Gilberto Farias de Sousa Filho

An efficient heuristic for the ring star problem

In this paper, we consider a combinatorial optimization problem that arises in the design of telecommunications network. It is known as the Ring Star Problem. In this problem the aim is to locate a simple cycle through a subset of vertices of a graph with the objective of minimizing the sum of two costs: a routing cost proportional to the length of the cycle, and an assignment cost from the vertices not in the cycle to their closest vertex on the cycle. We propose a new hybrid metaheuristic approach to solve the Ring Star Problem. In the hybrid metaheuristic, we use a General Variable Neighborhood Search (GVNS) to improve the quality of the solution obtained with a Greedy Randomized Adaptive Search Procedure (GRASP). A set of extensive computational experiments on instances from the classical TSP library and randomly generated are reported, comparing the GRASP/GVNS heuristic with other heuristic found in the literature. These results indicate that the proposed hybrid metaheuristic is highly efficient and superior to the other available method proposed for the Ring Star Problem.

A parallel hybrid metaheuristic for bicluster editing

The bicluster editing problem (BEP) consists of editing (adding or removing) the edges of a bipartite graph G in order to transform it into a vertex-disjoint union of complete bipartite subgraphs, in such a way that the sum of the weights of the edited edges is minimum. In this paper, we propose five parallel strategies for the implementation of a hybrid metaheuristic for the BEP, consisting of a GRASP with VNS as local search. Computational experiments show near-linear speedups on Linux cluster with 64 processors and better solutions than those of the sequential approach.

Hybrid Metaheuristic for Bicluster Editing Problem

The NP-hard Bicluster Editing Problem consists of adding and/or removing at most k edges to make a bipartite graph G=(V,E) a vertex-disjoint union of complete bipartite subgraphs. It has applications in the analysis of gene expression data. We propose the metaheuristics GRASP and VNS, and their hybridization, for the Bicluster Editing Problem, as well as a heuristic construction based on intersection neighborhood set.

Efficient heuristics for the minimum labeling global cut problem

Abstract Let G = ( V , E , L ) be an edge-labeled graph. Let V be the set of vertices of G, E the set of edges, L the set of labels (colors) such that each edge e ∈ E has an associated label L ( e ) . The goal of the minimum labeling global cut problem (MLGCP) is to find a subset L ′ ⊆ L of labels such that G ′ = ( V , E ′ , L \ L ′ ) is not connected and | L ′ | is minimized. In this work, we generate random instances for the MLGCP to perform empirical tests. Also propose a set of heuristics using concepts of Genetic Algorithm and metaheuristic VNS, including some of their procedures, like two local search moves, and an auxiliary data structure to speed up the local search. Computational experiments show that the metaheuristic VNS outperforms other methods with respect to solution quality.

Branch-and-price for p-cluster editing

Given an input graph, the p-cluster editing problem consists of minimizing the number of editions, i.e., additions and/or deletions of edges, so as to create p vertex-disjoint cliques (clusters). In order to solve this

Digital Data Networks Design Using Hybrid Metaheuristic

{\mathscr {NP}}

Optimization and implementation of a system for allocating services integrated with the Google Maps service

NP-hard problem, we propose a branch-and-price algorithm over a set partitioning based formulation with exponential number of variables. We show that this formulation theoretically dominates the best known formulation for the problem. Moreover, we compare the performance of three mathematical formulations for the pricing subproblem, which is strongly