Work

Two-phase GRASP for the Multi-Constraint Graph Partitioning problem

Year: 2024

Type: article

Abstract: The Multi-Constraint Graph Partitioning (MCGP) problem seeks a partition of the node set of a graph into a fixed number of clusters such that each cluster satisfies a collection of node-weight constraints and the total cost of the edges whose end nodes are in the same cluster is minimized. In this paper we propose a two-phase reactive GRASP heuristic to find near-optimal solutions to the MCGP problem. Our proposal is able to reach all the best known results for state-of-the-art instances, obtaining all the certified optimum values while spending only a fraction of the time in relation to the previous methods. To reach these results we have implemented an efficient computation method applied in the improvement phase. Besides, we have created a new set of larger instances for the MCGP problem and provided detailed results for future comparisons.

Source: Computers & Operations Research

DOI: 10.1016/j.cor.2024.106946

Authors: Alberto Herrán, J. Manuel Colmenar, Mauricio G.C. Resende

Institutions: Universidad Rey Juan Carlos, University of Washington

Language: English

Topic: Metaheuristic Optimization Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,470

Funders: Generalitat Valenciana, Ministerio de Ciencia e Innovación, European Regional Development Fund, Conselleria de Cultura, Educación y Ciencia, Generalitat Valenciana