ALTERNATIVE SAMPLING STRATEGY FOR A RANDOM OPTIMIZATION ALGORITHM

Alternative sampling procedures are compared to the pure random search method. It is shown that the efficiency of the algorithm can be improved with respect to the expected number of steps to reach an epsilon-neighborhood of the optimal point.

PMUs optimized allocation using a tabu search algorithm for fault location in electric power distribution system

Phasor Measurement Units (PMUs) optimized allocation allows control, monitoring and accurate operation of electric power distribution systems, improving reliability and service quality. Good quality and considerable results are obtained for transmission systems using fault location techniques based on voltage measurements. Based on these techniques and performing PMUs optimized allocation it is possible to develop an electric power distribution system fault locator, which provides accurate results. The PMUs allocation problem presents combinatorial features related to devices number that can be allocated, and also probably places for allocation. Tabu search algorithm is the proposed technique to carry out PMUs allocation. This technique applied in a 141 buses real-life distribution urban feeder improved significantly the fault location results. © 2004 IEEE.

An efficient implementation of tabu search in feeder reconfiguration of distribution systems

This paper presents an efficient tabu search algorithm (TSA) to solve the problem of feeder reconfiguration of distribution systems. The main characteristics that make the proposed TSA particularly efficient are a) the way in which the neighborhood of the current solution was defined; b) the way in which the objective function value was estimated; and c) the reduction of the neighborhood using heuristic criteria. Four electrical systems, described in detail in the specialized literature, were used to test the proposed TSA. The result demonstrate that it is computationally very fast and finds the best solutions known in the specialized literature. © 2012 IEEE.

Ergodic crossover in partially self-avoiding stochastic walks

Consider a one-dimensional environment with N randomly distributed sites. An agent explores this random medium moving deterministically with a spatial memory μ. A crossover from local to global exploration occurs in one dimension at a well-defined memory value μ1=log2N. In its stochastic version, the dynamics is ruled by the memory and by temperature T, which affects the hopping displacement. This dynamics also shows a crossover in one dimension, obtained computationally, between exploration schemes, characterized yet by the trajectory size (Np) (aging effect). In this paper we provide an analytical approach considering the modified stochastic version where the parameter T plays the role of a maximum hopping distance. This modification allows us to obtain a general analytical expression for the crossover, as a function of the parameters μ, T, and Np. Differently from what has been proposed by previous studies, we find that the crossover occurs in any dimension d. These results have been validated by numerical experiments and may be of great value for fixing optimal parameters in search algorithms. © 2013 American Physical Society.

Métodos de otimização global aplicados na busca dos parâmetros SRC

O método de empilhamento por Superfície de Reflexão Comum (SRC) produz seções simuladas de afastamento nulo (AN) por meio do somatório de eventos sísmicos dos dados de cobertura múltipla contidos nas superfícies de empilhamento. Este método não depende do modelo de velocidade do meio, apenas requer o conhecimento a priori da velocidade próxima a superfície. A simulação de seções AN por este método de empilhamento utiliza uma aproximação hiperbólica de segunda ordem do tempo de trânsito de raios paraxiais para definir a superfície de empilhamento ou operador de empilhamento SRC. Para meios 2D este operador depende de três atributos cinemáticos de duas ondas hipotéticas (ondas PIN e N), observados no ponto de emergência do raio central com incidência normal, que são: o ângulo de emergência do raio central com fonte-receptor nulo (β₀) , o raio de curvatura da onda ponto de incidência normal (R_PIN) e o raio de curvatura da onda normal (R_N). Portanto, o problema de otimização no método SRC consiste na determinação, a partir dos dados sísmicos, dos três parâmetros (β₀, R_PIN, R_N) ótimos associados a cada ponto de amostragem da seção AN a ser simulada. A determinação simultânea destes parâmetros pode ser realizada por meio de processos de busca global (ou otimização global) multidimensional, utilizando como função objetivo algum critério de coerência. O problema de otimização no método SRC é muito importante para o bom desempenho no que diz respeito a qualidade dos resultados e principalmente ao custo computacional, comparado com os métodos tradicionalmente utilizados na indústria sísmica. Existem várias estratégias de busca para determinar estes parâmetros baseados em buscas sistemáticas e usando algoritmos de otimização, podendo estimar apenas um parâmetro de cada vez, ou dois ou os três parâmetros simultaneamente. Levando em conta a estratégia de busca por meio da aplicação de otimização global, estes três parâmetros podem ser estimados através de dois procedimentos: no primeiro caso os três parâmetros podem ser estimados simultaneamente e no segundo caso inicialmente podem ser determinados simultaneamente dois parâmetros (β₀, R_PIN) e posteriormente o terceiro parâmetro (R_N) usando os valores dos dois parâmetros já conhecidos. Neste trabalho apresenta-se a aplicação e comparação de quatro algoritmos de otimização global para encontrar os parâmetros SRC ótimos, estes são: Simulated Annealing (SA), Very Fast Simulated Annealing (VFSA), Differential Evolution (DE) e Controlled Rando Search - 2 (CRS2). Como resultados importantes são apresentados a aplicação de cada método de otimização e a comparação entre os métodos quanto a eficácia, eficiência e confiabilidade para determinar os melhores parâmetros SRC. Posteriormente, aplicando as estratégias de busca global para a determinação destes parâmetros, por meio do método de otimização VFSA que teve o melhor desempenho foi realizado o empilhamento SRC a partir dos dados Marmousi, isto é, foi realizado um empilhamento SRC usando dois parâmetros (β₀, R_PIN) estimados por busca global e outro empilhamento SRC usando os três parâmetros (β₀, R_PIN, R_N) também estimados por busca global.

Combining meta-learning and search techniques to select parameters for support vector machines

Support Vector Machines (SVMs) have achieved very good performance on different learning problems. However, the success of SVMs depends on the adequate choice of the values of a number of parameters (e.g., the kernel and regularization parameters). In the current work, we propose the combination of meta-learning and search algorithms to deal with the problem of SVM parameter selection. In this combination, given a new problem to be solved, meta-learning is employed to recommend SVM parameter values based on parameter configurations that have been successfully adopted in previous similar problems. The parameter values returned by meta-learning are then used as initial search points by a search technique, which will further explore the parameter space. In this proposal, we envisioned that the initial solutions provided by meta-learning are located in good regions of the search space (i.e. they are closer to optimum solutions). Hence, the search algorithm would need to evaluate a lower number of candidate solutions when looking for an adequate solution. In this work, we investigate the combination of meta-learning with two search algorithms: Particle Swarm Optimization and Tabu Search. The implemented hybrid algorithms were used to select the values of two SVM parameters in the regression domain. These combinations were compared with the use of the search algorithms without meta-learning. The experimental results on a set of 40 regression problems showed that, on average, the proposed hybrid methods obtained lower error rates when compared to their components applied in isolation.

Learning Methods and Algorithms for Semantic Text Classification across Multiple Domains

Information is nowadays a key resource: machine learning and data mining techniques have been developed to extract high-level information from great amounts of data. As most data comes in form of unstructured text in natural languages, research on text mining is currently very active and dealing with practical problems. Among these, text categorization deals with the automatic organization of large quantities of documents in priorly defined taxonomies of topic categories, possibly arranged in large hierarchies. In commonly proposed machine learning approaches, classifiers are automatically trained from pre-labeled documents: they can perform very accurate classification, but often require a consistent training set and notable computational effort. Methods for cross-domain text categorization have been proposed, allowing to leverage a set of labeled documents of one domain to classify those of another one. Most methods use advanced statistical techniques, usually involving tuning of parameters. A first contribution presented here is a method based on nearest centroid classification, where profiles of categories are generated from the known domain and then iteratively adapted to the unknown one. Despite being conceptually simple and having easily tuned parameters, this method achieves state-of-the-art accuracy in most benchmark datasets with fast running times. A second, deeper contribution involves the design of a domain-independent model to distinguish the degree and type of relatedness between arbitrary documents and topics, inferred from the different types of semantic relationships between respective representative words, identified by specific search algorithms. The application of this model is tested on both flat and hierarchical text categorization, where it potentially allows the efficient addition of new categories during classification. Results show that classification accuracy still requires improvements, but models generated from one domain are shown to be effectively able to be reused in a different one.

Testing an alternative search algorithm for compound identification with the 'Wiley Registry of Tandem Mass Spectral Data. MSforID'

A tandem mass spectral database system consists of a library of reference spectra and a search program. State-of-the-art search programs show a high tolerance for variability in compound-specific fragmentation patterns produced by collision-induced decomposition and enable sensitive and specific 'identity search'. In this communication, performance characteristics of two search algorithms combined with the 'Wiley Registry of Tandem Mass Spectral Data, MSforID' (Wiley Registry MSMS, John Wiley and Sons, Hoboken, NJ, USA) were evaluated. The search algorithms tested were the MSMS search algorithm implemented in the NIST MS Search program 2.0g (NIST, Gaithersburg, MD, USA) and the MSforID algorithm (John Wiley and Sons, Hoboken, NJ, USA). Sample spectra were acquired on different instruments and, thus, covered a broad range of possible experimental conditions or were generated in silico. For each algorithm, more than 30,000 matches were performed. Statistical evaluation of the library search results revealed that principally both search algorithms can be combined with the Wiley Registry MSMS to create a reliable identification tool. It appears, however, that a higher degree of spectral similarity is necessary to obtain a correct match with the NIST MS Search program. This characteristic of the NIST MS Search program has a positive effect on specificity as it helps to avoid false positive matches (type I errors), but reduces sensitivity. Thus, particularly with sample spectra acquired on instruments differing in their Setup from tandem-in-space type fragmentation, a comparably higher number of false negative matches (type II errors) were observed by searching the Wiley Registry MSMS.

A review of estimation of distribution algorithms in bioinformatics

Evolutionary search algorithms have become an essential asset in the algorithmic toolbox for solving high-dimensional optimization problems in across a broad range of bioinformatics problems. Genetic algorithms, the most well-known and representative evolutionary search technique, have been the subject of the major part of such applications. Estimation of distribution algorithms (EDAs) offer a novel evolutionary paradigm that constitutes a natural and attractive alternative to genetic algorithms. They make use of a probabilistic model, learnt from the promising solutions, to guide the search process. In this paper, we set out a basic taxonomy of EDA techniques, underlining the nature and complexity of the probabilistic model of each EDA variant. We review a set of innovative works that make use of EDA techniques to solve challenging bioinformatics problems, emphasizing the EDA paradigm's potential for further research in this domain.

An application of fuzzy logic for hardware/software partitioning in embedded systems

Hardware/Software partitioning (HSP) is a key task for embedded system co-design. The main goal of this task is to decide which components of an application are to be executed in a general purpose processor (software) and which ones, on a specific hardware, taking into account a set of restrictions expressed by metrics. In last years, several approaches have been proposed for solving the HSP problem, directed by metaheuristic algorithms. However, due to diversity of models and metrics used, the choice of the best suited algorithm is an open problem yet. This article presents the results of applying a fuzzy approach to the HSP problem. This approach is more flexible than many others due to the fact that it is possible to accept quite good solutions or to reject other ones which do not seem good. In this work we compare six metaheuristic algorithms: Random Search, Tabu Search, Simulated Annealing, Hill Climbing, Genetic Algorithm and Evolutionary Strategy. The presented model is aimed to simultaneously minimize the hardware area and the execution time. The obtained results show that Restart Hill Climbing is the best performing algorithm in most cases.

Algoritmos metaheurísticos en el problema del particionado hardware/software de sistemas embebidos

El particionado hardware/software es una tarea fundamental en el co-diseño de sistemas embebidos. En ella se decide, teniendo en cuenta las métricas de diseño, qué componentes se ejecutarán en un procesador de propósito general (software) y cuáles en un hardware específico. En los últimos años se han propuesto diversas soluciones al problema del particionado dirigidas por algoritmos metaheurísticos. Sin embargo, debido a la diversidad de modelos y métricas utilizadas, la elección del algoritmo más apropiado sigue siendo un problema abierto. En este trabajo se presenta una comparación de seis algoritmos metaheurísticos: Búsqueda aleatoria (Random search), Búsqueda tabú (Tabu search), Recocido simulado (Simulated annealing), Escalador de colinas estocástico (Stochastic hill climbing), Algoritmo genético (Genetic algorithm) y Estrategia evolutiva (Evolution strategy). El modelo utilizado en la comparación está dirigido a minimizar el área ocupada y el tiempo de ejecución, las restricciones del modelo son consideradas como penalizaciones para incluir en el espacio de búsqueda otras soluciones. Los resultados muestran que los algoritmos Escalador de colinas estocástico y Estrategia evolutiva son los que mejores resultados obtienen en general, seguidos por el Algoritmo genético.

An Adaptive Genetic Algorithm with Dynamic Population Size for Optimizing Join Queries

The problem of finding the optimal join ordering executing a query to a relational database management system is a combinatorial optimization problem, which makes deterministic exhaustive solution search unacceptable for queries with a great number of joined relations. In this work an adaptive genetic algorithm with dynamic population size is proposed for optimizing large join queries. The performance of the algorithm is compared with that of several classical non-deterministic optimization algorithms. Experiments have been performed optimizing several random queries against a randomly generated data dictionary. The proposed adaptive genetic algorithm with probabilistic selection operator outperforms in a number of test runs the canonical genetic algorithm with Elitist selection as well as two common random search strategies and proves to be a viable alternative to existing non-deterministic optimization approaches.

Índice h de docentes em Saúde Coletiva no Brasil

OBJETIVO: Estimar valores de referência e função de hierarquia de docentes em Saúde Coletiva do Brasil por meio de análise da distribuição do índice h. MÉTODOS: A partir do portal da Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, 934 docentes foram identificados em 2008, dos quais 819 foram analisados. O índice h de cada docente foi obtido na Web of Science mediante algoritmos de busca com controle para homonímias e alternativas de grafia de nome. Para cada região e para o Brasil como um todo ajustou-se função densidade de probabilidade exponencial aos parâmetros média e taxa de decréscimo por região. Foram identificadas medidas de posição e, com o complemento da função probabilidade acumulada, função de hierarquia entre autores conforme o índice h por região. RESULTADOS: Dos docentes, 29,8% não tinham qualquer registro de citação (h = 0). A média de h para o País foi 3,1, com maior média na região Sul (4,7). A mediana de h para o País foi 2,1, também com maior mediana na Sul (3,2). Para uma padronização de população de autores em cem, os primeiros colocados para o País devem ter h = 16; na estratificação por região, a primeira posição demanda valores mais altos no Nordeste, Sudeste e Sul, sendo nesta última h = 24. CONCLUSÕES: Avaliados pelos índices h da Web of Science, a maioria dos autores em Saúde Coletiva não supera h = 5. Há diferenças entres as regiões, com melhor desempenho para a Sul e valores semelhantes entre Sudeste e Nordeste.

Planetary transit candidates in Corot-IRa01 field

Context. CoRoT is a pioneering space mission devoted to the analysis of stellar variability and the photometric detection of extrasolar planets. Aims. We present the list of planetary transit candidates detected in the first field observed by CoRoT, IRa01, the initial run toward the Galactic anticenter, which lasted for 60 days. Methods. We analysed 3898 sources in the coloured bands and 5974 in the monochromatic band. Instrumental noise and stellar variability were taken into account using detrending tools before applying various transit search algorithms. Results. Fifty sources were classified as planetary transit candidates and the most reliable 40 detections were declared targets for follow-up ground-based observations. Two of these targets have so far been confirmed as planets, CoRoT-1b and CoRoT-4b, for which a complete characterization and specific studies were performed.

New simple and efficient heuristics for the uncapacitated single allocation hub location problem

Hub-and-spoke networks are widely studied in the area of location theory. They arise in several contexts, including passenger airlines, postal and parcel delivery, and computer and telecommunication networks. Hub location problems usually involve three simultaneous decisions to be made: the optimal number of hub nodes, their locations and the allocation of the non-hub nodes to the hubs. In the uncapacitated single allocation hub location problem (USAHLP) hub nodes have no capacity constraints and non-hub nodes must be assigned to only one hub. In this paper, we propose three variants of a simple and efficient multi-start tabu search heuristic as well as a two-stage integrated tabu search heuristic to solve this problem. With multi-start heuristics, several different initial solutions are constructed and then improved by tabu search, while in the two-stage integrated heuristic tabu search is applied to improve both the locational and allocational part of the problem. Computational experiments using typical benchmark problems (Civil Aeronautics Board (CAB) and Australian Post (AP) data sets) as well as new and modified instances show that our approaches consistently return the optimal or best-known results in very short CPU times, thus allowing the possibility of efficiently solving larger instances of the USAHLP than those found in the literature. We also report the integer optimal solutions for all 80 CAB data set instances and the 12 AP instances up to 100 nodes, as well as for the corresponding new generated AP instances with reduced fixed costs. Published by Elsevier Ltd.