Ricardo M. A. Silva

Improved heuristics for the regenerator location problem

Telecommunication systems use optical signals to transmit information. The strength of a signal in an optical network deteriorates and loses power as it goes farther from the source, mainly due to attenuation. Therefore, to enable the signal to arrive its intended destination with good quality, it is necessary to regenerate the signal periodically using regenerators. These components are relatively expensive and therefore it is desirable to deploy as few of them as possible in the network. In the regenerator location problem (RLP), we are given an undirected graph, positive edge lengths, and a parameter specifying the maximum length that a signal can travel before its quality deteriorates and regeneration is required. The problem consists in determining paths that connect all pairs of nodes in the graph and, if necessary, locating single regenerators in some of those nodes such that the signal never travels more than the maximum allowed distance without traversing a regenerator node. In this paper, we present new implementations of previous heuristics and two new heuristics—a GRASP and a biased random-key genetic algorithm—for the RLP. Computational experiments comparing the proposed solution procedures with previous heuristics described in the literature illustrate the efficiency and effectiveness of our methods.

Randomized heuristics for handover minimization in mobility networks

A mobile device connects to the cell tower (base station) from which it receives the strongest signal. As the device moves it may connect to a series of towers. The process in which the device changes the base station it is connected to is called handover. A cell tower is connected to a radio network controller (RNC) which controls many of its operations, including handover. Each cell tower handles an amount of traffic and each radio network controller has capacity to handle a maximum amount of traffic from all base stations connected to it. Handovers between base stations connected to different RNCs tend to fail more often than handovers between base stations connected to the same RNC. Handover failures result in dropped connections and therefore should be minimized. The Handover Minimization Problem is to assign towers to RNCs such that RNC capacity is not violated and the number of handovers between base stations connected to different RNCs is minimized. We describe an integer programming formulation for the handover minimization problem and show that state-of-the-art integer programming solvers can solve only very small instances of the problem. We propose several randomized heuristics for finding approximate solutions of this problem, including a GRASP with path-relinking for the generalized quadratic assignment problem, a GRASP with evolutionary path-relinking, and a biased random-key genetic algorithm. Computational results are presented.

A biased random-key genetic algorithm for the Steiner triple covering problem

We present a biased random-key genetic algorithm (BRKGA) for finding small covers of computationally difficult set covering problems that arise in computing the 1-width of incidence matrices of Steiner triple systems. Using a parallel implementation of the BRKGA, we compute improved covers for the two largest instances in a standard set of test problems used to evaluate solution procedures for this problem. The new covers for instances A405 and A729 have sizes 335 and 617, respectively. On all other smaller instances our algorithm consistently produces covers of optimal size.

An edge-swap heuristic for generating spanning trees with minimum number of branch vertices

This paper presents a new edge-swap heuristic for generating spanning trees with a minimum number of branch vertices, i.e. vertices of degree greater than two. This problem was introduced in Gargano et al. (Lect Notes Comput Sci 2380:355–365, 2002) and has been called the minimum branch vertices problem by Cerulli et al. (Comput Optim Appl 42:353–370, 2009). The heuristic starts with a random spanning tree and iteratively reduces the number of branch vertices by swapping tree edges with edges not currently in the tree. It can be easily implemented as a multi-start heuristic. We report on extensive computational experiments comparing single-start and multi-start variants on our heuristic with other heuristics previously proposed in the literature.

GRASP with path-relinking for data clustering: a case study for biological data

Cluster analysis has been applied to several domains with numerous applications. In this paper, we propose several GRASP with path-relinking heuristics for data clustering problems using as case study biological datasets. All these variants are based on the construction and local search procedures introduced by Nascimento et. al [22]. We hybridized the GRASP proposed by Nascimento et. al [22] with four alternatives for relinking method: forward, backward, mixed, and randomized. To our knowledge, GRASP with path-relinking has never been applied to cluster biological datasets. Extensive comparative experiments with other algorithms on a large set of test instances, according to different distance metrics (Euclidean, city block, cosine, and Pearson), show that the best of the proposed variants is both effective and efficient.

Parameter tuning for document image binarization using a racing algorithm

It is investigated the use of I/F-Race to tune document image binarization methods.The method combines visual perception with the minimization of an energy function.Our experiments show that I/F-Race suggests promising parametric configurations.The binarization algorithm configured by I/F-Race outperforms other recent methods. Binarization of images of old documents is considered a challenging task due to the wide diversity of degradation effects that can be found. To deal with this, many algorithms whose performance depends on an appropriate choice of their parameters have been proposed. In this work, it is investigated the application of a racing procedure based on a statistical approach, named I/F-Race, to suggest the parameters for two binarization algorithms reasoned (i) on the perception of objects by distance (POD) and (ii) on the POD combined with a Laplacian energy-based technique. Our experiments show that both algorithms had their performance statistically improved outperforming other recent binarization techniques. The second proposal presented herein ranked first in H-DIBCO (Handwritten Document Image Binarization Contest) 2014.

Finding multiple roots of a box-constrained system of nonlinear equations with a biased random-key genetic algorithm

Several numerical methods for solving nonlinear systems of equations assume that derivative information is available. Furthermore, these approaches usually do not consider the problem of finding all solutions to a nonlinear system. Rather, most methods output a single solution. In this paper, we address the problem of finding all roots of a system of equations. Our method makes use of a biased random-key genetic algorithm (BRKGA). Given a nonlinear system, we construct a corresponding optimization problem, which we solve multiple times, making use of a BRKGA, with areas of repulsion around roots that have already been found. The heuristic makes no use of derivative information. We illustrate the approach on seven nonlinear equations systems with multiple roots from the literature.

An iterative refinement algorithm for the minimum branch vertices problem

This paper presents a new approach to solve the NP-complete minimumbranch vertices problem (MBV) introduced by Gargano et. al[1]. In spite of being a recently proposed problem in the network optimization literature, there are some heuristics to solve it [3]. The main contribution of this paper consists in a new heuristic based on the iterative refinement approach proposed by Deo and Kumar [2]. The experimental results suggest that this approach is capable of finding solutions that are better than the best known in the literature. In thiswork, for instance, the proposed heuristic found better solutions for 78% of the instances tested. The heuristic looks very promising for the solution of problems related with constrained spanning trees.

Virtual Network Resource Allocation Considering Dependability Issues

Virtualized Networks (VN) has been pointed by the scientific community as a promising way to solve the current ossification problem of the Internet, since several heterogeneous VN can coexist on a shared physical infrastructure. An important issue for VN allocation algorithms is related to dependability, since components of the physical network are failure-prone (issue not considered by several techniques). This paper proposes a GRASP (Greedy Randomized Adaptive Search Procedure) based algorithm for allocating virtualized networks taking into account dependability issues. Dependability metrics are estimated using stochastic Petri nets and reliability block diagrams, as well as redundancy techniques are adopted for improving such metrics. Experimental results demonstrate the feasibility of the proposed approach when dependability issues are taken into account.

A Python/C++ library for bound-constrained global optimization using a biased random-key genetic algorithm

This paper describes libbrkga, a GNU-style dynamic shared Python/C++ library of the biased random-key genetic algorithm (BRKGA) for bound constrained global optimization. BRKGA (J Heuristics 17:487–525, 2011b) is a general search metaheuristic for finding optimal or near-optimal solutions to hard optimization problems. It is derived from the random-key genetic algorithm of Bean (ORSA J Comput 6:154–160, 1994), differing in the way solutions are combined to produce offspring. After a brief introduction to the BRKGA, including a description of the local search procedure used in its decoder, we show how to download, install, configure, and use the library through an illustrative example.