constraints C in such away that the space of valuations that satisfies these constraints is connected in order to guarantee that a local search can reach a solution from any valuation in this space without violating C. A previous study has shown that the DLMI algorithm performs better than the original DLM algorithm. In this paper, we describe how incorporating learning in the island traps and restart improves the DLMI algorithm.
Abdullah Mohdzin and Yousef Kilani , 2005. Improving Dlmi Algorithm by Incorporating New Features . Asian Journal of Information Technology, 4: 1120-1126.