1,382 research outputs found
An efficient genetic algorithm for large-scale planning of robust industrial wireless networks
An industrial indoor environment is harsh for wireless communications
compared to an office environment, because the prevalent metal easily causes
shadowing effects and affects the availability of an industrial wireless local
area network (IWLAN). On the one hand, it is costly, time-consuming, and
ineffective to perform trial-and-error manual deployment of wireless nodes. On
the other hand, the existing wireless planning tools only focus on office
environments such that it is hard to plan IWLANs due to the larger problem size
and the deployed IWLANs are vulnerable to prevalent shadowing effects in harsh
industrial indoor environments. To fill this gap, this paper proposes an
overdimensioning model and a genetic algorithm based over-dimensioning (GAOD)
algorithm for deploying large-scale robust IWLANs. As a progress beyond the
state-of-the-art wireless planning, two full coverage layers are created. The
second coverage layer serves as redundancy in case of shadowing. Meanwhile, the
deployment cost is reduced by minimizing the number of access points (APs); the
hard constraint of minimal inter-AP spatial paration avoids multiple APs
covering the same area to be simultaneously shadowed by the same obstacle. The
computation time and occupied memory are dedicatedly considered in the design
of GAOD for large-scale optimization. A greedy heuristic based
over-dimensioning (GHOD) algorithm and a random OD algorithm are taken as
benchmarks. In two vehicle manufacturers with a small and large indoor
environment, GAOD outperformed GHOD with up to 20% less APs, while GHOD
outputted up to 25% less APs than a random OD algorithm. Furthermore, the
effectiveness of this model and GAOD was experimentally validated with a real
deployment system
Approximating Probability Densities by Iterated Laplace Approximations
The Laplace approximation is an old, but frequently used method to
approximate integrals for Bayesian calculations. In this paper we develop an
extension of the Laplace approximation, by applying it iteratively to the
residual, i.e., the difference between the current approximation and the true
function. The final approximation is thus a linear combination of multivariate
normal densities, where the coefficients are chosen to achieve a good fit to
the target distribution. We illustrate on real and artificial examples that the
proposed procedure is a computationally efficient alternative to current
approaches for approximation of multivariate probability densities. The
R-package iterLap implementing the methods described in this article is
available from the CRAN servers.Comment: to appear in Journal of Computational and Graphical Statistics,
http://pubs.amstat.org/loi/jcg
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Combinatorial optimization and metaheuristics
Today, combinatorial optimization is one of the youngest and most active areas of discrete mathematics. It is a branch of optimization in applied mathematics and computer science, related to operational research, algorithm theory and computational complexity theory. It sits at the intersection of several fields, including artificial intelligence, mathematics and software engineering. Its increasing interest arises for the fact that a large number of scientific and industrial problems can be formulated as abstract combinatorial optimization problems, through graphs and/or (integer) linear programs. Some of these problems have polynomial-time (“efficient”) algorithms, while most of them are NP-hard, i.e. it is not proved that they can be solved in polynomial-time. Mainly, it means that it is not possible to guarantee that an exact solution to the problem can be found and one has to settle for an approximate solution with known performance guarantees. Indeed, the goal of approximate methods is to find “quickly” (reasonable run-times), with “high” probability, provable “good” solutions (low error from the real optimal solution). In the last 20 years, a new kind of algorithm commonly called metaheuristics have emerged in this class, which basically try to combine heuristics in high level frameworks aimed at efficiently and effectively exploring the search space. This report briefly outlines the components, concepts, advantages and disadvantages of different metaheuristic approaches from a conceptual point of view, in order to analyze their similarities and differences. The two very significant forces of intensification and diversification, that mainly determine the behavior of a metaheuristic, will be pointed out. The report concludes by exploring the importance of hybridization and integration methods
Benchmarking a wide spectrum of metaheuristic techniques for the radio network design problem
The radio network design (RND) is an NP-hard optimization problem which consists of the maximization of the coverage of a given area while minimizing the base station deployment. Solving RND problems efficiently is relevant to many fields of application and has a direct impact in the engineering, telecommunication, scientific, and industrial areas. Numerous works can be found in the literature dealing with the RND problem, although they all suffer from the same shortfall: a noncomparable efficiency. Therefore, the aim of this paper is twofold: first, to offer a reliable RND comparison base reference in order to cover a wide algorithmic spectrum, and, second, to offer a comprehensible insight into accurate comparisons of efficiency, reliability, and swiftness of the different techniques applied to solve the RND problem. In order to achieve the first aim we propose a canonical RND problem formulation driven by two main directives: technology independence and a normalized comparison criterion. Following this, we have included an exhaustive behavior comparison between 14 different techniques. Finally, this paper indicates algorithmic trends and different patterns that can be observed through this analysis.Publicad
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