Tailored heuristics in adaptive large neighborhood search applied to the cutwidth minimization problem.

Abstract

The cutwidth minimization problem (CMP) consists in determining a linear layout (i.e., a one-dimensional arrangement), of the vertices of a graph that minimizes the maximum number of edges crossing any consecutive pair of vertices. This problem has applications, for instance, in design of very large-scale integration circuits, graph drawing, and compiler design. The CMP is an N P-Hard problem and presents a challenge to exact methods and heuristics. In this study, the metaheuristic adaptive large neighborhood search is applied to the CMP. The computational experiments include 11,786 benchmark instances from four sets in the literature, and the obtained results are compared with state-of-the-art methods. The proposed method was demonstrated to be competitive, as it matched most optimal and best known results, improved some of the (not proved optimal) best known solutions, and provided the first upper bounds for unsolved instances.