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.
Alberto Herrán González
Associate Professor
J. Manuel Colmenar
Full Professor
My research interests are focused on metaheuristics applied to optimization problems. I have worked on different combinatorial optimization problems applying trajectorial algorithms such us GRASP or VNS. Besides, I am very interested in applications of Grammatical Evolution, specifically in model and prediction domain, as alternative to machine learning approaches.