Randomized heuristics for the Max-Cut problem


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

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@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} }