7 resultados para Branch-and-bound algorithm

em SAPIENTIA - Universidade do Algarve - Portugal


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Discrete optimization problems are very difficult to solve, even if the dimention is small. For most of them the problem of finding an ε-approximate solution is already NP-hard. The branch-and-bound algorithms are the most used algorithms for solving exactly this sort of problems.

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Discrete optimization problems are very difficult to solve, even if the dimantion is small. For most of them the problem of finding an ε-approximate solution is already NP-hard.

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Tese dout., Matemática, Investigação Operacional, Universidade do Algarve, 2009

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This talk addresses the problem of controlling a heating ventilating and air conditioning system with the purpose of achieving a desired thermal comfort level and energy savings. The formulation uses the thermal comfort, assessed using the predicted mean vote (PMV) index, as a restriction and minimises the energy spent to comply with it. This results in the maintenance of thermal comfort and on the minimisation of energy, which in most operating conditions are conflicting goals requiring some sort of optimisation method to find appropriate solutions over time. In this work a discrete model based predictive control methodology is applied to the problem. It consists of three major components: the predictive models, implemented by radial basis function neural networks identifed by means of a multi-objective genetic algorithm [1]; the cost function that will be optimised to minimise energy consumption and provide adequate thermal comfort; and finally the optimisation method, in this case a discrete branch and bound approach. Each component will be described, with a special emphasis on a fast and accurate computation of the PMV indices [2]. Experimental results obtained within different rooms in a building of the University of Algarve will be presented, both in summer [3] and winter [4] conditions, demonstrating the feasibility and performance of the approach. Energy savings resulting from the application of the method are estimated to be greater than 50%.

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In the field of control systems it is common to use techniques based on model adaptation to carry out control for plants for which mathematical analysis may be intricate. Increasing interest in biologically inspired learning algorithms for control techniques such as Artificial Neural Networks and Fuzzy Systems is in progress. In this line, this paper gives a perspective on the quality of results given by two different biologically connected learning algorithms for the design of B-spline neural networks (BNN) and fuzzy systems (FS). One approach used is the Genetic Programming (GP) for BNN design and the other is the Bacterial Evolutionary Algorithm (BEA) applied for fuzzy rule extraction. Also, the facility to incorporate a multi-objective approach to the GP algorithm is outlined, enabling the designer to obtain models more adequate for their intended use.

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A new electrochemical methodology to study labile trace metal/natural organic matter complexation at low concentration levels in natural waters is presented. This methodology consists of three steps: (i) an estimation of the complex diffusion coefficient (DML), (ii) determination at low pH of the total metal concentration initially present in the sample, (iii) a metal titration at the desired pH. The free and bound metal concentrations are determined for each point of the titration and modeled with the non-ideal competitive adsorption (NICA-Donnan) model in order to obtain the binding parameters. In this methodology, it is recommended to determine the hydrodynamic transport parameter, α, for each set of hydrodynamic conditions used in the voltammetric measurements. The methodology was tested using two fractions of natural organic matter (NOM) isolated from the Loire river, namely the hydrophobic organic matter (HPO) and the transphilic organic matter (TPI), and a well characterized fulvic acid (Laurentian fulvic acid, LFA). The complex diffusion coefficients obtained at pH 5 were 0.4 ± 0.2 for Pb and Cu/HPO, 1.8 ± 0.2 for Pb/TPI and (0.612 ± 0.009) × 10−10 m2 s−1 for Pb/LFA. NICA-Donnan parameters for lead binding were obtained for the HPO and TPI fractions. The new lead/LFA results were successfully predicted using parameters derived in our previous work.

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Themarine environment seems, at first sight, to be a homogeneousmediumlacking barriers to species dispersal. Nevertheless, populations of marine species show varying levels of gene flow and population differentiation, so barriers to gene flow can often be detected. Weaimto elucidate the role of oceanographical factors ingenerating connectivity among populations shaping the phylogeographical patterns in the marine realm, which is not only a topic of considerable interest for understanding the evolution ofmarine biodiversity but also formanagement and conservation of marine life. For this proposal,we investigate the genetic structure and connectivity between continental and insular populations ofwhite seabreamin North East Atlantic (NEA) and Mediterranean Sea (MS) aswell as the influence of historical and contemporary factors in this scenario using mitochondrial (cytochrome b) and nuclear (a set of 9 microsatellite) molecular markers. Azores population appeared genetically differentiated in a single cluster using Structure analysis. This result was corroborated by Principal Component Analysis (PCA) and Monmonier algorithm which suggested a boundary to gene flow, isolating this locality. Azorean population also shows the highest significant values of FST and genetic distances for both molecular markers (microsatellites and mtDNA). We suggest that the breakdown of effective genetic exchange between Azores and the others' samples could be explained simultaneously by hydrographic (deep water) and hydrodynamic (isolating current regimes) factors acting as barriers to the free dispersal of white seabream(adults and larvae) and by historical factors which could be favoured for the survival of Azorean white seabream population at the last glaciation. Mediterranean islands show similar genetic diversity to the neighbouring continental samples and nonsignificant genetic differences. Proximity to continental coasts and the current system could promote an optimal larval dispersion among Mediterranean islands (Mallorca and Castellamare) and coasts with high gene flow.