Randomized heuristics for the Max-Cut problem

Abstract

Given an undirected graph with edge weights, the MAX-CUT problem consists in finding a partition of the nodes into two subsets, such that the sum of the weights of the edges having endpoints in different subsets is maximized. It is a well-known NP-hard problem with applications in several fields, including VLSI design and statistical physics. In this paper, a greedy randomized adaptive search procedure (GRASP), a variable neighborhood search (VNS), and a path-relinking (PR) intensification heuristic for MAX-CUT are proposed and tested. New hybrid heuristics that combine GRASP, VNS, and PR are also proposed and tested. Computational results indicate that these randomized heuristics find near-optimal solutions. On a set of standard test problems, new best known solutions were produced for many of the instances.

DOI: 10.1080/1055678021000090033

10 Figures and Tables

0102030'04'06'08'10'12'14'16
Citations per Year

174 Citations

Semantic Scholar estimates that this publication has 174 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@article{Festa2002RandomizedHF, title={Randomized heuristics for the Max-Cut problem}, author={Paola Festa and Panos M. Pardalos and Mauricio G. C. Resende and C. C. Ribeiro}, journal={Optimization Methods and Software}, year={2002}, volume={17}, pages={1033-1058} }