Word Storms: Multiples of Word Clouds for Visual Comparison of Documents

Word clouds are a popular tool for visualizing documents, but they are not a good tool for comparing documents, because identical words are not presented consistently across different clouds. We introduce the concept of word storms, a visualization tool for analysing corpora of documents. A word storm is a group of word clouds, in which each cloud represents a single document, juxtaposed to allow the viewer to compare and contrast the documents. We present a novel algorithm that creates a coordinated word storm, in which words that appear in multiple documents are placed in the same location, using the same color and orientation, in all of the corresponding clouds. In this way, similar documents are represented by similar-looking word clouds, making them easier to compare and contrast visually. We evaluate the algorithm in two ways: first, an automatic evaluation based on document classification; and second, a user study. The results confirm that unlike standard word clouds, a coordinated word storm better allows for visual comparison of documents

Using iterated local search for solving the flow-shop problem: parametrization, randomization and parallelization issues

Iterated local search (ILS) is a powerful framework for developing efficient algorithms for the permutation flow‐shop problem (PFSP). These algorithms are relatively simple to implement and use very few parameters, which facilitates the associated fine‐tuning process. Therefore, they constitute an attractive solution for real‐life applications. In this paper, we discuss some parallelization, parametrization, and randomization issues related to ILS‐based algorithms for solving the PFSP. In particular, the following research questions are analyzed: (a) Is it possible to simplify even more the parameter setting in an ILS framework without affecting performance? (b) How do parallelized versions of these algorithms behave as we simultaneously vary the number of different runs and the computation time? (c) For a parallelized version of these algorithms, is it worthwhile to randomize the initial solution so that different starting points are considered? (d) Are these algorithms affected by the use of a “good‐quality” pseudorandom number generator? In this paper, we introduce the new ILS‐ESP (where ESP is efficient, simple, and parallelizable) algorithm that is specifically designed to take advantage of parallel computing, allowing us to obtain competitive results in “real time” for all tested instances. The ILS‐ESP also uses “natural” parameters, which simplifies the calibration process. An extensive set of computational experiments has been carried out in order to answer the aforementioned research questions.This work has been partially supported by the Spanish Ministry of Science and Innovation (grants TRA2010-21644-C03, ECO2009-11307, and DPI2007-61371), and by the CYTEDHAROSA Network (http://dpcs.uoc.edu)

Using iterated local search for solving the flow-shop problem: Parallelization, parametrization, and randomization issues

Iterated local search (ILS) is a powerful framework for developing efficient algorithms for the permutation flow-shop problem (PFSP). These algorithms are relatively simple to implement and use very few parameters, which facilitates the associated fine-tuning process. Therefore, they constitute an attractive solution for real- life applications. In this paper, we discuss some parallelization, parametrization, and randomization issues related to ILS-based algorithms for solving the PFSP. In particular, the following research questions are analyzed: (a) Is it possible to simplify even more the parameter setting in an ILS framework without affecting performance? (b) How do parallelized versions of these algorithms behave as we simultaneously vary the number of different runs and the computation time? (c) For a parallelized version of these algorithms, is it worthwhile to randomize the initial solution so that different starting points are considered? (d) Are these algorithms affected by the use of a “good-quality” pseudorandom number generator? In this paper, we introduce the new ILS-ESP (where ESP is efficient, simple, and parallelizable) algorithm that is specifically designed to take advantage of parallel computing, allowing us to obtain competitive results in “real time” for all tested instances. The ILS-ESP also uses “natural” parameters, which simplifies the calibration process. An extensive set of computational experiments has been carried out in order to answer the aforementioned research questionsPeer Reviewe