1,720,957 research outputs found
An enhanced differential evolution algorithm with adaptation of switching crossover strategy for continuous optimization
No full textDesigning an efficient optimization method which also has a simple structure is generally required by users for its applications to a wide range of practical problems. In this research, an enhanced differential evolution algorithm with adaptation of switching crossover strategy (DEASC) is proposed as a general-purpose population-based optimization method for continuous optimization problems. DEASC extends the solving ability of a basic differential evolution algorithm (DE) whose performance significantly depends on user selection of the control parameters: scaling factor, crossover rate and population size. Like the original DE, the proposed method is aimed at e ciency, simplicity and robustness. The appropriate population size is selected to work in accordance with good choices of the scaling factors. Then, the switching crossover strategy of using low or high crossover rates are incorporated and adapted to suit the problem being solved. In this manner, the adaptation strategy is just a convenient add-on mechanism. To verify the performance of DEASC, it is tested on several benchmark problems of various types and di culties, and compared with some well-known methods in the literature. It is also applied to solve some practical systems of nonlinear equations. Despite its much simpler algorithmic structure, the experimental results show that DEASC greatly enhances the basic DE. It is able to solve all the test problems with fast convergence speed and overall outperforms the compared methods which have more complicated structures. In addition, DEASC also shows promising results on high dimensional test functions
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Adaptive differential evolution algorithm with a pheromone-based learning strategy for global continuous optimization
No full textDifferential evolution algorithm (DE) is a well-known population-based method for solving continuous optimization problems. It has a simple structure and is easy to adapt to a wide range of applications. However, with suitable population sizes, its performance depends on the two main control parameters: scaling factor (F ) and crossover rate (CR). The classical DE method can achieve high performance by a time-consuming tunning process or a sophisticated adaptive control implementation. We propose in this paper an adaptive differential evolution algorithm with a pheromone-based learning strategy (ADE-PS) inspired by ant colony optimization (ACO). The ADE-PS embeds a pheromone-based mechanism that manages the probabilities associated with the partition values of F and CR. It also introduces a resetting strategy to reset the pheromone at a specific time to unlearn and relearn the progressing search. The preliminary experiments find a suitable number of subintervals (ns) for partitioning the control parameter ranges and the reset period (rs) for resetting the pheromone. Then the comparison experiments evaluate ADE-PS using the suitable ns and rs against some adaptive DE methods in the literature. The results show that ADE-PS is more reliable and outperforms several well-known methods in the literature
ADAPTACYJNY RÓŻNICZKOWY ALGORYTM EWOLUCYJNY ZE STRATEGIĄ DOSTOSOWYWANIA GRANIC DO ROZWIĄZYWANIA NIELINIOWYCH PROBLEMÓW IDENTYFIKACJI PARAMETRÓW
Get PDFReal-world parameter identification problems require determining the bounds that cover the unknown solutions. This paper presents an adaptive differential evolution algorithm with a bound adjustment strategy (ADEBAS) for solving nonlinear parameter identification problems. The adjustment strategy detects the parameter-bound violations of mutant vectors during the evolution process and gradually extends the bounds. The algorithm adaptively uses two mutation strategies and two ranges of crossover rate to balance the population diversity and convergence speed. Experimental results show that ADEBAS can solve 24 nonlinear regression tasks from the National Institute of Standards and Technology benchmark with accurate estimation and reliability. It also outperforms the compared methods on real-world parameter identification problems.Problemy identyfikacji parametrów w świecie rzeczywistym wymagają określenia granic, które pokrywają nieznane rozwiązania. W artykule przedstawiono adaptacyjny różniczkowy algorytm ewolucyjny ze strategią dostosowywania granic (ADEBAS) do rozwiązywania nieliniowych problemów identyfikacji parametrów. Strategia dostosowywania wykrywa naruszenia granic parametrów zmutowanych wektorów podczas procesu ewolucji i stopniowo rozszerza granice. Algorytm adaptacyjnie wykorzystuje dwie strategie mutacji i dwa zakresy szybkości krzyżowania, aby zrównoważyć różnorodność populacji i szybkość zbieżności. Wyniki eksperymentów pokazują, że ADEBAS może rozwiązać 24 zadania regresji nieliniowej z benchmarku National Institute of Standards and Technology z dokładnym oszacowaniem i niezawodnością. Przewyższa również porównywane metody w rzeczywistych problemach identyfikacji parametrów
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Differential evolution with adaptive mutation and crossover strategies for nonlinear regression problems
Get PDFThis paper presents the differential evolution algorithm with adaptive mutation and crossover strategies (DEAMC) for solving nonlinear regression problems. The DEAMC algorithm adaptively uses two mutation strategies and two ranges of crossover rate. We evaluate its performance on the National Institute of Standards and Technology (NIST) nonlinear-regression benchmark containing many models of varying levels of difficulty and compare it with classic differential evolution (DE), enhanced differential evolution algorithm with an adaptation of switching crossover strategy (DEASC), and controlled random search methods (CRS4HC, CRS4HCe). We also apply the proposed method to solve parameter identification applications and compare it with enhanced chaotic grasshopper optimization algorithms (ECGOA), self-adaptive differential evolution with dynamic mutation and pheromone strategy (SDE-FMP), and JAYA and its variant methods. The experimental results show that DEAMC is more reliable and gives more accurate results than the compared methods
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Self-adaptive differential evolution algorithm with dynamic fitness-ranking mutation and pheromone strategy
Get PDFDifferential evolution (DE) is a population-based optimization algorithm widely used to solve a variety of continuous optimization problems. The self-adaptive DE algorithm improves the DE by encoding individual parameters to produce and propagate better solutions. This paper proposes a self-adaptive differential evolution algorithm with dynamic fitness-ranking mutation and pheromone strategy (SDE-FMP). The algorithm introduces the dynamical mutation operation using the fitness rank of the individuals to divide the population into three groups and then select groups and their vectors with adaptive probabilities to create a mutant vector. Mutation and crossover operations use the encoded scaling factor and the crossover rate values in a target vector to generate the corresponding trial vector. The values are changed according to the pheromone when the trial vector is inferior in the selection, whereas the pheromone is increased when the trial vector is superior. In addition, the algorithm also employs the resetting operation to unlearn and relearn the dominant pheromone values in the progressing search. The proposed SDE-FMP algorithm using the suitable resetting periods is compared with the well-known adaptive DE algorithms on several test problems. The results show that SDE-FMP can give high-precision solutions and outperforms the compared methods
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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