Editorial
Computer science and engineering

https://doi.org/10.1016/j.jocs.2018.04.005Get rights and content

Abstract

During previous decades, modern approaches to Operational Research (OR), Computation and Optimization have attracted a growing number of scientists, pedagogues, advisors, deciders and practitioners. Actually, very strong and smart (“intelligent”) computational techniques arose for handling a very large number of real-world challenges and crises, in particular, in relation with the main areas of optimization in theory and practical use. Those real problems are characterized by their tremendous non-convexity and complexity. In the communities of OR, Analytics and Computation worldwide, researchers and investigators from different fields, spectra, continents and regions have cooperated to find smart answers for those striking and urgent difficulties. Within that scientific global family, there are scholars from IFORS, its regional groupings and, inside of them, national OR societies and working groups. High increase of human population, feeling of being lonely or lost, problems with the fast process of “globalization”, related “losses” everywhere on the globe, illness, societal conflicts and wars, prove that the “Human Factor” ought to be considered and taken into account much more than in past eras by researchers, educators, leaders, engineers, officials, and also medical doctors, advisers and trainers. Important representations and incorporations of the “Human Factor” through Intelligence, Humanity and Humility should be hosted to a rising degree in the areas of OR, Analytics and Computation.

Introduction

In the presence of an environment full of turbulences, classical and traditional approaches are employed very much to receive “holistic” answers and solutions with a high level of fulfilment in situations of real-life application of modern OR. Hence, innovative global optimization and analytics approaches are necessary to sustainably deal with the challenges aforementioned. One class of such methods are modern programming, computing and optimization methods which establish a generic and responsive, robust and prosperous toolbox for overcoming of crises and challenges caused by complexity and non-convexity in monitoring, calculating and optimizing, in supervising, supporting and accompanying real-world projects of economies and finance domains, on natural reserves and of high-tech enterprises, of politics, NGOs, charity sectors and, eventually, for health and happiness of humans, in social entities and in communities.

With the special issues [[1], [2], [3], [4], [5]] and their works included we recommend, we propose the reader to obtain a first understanding and, what is more, feeling and inspiration about this challenging field where Operational Research, Computation and Optimization meet the real-world on subjects that strongly appeal to the use of quantitative methods with a strong inclusion of the “Human Factor” as well. Those special issues range among subjects on Sustainable Development and Developing Countries [1], Computational Biology, Bioinformatics and Medicine [2], Power Control, Energy Sector and Electricity Markets [3], Environmental Management [4], Systems, Decision Making, Collaborative Work and Learning [5].

In the following please find a short introduction of the articles of this novel and pioneering issue of premium Journal of Computational Science.

Various distortions in the source data not only decrease the overall identification performance of a biometric system, but also alter the optimal parameters of a template creation method. In this article [6], the influence of distortions to wavelength and spread parameters of the wavelets is presented. There are 3 types of source data degrading factors investigated: image blurring, image noise and iris segmentation errors. Therefore, 2 popular methods of template creation, namely, Gabor and Log-Gabor transforms, are involved. CASIA and NDIRIS public domain databases are used for tests. It is shown that the optimum wavelength is strongly altered by image degradation, whereas the optimal ratio of wavelength to spread, defining filter shape, stays constant almost.

The problems of production scheduling and sequencing refer to decision making regarding the designation of jobs to available resources and their subsequent order to optimize pre-defined performance measures. From the early days of research in this area until this last decade, the publication of case studies has been scarce, with their frequency increasing just very recently. With the survey [7] the authors aim to highlight practical research and case studies published in the literature on scheduling, identifying the main characteristics of the problems treated, trends in this research and also gaps showing potential areas for future study.

In this article [8], the authors investigate the possibility of unique mutual fusion of evolutionary algorithms, complex networks, “strange dynamics” and hidden attractors. As it was shown in many research papers, evolutionary algorithms are capable of very complex tasks such as chaotic system control, identification or synthesis and vice versa; chaos can also be observed in the evolutionary dynamics, as originally demonstrated by various researchers. The authors submit a novel approach on how to analyze and control the dynamics of the evolutionary algorithm and discuss the possibility of a strange dynamics analysis which could become a part of the dynamic of evolutionary algorithms. Herewith, they propose an understanding of algorithms as a discrete dynamical system which exhibits a wide spectrum of the behavior that can be controlled and analyzed.

Let P be a poset and S be a set of elements. A well-known method for representing P is called a bit-vector encoding and consists in associating to each element of P a subset of S such that the order relation between two elements coincides with their subsets inclusion. The size of this encoding is |S| and it determines both space and time needed to store and compare the poset’s elements. As a consequence, this encoding has found applications for handling hierarchies in, e.g., databases, distributed computing and object-oriented programming languages. The computation of the smallest size of a bit-vector encoding of a poset, called 2-dimension, is an NP-hard problem. Thus, research projects deal with heuristics that provide tight bounds or an approximation of this parameter. The article [9] introduces new classes of trees, where that dimension 2 is also known or 2-approximated. Furthermore, it presents a new heuristic for partial orders encoding by modular decomposition. This unified process is a 4-approximation for the 2-dimension of rooted trees and yields a reduced encoding by nearly 40% for series-parallel posets. Herewith, it is competitive and even improves the best results for general posets.

The large amount of text information on the Internet and in modern applications makes it complicated to deal with this vast volume of information. Text clustering is an appropriate tool to deal with an enormous amount of text documents by subdividing those documents into coherent groups. Document size decreases the effectiveness of the text clustering technique. Subsequently, text documents contain sparse and uninformative (i.e., noisy, irrelevant, and unnecessary) features, which reduce the effectiveness of the text clustering technique. Feature selection technique is a primary unsupervised learning method to select the informative text features for creating a new subset of a document’s features. This method is used to increase the effectiveness of the underlying clustering algorithm. Recently, several complex optimization problems could be successfully solved by using metaheuristic algorithms. The article [10] proposes a novel feature selection method: feature selection method, using particle swarm optimization (PSO) algorithm (FSPSOTC) to resolve the feature selection problem by creating a new subset of informative text features. This subset can enhance the performance of the text clustering technique and reduce the computational time. Experiments were conducted using 6 standard text datasets with several characteristics. The results revealed that the proposed method (FSPSOTC) enhanced the effectiveness of the text clustering technique by dealing with a new subset of informative features. The proposed method is compared with other well-known algorithms, namely, feature selection method using a genetic algorithm to improve the text clustering (FSGATC), and feature selection method using the harmony search algorithm to improve the text clustering (FSHSTC) in the text feature selection.

Sensitive information can be endangered by critical risks whenever it is communicated through computer networks. The ability of cybercriminals to hide their attacking intention obstructs existing protection systems, causing the system to be fail from preventing any possible sabotage in network systems. The authors of the contribution [11] propose a similarity approach for Attack Intention Recognition using Fuzzy Min-Max Neural Network (SAIRF). In particular, SAIRF aims to recognize attack intention in real time. This approach classifies attacks according to their characteristics and uses a similar metric method to identify motives of attacks and predict their intentions. Here, network attack intentions are categorized into specific and general intentions. General intentions are recognized by investigating violations against security metrics of confidentiality, integrity, availability and authenticity. Specific intentions are recognized by investigating network attacks used to cause a violation. The results obtained by the authors demonstrate the capability of their approach to investigate similarity of network attack evidence and to recognize the intentions of the attack investigated.

Section snippets

Conclusion

Our Special Issue of Journal of Computational Science means an offer of new views and interpretations of modelling and solving; it could become a valuable resource for graduate and postgraduate students, for decision makers and investigators in private arenas, in universities and emerging industries, connected with a diversity of sciences, high-tech and managerial fields, among them mathematics and statistics, economics and management sciences, OR and game theory, physics and chemistry,

Acknowledgments

We, as the Guest Editors, express our heartily thanks to all the authors for their important contributions and to the reviewers for their valuable service, care and helpful comments. Eventually, we are very grateful to the Editor-in-Chief Prof. Dr. Peter Sloot of prestigious Journal of Computational Science for the opportunity of preparing this special issue, and to the co-workers of Elsevier, namely, Mrs. Hilda Xu and Mrs. Chitra Krishnamoorthy, who were with us, guided and helped us at every

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