Journal:Informatica
Volume 27, Issue 2 (2016), pp. 335–349
Abstract
We investigate the problem of detecting a point set’s deviation from uniformity in the unit hypercube. High uniformity is for example desirable in Monte Carlo methods for numerical integration, but also for obtaining a good worst-case bound in global optimization. In high dimensions, many points are required to get reliable results, so the point sets are preferably generated by fast methods such as quasirandom sequences. Unfortunately, assessing their uniformity often requires quadratic time. So, we present several numerical summary characteristics of point sets that can be computed in linear time. They do not measure uniformity directly, but by comparing them to reference values for the uniform distribution, deviations from uniformity can be quickly detected. The necessary reference values are also derived here, if possible exactly, else approximately.
Journal:Informatica
Volume 27, Issue 2 (2016), pp. 323–334
Abstract
This paper reviews the interplay between global optimization and probability models, concentrating on a class of deterministic optimization algorithms that are motivated by probability models for the objective function. Some complexity results are described for the univariate and multivariate cases.
Journal:Informatica
Volume 27, Issue 2 (2016), pp. 299–322
Abstract
We propose a heuristic global optimization technique which combines combinatorial and continuous local search. The combinatorial component, based on Reactive Search Optimization, generates a trajectory of binary strings describing search districts. Each district is evaluated by random sampling and by selective runs of continuous local search. A reactive prohibition mechanisms guarantees that the search is not stuck at locally optimal districts.
The continuous stochastic local search is based on the Inertial Shaker method: candidate points are generated in an adaptive search box and a moving average of the steps filters out evaluation noise and high-frequency oscillations.
The overall subdivision of the input space in a tree of non-overlapping search districts is adaptive, with a finer subdivision in the more interesting input zones, potentially leading to lower local minima.
Finally, a portfolio of independent CoRSO search streams (P-CoRSO) is proposed to increase the robustness of the algorithm.
An extensive experimental comparison with Genetic Algorithms and Particle Swarm demonstrates that CoRSO and P-CoRSO reach results which are fully competitive and in some cases significantly more robust.
Journal:Informatica
Volume 27, Issue 2 (2016), pp. 283–297
Abstract
A comparison of two nonlinear input-output models describing the relationship between human emotion (excitement, frustration and engagement/boredom) signals and a virtual 3D face feature (distance-between-eyes) is introduced in this paper. A method of least squares with projection to stability domain for the building of stable models with the least output prediction error is proposed. Validation was performed with seven volunteers, and three types of inputs. The results of the modelling showed relatively high prediction accuracy of excitement, frustration and engagement/boredom signals.
Journal:Informatica
Volume 27, Issue 2 (2016), pp. 257–281
Abstract
The estimation of intrinsic dimensionality of high-dimensional data still remains a challenging issue. Various approaches to interpret and estimate the intrinsic dimensionality are developed. Referring to the following two classifications of estimators of the intrinsic dimensionality – local/global estimators and projection techniques/geometric approaches – we focus on the fractal-based methods that are assigned to the global estimators and geometric approaches. The computational aspects of estimating the intrinsic dimensionality of high-dimensional data are the core issue in this paper. The advantages and disadvantages of the fractal-based methods are disclosed and applications of these methods are presented briefly.
Journal:Informatica
Volume 27, Issue 2 (2016), pp. 229–256
Abstract
This is a survey of the main achievements in the methodology and theory of stochastic global optimization. It comprises two complimentary directions: global random search and the methodology based on the use of stochastic models about the objective function. The main attention is paid to theoretically substantiated methods and mathematical results proven in the last 25 years.
Journal:Informatica
Volume 27, Issue 1 (2016), pp. 203–229
Abstract
This paper reviews the existing definitions and formulas of entropy for interval-valued intuitionistic fuzzy sets (IVIFSs) and demonstrates that they cannot fully capture the uncertainty of IVIFSs. Then considering both fuzziness and intuitionism of IVIFSs, we introduce a novel axiomatic definition of entropy for IVIFSs and develop several entropy formulas. Example analyses show that the developed entropy formulas can fully reflect both fuzziness and intuitionism of IVIFSs. Furthermore, based on the entropy formulas of IVIFSs, a method is proposed to solve multi-attribute decision making problems with IVIFSs. Additionally, an investment alternative selection example is provided to validate the practicality and effectiveness of the method.
Journal:Informatica
Volume 27, Issue 1 (2016), pp. 179–202
Abstract
This paper proposes the concept of an interval neutrosophic hesitant fuzzy set (INHFS) and the operational relations of INHFSs. Then, we develop correlation coefficients of INHFSs and investigate the relation between the similarity measures and the correlation coefficients. Furthermore, a multiple attribute decision making method based on the correlation coefficients is established under interval neutrosophic hesitant fuzzy environment. Through the correlation coefficients between each alternative and the ideal alternative, we obtain the ranking order of all alternatives and the best one. Finally, an illustrative example of investment alternatives is given to demonstrate the application and effectiveness of the developed approach.
Journal:Informatica
Volume 27, Issue 1 (2016), pp. 161–178
Abstract
In this work, we study the efficiency of developed OpenFOAM-based parallel solver for the simulation of heat transfer in and around the electrical power cables. First benchmark problem considers three cables directly buried in the soil. We study and compare the efficiency of conjugate gradient solver with diagonal incomplete Cholesky (DIC) preconditioner, generalized geometric-algebraic multigrid GAMG solver from OpenFOAM and conjugate gradient solver with GAMG multigrid solver used as preconditioner. The convergence and parallel scalability of the solvers are presented and analyzed on quadrilateral and acute triangle meshes. Second benchmark problem considers a more complicated case, when cables are placed into plastic pipes, which are buried in the soil. Then a coupled multi-physics problem is solved, which describes the heat transfer in cables, air and soil. Non-standard parallelization approach is presented for multi-physics solver. We show the robustness of selected parallel preconditioners. Parallel numerical tests are performed on the cluster of multicore computers.
Journal:Informatica
Volume 27, Issue 1 (2016), pp. 141–159
Abstract
In this paper we establish some properties of fuzzy quasi-pseudo-metric spaces. An important result is that any partial ordering can be defined by a fuzzy quasi-metric, which can be applied both in theoretical computer science and in information theory, where it is usual to work with sequences of objects of increasing information. We also obtain decomposition theorems of a fuzzy quasi-pseudo metric into a right continuous and ascending family of quasi-pseudo metrics. We develop a topological foundation for complexity analysis of algorithms and programs, and based on our results a fuzzy complexity space can be considered. Also, we built a fertile ground to study some types of fuzzy quasi-pseudo-metrics on the domain of words, which play an important role on denotational semantics, and on the poset of all closed formal balls on a metric space.