Aptidão agrícola e algoritmos genéticos na organização espacial em projetos de reforma agrária

A definição das parcelas familiares em projetos de reforma agrária envolve questões técnicas e sociais. Essas questões estão associadas principalmente às diferentes aptidões agrícolas do solo nestes projetos. O objetivo deste trabalho foi apresentar método para realizar o processo de ordenamento territorial em assentamentos de reforma agrária empregando Algoritmo Genético (AG). O AG foi testado no Projeto de Assentamento Veredas, em Minas Gerais, e implementado com base no sistema de aptidão agrícola das terras.

Design methodology for multi-pumped discrete Raman amplifiers: case-study employing photonic crystal fibers

This paper proposes a new design methodology for discrete multi-pumped Raman amplifier. In a multi-objective optimization scenario, in a first step the whole solution-space is inspected by a CW analytical formulation. Then, the most promising solutions are fully investigated by a rigorous numerical treatment and the Raman amplification performance is thus determined by the combination of analytical and numerical approaches. As an application of our methodology we designed an photonic crystal fiber Raman amplifier configuration which provides low ripple, high gain, clear eye opening and a low power penalty. The amplifier configuration also enables to fully compensate the dispersion introduced by a 70-km singlemode fiber in a 10 Gbit/s system. We have successfully obtained a configuration with 8.5 dB average gain over the C-band and 0.71 dB ripple with almost zero eye-penalty using only two pump lasers with relatively low pump power. (C) 2009 Optical Society of America

Simultaneous optimization by neuro-genetic approach for analysis of plant materials by laser induced breakdown spectroscopy

A simultaneous optimization strategy based on a neuro-genetic approach is proposed for selection of laser induced breakdown spectroscopy operational conditions for the simultaneous determination of macronutrients (Ca, Mg and P), micro-nutrients (B, Cu, Fe, Mn and Zn), Al and Si in plant samples. A laser induced breakdown spectroscopy system equipped with a 10 Hz Q-switched Nd:YAG laser (12 ns, 532 nm, 140 mJ) and an Echelle spectrometer with intensified coupled-charge device was used. Integration time gate, delay time, amplification gain and number of pulses were optimized. Pellets of spinach leaves (NIST 1570a) were employed as laboratory samples. In order to find a model that could correlate laser induced breakdown spectroscopy operational conditions with compromised high peak areas of all elements simultaneously, a Bayesian Regularized Artificial Neural Network approach was employed. Subsequently, a genetic algorithm was applied to find optimal conditions for the neural network model, in an approach called neuro-genetic, A single laser induced breakdown spectroscopy working condition that maximizes peak areas of all elements simultaneously, was obtained with the following optimized parameters: 9.0 mu s integration time gate, 1.1 mu s delay time, 225 (a.u.) amplification gain and 30 accumulated laser pulses. The proposed approach is a useful and a suitable tool for the optimization process of such a complex analytical problem. (C) 2009 Elsevier B.V. All rights reserved.

Node-Depth Encoding and Multiobjective Evolutionary Algorithm Applied to Large-Scale Distribution System Reconfiguration

The power loss reduction in distribution systems (DSs) is a nonlinear and multiobjective problem. Service restoration in DSs is even computationally hard since it additionally requires a solution in real-time. Both DS problems are computationally complex. For large-scale networks, the usual problem formulation has thousands of constraint equations. The node-depth encoding (NDE) enables a modeling of DSs problems that eliminates several constraint equations from the usual formulation, making the problem solution simpler. On the other hand, a multiobjective evolutionary algorithm (EA) based on subpopulation tables adequately models several objectives and constraints, enabling a better exploration of the search space. The combination of the multiobjective EA with NDE (MEAN) results in the proposed approach for solving DSs problems for large-scale networks. Simulation results have shown the MEAN is able to find adequate restoration plans for a real DS with 3860 buses and 632 switches in a running time of 0.68 s. Moreover, the MEAN has shown a sublinear running time in function of the system size. Tests with networks ranging from 632 to 5166 switches indicate that the MEAN can find network configurations corresponding to a power loss reduction of 27.64% for very large networks requiring relatively low running time.

A novel multiobjective optimization algorithm based on bacterial chemotaxis

In this article a novel algorithm based on the chemotaxis process of Echerichia coil is developed to solve multiobjective optimization problems. The algorithm uses fast nondominated sorting procedure, communication between the colony members and a simple chemotactical strategy to change the bacterial positions in order to explore the search space to find several optimal solutions. The proposed algorithm is validated using 11 benchmark problems and implementing three different performance measures to compare its performance with the NSGA-II genetic algorithm and with the particle swarm-based algorithm NSPSO. (C) 2009 Elsevier Ltd. All rights reserved.

Adaptive Approach for a Maximum Entropy Algorithm in Ecological Niche Modeling

This paper presents an Adaptive Maximum Entropy (AME) approach for modeling biological species. The Maximum Entropy algorithm (MaxEnt) is one of the most used methods in modeling biological species geographical distribution. The approach presented here is an alternative to the classical algorithm. Instead of using the same set features in the training, the AME approach tries to insert or to remove a single feature at each iteration. The aim is to reach the convergence faster without affect the performance of the generated models. The preliminary experiments were well performed. They showed an increasing on performance both in accuracy and in execution time. Comparisons with other algorithms are beyond the scope of this paper. Some important researches are proposed as future works.

Parallel Dynamic Optimization of Steel Risers

This paper addresses the use of optimization techniques in the design of a steel riser. Two methods are used: the genetic algorithm, which imitates the process of natural selection, and the simulated annealing, which is based on the process of annealing of a metal. Both of them are capable of searching a given solution space for the best feasible riser configuration according to predefined criteria. Optimization issues are discussed, such as problem codification, parameter selection, definition of objective function, and restrictions. A comparison between the results obtained for economic and structural objective functions is made for a case study. Optimization method parallelization is also addressed. [DOI: 10.1115/1.4001955]

A spectral clustering algorithm for manufacturing cell formation

A graph clustering algorithm constructs groups of closely related parts and machines separately. After they are matched for the least intercell moves, a refining process runs on the initial cell formation to decrease the number of intercell moves. A simple modification of this main approach can deal with some practical constraints, such as the popular constraint of bounding the maximum number of machines in a cell. Our approach makes a big improvement in the computational time. More importantly, improvement is seen in the number of intercell moves when the computational results were compared with best known solutions from the literature. (C) 2009 Elsevier Ltd. All rights reserved.

Inteligência artificial aplicada à Zootecnia

Os sistemas biológicos são surpreendentemente flexíveis pra processar informação proveniente do mundo real. Alguns organismos biológicos possuem uma unidade central de processamento denominada de cérebro. O cérebro humano consiste de 10(11) neurônios e realiza processamento inteligente de forma exata e subjetiva. A Inteligência Artificial (IA) tenta trazer para o mundo da computação digital a heurística dos sistemas biológicos de várias maneiras, mas, ainda resta muito para que isso seja concretizado. No entanto, algumas técnicas como Redes neurais artificiais e lógica fuzzy tem mostrado efetivas para resolver problemas complexos usando a heurística dos sistemas biológicos. Recentemente o numero de aplicação dos métodos da IA em sistemas zootécnicos tem aumentado significativamente. O objetivo deste artigo é explicar os princípios básicos da resolução de problemas usando heurística e demonstrar como a IA pode ser aplicada para construir um sistema especialista para resolver problemas na área de zootecnia.

The Effect of Spatial Scale on Predicting Time Series: A Study on Epidemiological System Identification

A susceptible-infective-recovered (SIR) epidemiological model based on probabilistic cellular automaton (PCA) is employed for simulating the temporal evolution of the registered cases of chickenpox in Arizona, USA, between 1994 and 2004. At each time step, every individual is in one of the states S, I, or R. The parameters of this model are the probabilities of each individual (each cell forming the PCA lattice ) passing from a state to another state. Here, the values of these probabilities are identified by using a genetic algorithm. If nonrealistic values are allowed to the parameters, the predictions present better agreement with the historical series than if they are forced to present realistic values. A discussion about how the size of the PCA lattice affects the quality of the model predictions is presented. Copyright (C) 2009 L. H. A. Monteiro et al.

System Identification and Prediction of Dengue Fever Incidence in Rio de Janeiro

Identification, prediction, and control of a system are engineering subjects, regardless of the nature of the system. Here, the temporal evolution of the number of individuals with dengue fever weekly recorded in the city of Rio de Janeiro, Brazil, during 2007, is used to identify SIS (susceptible-infective-susceptible) and SIR (susceptible-infective-removed) models formulated in terms of cellular automaton (CA). In the identification process, a genetic algorithm (GA) is utilized to find the probabilities of the state transition S -> I able of reproducing in the CA lattice the historical series of 2007. These probabilities depend on the number of infective neighbors. Time-varying and non-time-varying probabilities, three different sizes of lattices, and two kinds of coupling topology among the cells are taken into consideration. Then, these epidemiological models built by combining CA and GA are employed for predicting the cases of sick persons in 2008. Such models can be useful for forecasting and controlling the spreading of this infectious disease.

Kinematic parameters and membership probabilities of open clusters in the Bordeaux PM2000 catalogue

Aims. We derive lists of proper-motions and kinematic membership probabilities for 49 open clusters and possible open clusters in the zone of the Bordeaux PM2000 proper motion catalogue (+ 11 degrees <= delta <= + 18 degrees). We test different parametrisations of the proper motion and position distribution functions and select the most successful one. In the light of those results, we analyse some objects individually. Methods. We differenciate between cluster and field member stars, and assign membership probabilities, by applying a new and fully automated method based on both parametrisations of the proper motion and position distribution functions, and genetic algorithm optimization heuristics associated with a derivative-based hill climbing algorithm for the likelihood optimization. Results. We present a catalogue comprising kinematic parameters and associated membership probability lists for 49 open clusters and possible open clusters in the Bordeaux PM2000 catalogue region. We note that this is the first determination of proper motions for five open clusters. We confirm the non-existence of two kinematic populations in the region of 15 previously suspected non-existent objects.

DISTRIBUTION OF CAPYBARAS IN AN AGROECOSYSTEM, SOUTHEASTERN BRAZIL, BASED ON ECOLOGICAL NICHE MODELING

Southeastern Brazil has seen dramatic landscape modifications in recent decades, due to expansion of agriculture and urban areas; these changes have influenced the distribution and abundance of vertebrates. We developed predictive models of ecological and spatial distributions of capybaras (Hydrochoerus hydrochaeris) using ecological niche modeling. Most Occurrences of capybaras were in flat areas with water bodies Surrounded by sugarcane and pasture. More than 75% of the Piracicaba River basin was estimated as potentially habitable by capybara. The models had low omission error (2.3-3.4%), but higher commission error (91.0-98.5%); these ""model failures"" seem to be more related to local habitat characteristics than to spatial ones. The potential distribution of capybaras in the basin is associated with anthropogenic habitats, particularly with intensive land use for agriculture.

A simulation-based evolutionary multiobjective approach to manufacturing cell formation

The purpose of this paper is to propose a multiobjective optimization approach for solving the manufacturing cell formation problem, explicitly considering the performance of this said manufacturing system. Cells are formed so as to simultaneously minimize three conflicting objectives, namely, the level of the work-in-process, the intercell moves and the total machinery investment. A genetic algorithm performs a search in the design space, in order to approximate to the Pareto optimal set. The values of the objectives for each candidate solution in a population are assigned by running a discrete-event simulation, in which the model is automatically generated according to the number of machines and their distribution among cells implied by a particular solution. The potential of this approach is evaluated via its application to an illustrative example, and a case from the relevant literature. The obtained results are analyzed and reviewed. Therefore, it is concluded that this approach is capable of generating a set of alternative manufacturing cell configurations considering the optimization of multiple performance measures, greatly improving the decision making process involved in planning and designing cellular systems. (C) 2010 Elsevier Ltd. All rights reserved.

Optimization of modal filters based on arrays of piezoelectric sensors

Modal filters may be obtained by a properly designed weighted sum of the output signals of an array of sensors distributed on the host structure. Although several research groups have been interested in techniques for designing and implementing modal filters based on a given array of sensors, the effect of the array topology on the effectiveness of the modal filter has received much less attention. In particular, it is known that some parameters, such as size, shape and location of a sensor, are very important in determining the observability of a vibration mode. Hence, this paper presents a methodology for the topological optimization of an array of sensors in order to maximize the effectiveness of a set of selected modal filters. This is done using a genetic algorithm optimization technique for the selection of 12 piezoceramic sensors from an array of 36 piezoceramic sensors regularly distributed on an aluminum plate, which maximize the filtering performance, over a given frequency range, of a set of modal filters, each one aiming to isolate one of the first vibration modes. The vectors of the weighting coefficients for each modal filter are evaluated using QR decomposition of the complex frequency response function matrix. Results show that the array topology is not very important for lower frequencies but it greatly affects the filter effectiveness for higher frequencies. Therefore, it is possible to improve the effectiveness and frequency range of a set of modal filters by optimizing the topology of an array of sensors. Indeed, using 12 properly located piezoceramic sensors bonded on an aluminum plate it is shown that the frequency range of a set of modal filters may be enlarged by 25-50%.