Solving large 0–1 multidimensional knapsack problems by a new simplified binary artificial fish swarm algorithm

The artificial fish swarm algorithm has recently been emerged in continuous global optimization. It uses points of a population in space to identify the position of fish in the school. Many real-world optimization problems are described by 0-1 multidimensional knapsack problems that are NP-hard. In the last decades several exact as well as heuristic methods have been proposed for solving these problems. In this paper, a new simpli ed binary version of the artificial fish swarm algorithm is presented, where a point/ fish is represented by a binary string of 0/1 bits. Trial points are created by using crossover and mutation in the different fi sh behavior that are randomly selected by using two user de ned probability values. In order to make the points feasible the presented algorithm uses a random heuristic drop item procedure followed by an add item procedure aiming to increase the profit throughout the adding of more items in the knapsack. A cyclic reinitialization of 50% of the population, and a simple local search that allows the progress of a small percentage of points towards optimality and after that refines the best point in the population greatly improve the quality of the solutions. The presented method is tested on a set of benchmark instances and a comparison with other methods available in literature is shown. The comparison shows that the proposed method can be an alternative method for solving these problems.

A new competitive implementation of the electromagnetism-like algorithm for global optimization

The Electromagnetism-like (EM) algorithm is a population- based stochastic global optimization algorithm that uses an attraction- repulsion mechanism to move sample points towards the optimal. In this paper, an implementation of the EM algorithm in the Matlab en- vironment as a useful function for practitioners and for those who want to experiment a new global optimization solver is proposed. A set of benchmark problems are solved in order to evaluate the performance of the implemented method when compared with other stochastic methods available in the Matlab environment. The results con rm that our imple- mentation is a competitive alternative both in term of numerical results and performance. Finally, a case study based on a parameter estimation problem of a biology system shows that the EM implementation could be applied with promising results in the control optimization area.

Combining non-dominance, objective-order and spread metric to extend firefly algorithm to multi-objective optimization

In this paper, we propose an extension of the firefly algorithm (FA) to multi-objective optimization. FA is a swarm intelligence optimization algorithm inspired by the flashing behavior of fireflies at night that is capable of computing global solutions to continuous optimization problems. Our proposal relies on a fitness assignment scheme that gives lower fitness values to the positions of fireflies that correspond to non-dominated points with smaller aggregation of objective function distances to the minimum values. Furthermore, FA randomness is based on the spread metric to reduce the gaps between consecutive non-dominated solutions. The obtained results from the preliminary computational experiments show that our proposal gives a dense and well distributed approximated Pareto front with a large number of points.

New control algorithm for single-phase series active power filter

This paper presents a single-phase Series Active Power Filter (Series APF) for mitigation of the load voltage harmonic content, while maintaining the voltage on the DC side regulated without the support of a voltage source. The proposed series active power filter control algorithm eliminates the additional voltage source to regulate the DC voltage, and with the adopted topology it is not used a coupling transformer to interface the series active power filter with the electrical power grid. The paper describes the control strategy which encapsulates the grid synchronization scheme, the compensation voltage calculation, the damping algorithm and the dead-time compensation. The topology and control strategy of the series active power filter have been evaluated in simulation software and simulations results are presented. Experimental results, obtained with a developed laboratorial prototype, validate the theoretical assumptions, and are within the harmonic spectrum limits imposed by the international recommendations of the IEEE-519 Standard.

MOEA/VAN: Multiobjective Evolutionary Algorithm based on Vector Angle Neighborhood

Natural selection favors the survival and reproduction of organisms that are best adapted to their environment. Selection mechanism in evolutionary algorithms mimics this process, aiming to create environmental conditions in which artificial organisms could evolve solving the problem at hand. This paper proposes a new selection scheme for evolutionary multiobjective optimization. The similarity measure that defines the concept of the neighborhood is a key feature of the proposed selection. Contrary to commonly used approaches, usually defined on the basis of distances between either individuals or weight vectors, it is suggested to consider the similarity and neighborhood based on the angle between individuals in the objective space. The smaller the angle, the more similar individuals. This notion is exploited during the mating and environmental selections. The convergence is ensured by minimizing distances from individuals to a reference point, whereas the diversity is preserved by maximizing angles between neighboring individuals. Experimental results reveal a highly competitive performance and useful characteristics of the proposed selection. Its strong diversity preserving ability allows to produce a significantly better performance on some problems when compared with stat-of-the-art algorithms.

Reconstructing historical forest cover change in the Lower Amazon floodplains using the LandTrendr algorithm

ABSTRACTThe Amazon várzeas are an important component of the Amazon biome, but anthropic and climatic impacts have been leading to forest loss and interruption of essential ecosystem functions and services. The objectives of this study were to evaluate the capability of the Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr) algorithm to characterize changes in várzeaforest cover in the Lower Amazon, and to analyze the potential of spectral and temporal attributes to classify forest loss as either natural or anthropogenic. We used a time series of 37 Landsat TM and ETM+ images acquired between 1984 and 2009. We used the LandTrendr algorithm to detect forest cover change and the attributes of "start year", "magnitude", and "duration" of the changes, as well as "NDVI at the end of series". Detection was restricted to areas identified as having forest cover at the start and/or end of the time series. We used the Support Vector Machine (SVM) algorithm to classify the extracted attributes, differentiating between anthropogenic and natural forest loss. Detection reliability was consistently high for change events along the Amazon River channel, but variable for changes within the floodplain. Spectral-temporal trajectories faithfully represented the nature of changes in floodplain forest cover, corroborating field observations. We estimated anthropogenic forest losses to be larger (1.071 ha) than natural losses (884 ha), with a global classification accuracy of 94%. We conclude that the LandTrendr algorithm is a reliable tool for studies of forest dynamics throughout the floodplain.

Análise de Cluster e Análise Heurística na construção de uma tipologia de alcoolistas

Objetivos Desenvolver e avaliar uma tipologia binária de alcoolistas. Métodos Analisados registros de 329 alcoolistas acompanhados em estudo prospectivo e observacional. Análise de Correspondência foi aplicada para selecionar o menor número de variáveis capazes de representar a máxima variabilidade dos sujeitos; Análise de Cluster para identificar dois subtipos; e Análise Heurística para organizar as variáveis selecionadas em grupos de sentido. Análises Bivariadas foram utilizadas para testar possíveis associações com 51 variáveis demográficas ou clinicamente relevantes. Resultados A Análise de Correspondência selecionou 20 variáveis, posteriormente categorizadas em três grupos de sentidos por meio de Análise Heurística: “comportamentos”, “crenças” e “sentimentos”. Os pacientes do subgrupo 1 possuem perfil de maior gravidade clínica, psicopatológica e social. Sentimentos e crenças identificados se associaram a variáveis de consumo e recaída e os comportamentos com repercussão social. Conclusões Os resultados deste estudo permitem supor que a tipologia aqui apresentada possa contribuir para o tratamento desses subtipos de alcoolistas identificados por meio de estratégias teórico-práticas adaptadas às necessidades desses sujeitos.

Entrada de multinacionais num cluster e efeitos na estratégia das empresas instaladas

Dissertação de mestrado em Economia Industrial e da Empresa

Aceleración de Algoritmos en Procesadores Multicore, GPGPU y FPGA. Algorithm Acceleration with Multicore, GPGPU and FPGA Processors.

El avance en la potencia de cómputo en nuestros días viene dado por la paralelización del procesamiento, dadas las características que disponen las nuevas arquitecturas de hardware. Utilizar convenientemente este hardware impacta en la aceleración de los algoritmos en ejecución (programas). Sin embargo, convertir de forma adecuada el algoritmo en su forma paralela es complejo, y a su vez, esta forma, es específica para cada tipo de hardware paralelo. En la actualidad los procesadores de uso general más comunes son los multicore, procesadores paralelos, también denominados Symmetric Multi-Processors (SMP). Hoy en día es difícil hallar un procesador para computadoras de escritorio que no tengan algún tipo de paralelismo del caracterizado por los SMP, siendo la tendencia de desarrollo, que cada día nos encontremos con procesadores con mayor numero de cores disponibles. Por otro lado, los dispositivos de procesamiento de video (Graphics Processor Units - GPU), a su vez, han ido desarrollando su potencia de cómputo por medio de disponer de múltiples unidades de procesamiento dentro de su composición electrónica, a tal punto que en la actualidad no es difícil encontrar placas de GPU con capacidad de 200 a 400 hilos de procesamiento paralelo. Estos procesadores son muy veloces y específicos para la tarea que fueron desarrollados, principalmente el procesamiento de video. Sin embargo, como este tipo de procesadores tiene muchos puntos en común con el procesamiento científico, estos dispositivos han ido reorientándose con el nombre de General Processing Graphics Processor Unit (GPGPU). A diferencia de los procesadores SMP señalados anteriormente, las GPGPU no son de propósito general y tienen sus complicaciones para uso general debido al límite en la cantidad de memoria que cada placa puede disponer y al tipo de procesamiento paralelo que debe realizar para poder ser productiva su utilización. Los dispositivos de lógica programable, FPGA, son dispositivos capaces de realizar grandes cantidades de operaciones en paralelo, por lo que pueden ser usados para la implementación de algoritmos específicos, aprovechando el paralelismo que estas ofrecen. Su inconveniente viene derivado de la complejidad para la programación y el testing del algoritmo instanciado en el dispositivo. Ante esta diversidad de procesadores paralelos, el objetivo de nuestro trabajo está enfocado en analizar las características especificas que cada uno de estos tienen, y su impacto en la estructura de los algoritmos para que su utilización pueda obtener rendimientos de procesamiento acordes al número de recursos utilizados y combinarlos de forma tal que su complementación sea benéfica. Específicamente, partiendo desde las características del hardware, determinar las propiedades que el algoritmo paralelo debe tener para poder ser acelerado. Las características de los algoritmos paralelos determinará a su vez cuál de estos nuevos tipos de hardware son los mas adecuados para su instanciación. En particular serán tenidos en cuenta el nivel de dependencia de datos, la necesidad de realizar sincronizaciones durante el procesamiento paralelo, el tamaño de datos a procesar y la complejidad de la programación paralela en cada tipo de hardware. Today´s advances in high-performance computing are driven by parallel processing capabilities of available hardware architectures. These architectures enable the acceleration of algorithms when thes ealgorithms are properly parallelized and exploit the specific processing power of the underneath architecture. Most current processors are targeted for general pruposes and integrate several processor cores on a single chip, resulting in what is known as a Symmetric Multiprocessing (SMP) unit. Nowadays even desktop computers make use of multicore processors. Meanwhile, the industry trend is to increase the number of integrated rocessor cores as technology matures. On the other hand, Graphics Processor Units (GPU), originally designed to handle only video processing, have emerged as interesting alternatives to implement algorithm acceleration. Current available GPUs are able to implement from 200 to 400 threads for parallel processing. Scientific computing can be implemented in these hardware thanks to the programability of new GPUs that have been denoted as General Processing Graphics Processor Units (GPGPU).However, GPGPU offer little memory with respect to that available for general-prupose processors; thus, the implementation of algorithms need to be addressed carefully. Finally, Field Programmable Gate Arrays (FPGA) are programmable devices which can implement hardware logic with low latency, high parallelism and deep pipelines. Thes devices can be used to implement specific algorithms that need to run at very high speeds. However, their programmability is harder that software approaches and debugging is typically time-consuming. In this context where several alternatives for speeding up algorithms are available, our work aims at determining the main features of thes architectures and developing the required know-how to accelerate algorithm execution on them. We look at identifying those algorithms that may fit better on a given architecture as well as compleme

Anwendung der Coupled-Cluster-Methode zur Untersuchung frustrierter quasi-eindimensionaler und zweidimensionaler Quantenspinsysteme

Magdeburg, Univ., Fak. für Naturwiss., Diss., 2010

A Novel Algorithm to Quantify Coronary Remodeling Using Inferred Normal Dimensions

Background:Vascular remodeling, the dynamic dimensional change in face of stress, can assume different directions as well as magnitudes in atherosclerotic disease. Classical measurements rely on reference to segments at a distance, risking inappropriate comparison between dislike vessel portions.Objective:to explore a new method for quantifying vessel remodeling, based on the comparison between a given target segment and its inferred normal dimensions.Methods:Geometric parameters and plaque composition were determined in 67 patients using three-vessel intravascular ultrasound with virtual histology (IVUS-VH). Coronary vessel remodeling at cross-section (n = 27.639) and lesion (n = 618) levels was assessed using classical metrics and a novel analytic algorithm based on the fractional vessel remodeling index (FVRI), which quantifies the total change in arterial wall dimensions related to the estimated normal dimension of the vessel. A prediction model was built to estimate the normal dimension of the vessel for calculation of FVRI.Results:According to the new algorithm, “Ectatic” remodeling pattern was least common, “Complete compensatory” remodeling was present in approximately half of the instances, and “Negative” and “Incomplete compensatory” remodeling types were detected in the remaining. Compared to a traditional diagnostic scheme, FVRI-based classification seemed to better discriminate plaque composition by IVUS-VH.Conclusion:Quantitative assessment of coronary remodeling using target segment dimensions offers a promising approach to evaluate the vessel response to plaque growth/regression.

Quantum phase transitions in magnetic systems: application of coupled cluster method

Magdeburg, Univ., Fak. für Naturwiss., Diss., 2009

Algorithm engineering for expression dag based number types

Magdeburg, Univ., Fak. für Informatik, Diss., 2015

Förderung von Gewerbeneuansiedlungen im Wirtschaftsraum Blankenburg (Harz) unter Berücksichtigung der bestehenden gewerblichen Cluster

Ziel der Arbeit war es, den Wirtschaftsraum Blankenburg (Harz) durch die Bildung und Förderung von gewerblichen Clustern attraktiver zu gestalten. In diesem Rahmen wurde zunächst die Entwicklung von Gewerbe- und Industrieflächen mithilfe der Bauleitplanung in Theorie und Praxis beschrieben. Die anschließende Untersuchung des Bundeslandes Sachsen-Anhalt und im speziellen der Stadt Blankenburg (Harz) bildeten die Datengrundlage für die weiteren Arbeitsschritte. Um die Ergebnisse dieser Standortanalyse in einen direkten Zusammenhang mit tatsächlichen Eindrücken der ansässigen Unternehmen bringen zu können, wurde eine schriftliche Unternehmensbefragung durchgeführt. Diese thematisierte vor allem die Einschätzung der Standortfaktoren in Blankenburg (Harz). Die im Zwischenfazit beschriebene Wertung, dass die Stadt derzeit nur wenige Anreize für Unternehmensneuansiedlungen bietet, war Ausgangspunkt für die Theorie der Clusterpolitik. Dabei wurden zunächst gewerbliche Cluster aus den ansässigen Unternehmen gebildet. Daraus resultierten die Wirtschaftsbereiche, deren Unternehmen für eine Ansiedlung in Blankenburg besonders in Frage kommen. Da diese Betriebe feste Anforderungen an Gewerbe- oder Industrieflächen in Bezug auf die Größe, die planungsrechtlichen Beschränkungen und die infrastrukturelle Anbindung stellen, konnten die vorhandenen Freiflächen im Rahmen einer Mikroanalyse hinsichtlich ihrer Eignung für Neuansiedlungen untersucht werden.

Creating and using a non-dedicated HPC cluster with ParallelKnoppix

This note describes ParallelKnoppix, a bootable CD that allows econometricians with average knowledge of computers to create and begin using a high performance computing cluster for parallel computing in very little time. The computers used may be heterogeneous machines, and clusters of up to 200 nodes are supported. When the cluster is shut down, all machines are in their original state, so their temporary use in the cluster does not interfere with their normal uses. An example shows how a Monte Carlo study of a bootstrap test procedure may be done in parallel. Using a cluster of 20 nodes, the example runs approximately 20 times faster than it does on a single computer.