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Special Issue "Softcomputing: Theories and Applications II"

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A special issue of Axioms (ISSN 2075-1680). This special issue belongs to the section "Logic".

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 13842

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Special Issue Editors

Prof. Dr. Rafael Alejandro Espin Andrade

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Guest Editor

Accounting and Administration Faculty, Autonomous University of Coahuila, Torreón 27298, Mexico
Interests: fuzzy logic; compensatory fuzzy logic; business analytics; decision making; games theory
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Witold Pedrycz

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Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 2E1, Canada
Interests: fuzzy set theory; pattern clustering; learning (artificial intelligence); decision making; granular
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Laura Cruz-Reyes
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Guest Editor

Instituto Tecnológico de Ciudad Madero, División de Estudios de Posgrado e Investigación, Juventino Rosas y Jesús Urueta, C.P. 89440 Cd. Madero, Tamps., Mexico
Interests: intelligent optimisation; algorithmics; evolutionary computation; machine learning; multicriteria decision and logistics

Special Issue Information

Dear Colleagues,

Softcomputing or Computational Intelligence is a very open scientific area which emerged in the second half of the 20th century, changing dramatically the space of mathematical and computational modelling, especially in the areas of decision making, data analytics, Artificial Intelligence, machine learning, and automated control.

Softcomputing offered a new perspective very much based on intuition, characterized by hybrid solutions and intelligent methods frequently inspired from natural connectionist and evolutive metaphors, but more and more associated to new axiomatic developments which incorporate a mathematical compass to the construction of new useful theoretical spaces with strong impact.

Papers with softcomputing theoretical approaches and diverse applications are brought together in this Special Issue.

Papers gather softcomputing classical approaches like fuzzy logic, neural network probabilistic modelling, support vector machines, and rough sets, new theoretical approaches to them, and applications in the framework of multicriteria decision making, outranking, optimization, games theory, coalition analysis, and other disciplines.

A wide range of management and technology problems in business and organizations like supply chain risk management, portfolio selection, sorting, trading, image treatment, risk management, design of processes and products, failure dynamics study, big data optimization, text mining, public policies, political collaboration in campaigns, human capital, competences management, customer relationship management, corporate open data assessing, decision support in medicine, public policies, social wellbeing analysis, evaluation of energy efficiency, video indexing, retrieval based on content, signal processing, coalition analysis, group decision making, minimum cost consensus, etc. are presented using the mentioned methods and mixing them.

Prof. Dr. Rafael Alejandro Espin Andrade
Prof. Dr. Witold Pedrycz
Prof. Dr. Laura Cruz-Reyes
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Axioms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Softcomputing
Computational intelligence
Fuzzy logic
Data analytics
Decision support

Published Papers (7 papers)

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Research

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Open AccessArticle

Three Hybrid Scatter Search Algorithms for Multi-Objective Job Shop Scheduling Problem

Leo Hernández-Ramírez

Juan Frausto-Solís

Guadalupe Castilla-Valdez

Javier González-Barbosa

and

Juan-Paulo Sánchez Hernández

Axioms 2022, 11(2), 61; https://doi.org/10.3390/axioms11020061 - 31 Jan 2022

Cited by 2 | Viewed by 1586

Abstract

The Job Shop Scheduling Problem (JSSP) consists of finding the best scheduling for a set of jobs that should be processed in a specific order using a set of machines. This problem belongs to the NP-hard class problems and has enormous industrial applicability. In the manufacturing area, decision-makers consider several criteria to elaborate their production schedules. These cases are studied in multi-objective optimization. However, few works are addressed from this multi-objective perspective. The literature shows that multi-objective evolutionary algorithms can solve these problems efficiently; nevertheless, multi-objective algorithms have slow convergence to the Pareto optimal front. This paper proposes three multi-objective Scatter Search hybrid algorithms that improve the convergence speed evolving on a reduced set of solutions. These algorithms are: Scatter Search/Local Search (SS/LS), Scatter Search/Chaotic Multi-Objective Threshold Accepting (SS/CMOTA), and Scatter Search/Chaotic Multi-Objective Simulated Annealing (SS/CMOSA). The proposed algorithms are compared with the state-of-the-art algorithms IMOEA/D, CMOSA, and CMOTA, using the MID, Spacing, HV, Spread, and IGD metrics; according to the experimental results, the proposed algorithms achieved the best performance. Notably, they obtained a 47% reduction in the convergence time to reach the optimal Pareto front. Full article

(This article belongs to the Special Issue Softcomputing: Theories and Applications II)

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Open AccessArticle

SAIPO-TAIPO and Genetic Algorithms for Investment Portfolios

Juan Frausto Solis

José L. Purata Aldaz

Manuel González del Angel

Javier González Barbosa

and

Guadalupe Castilla Valdez

Axioms 2022, 11(2), 42; https://doi.org/10.3390/axioms11020042 - 21 Jan 2022

Cited by 1 | Viewed by 1513

Abstract

The classic model of Markowitz for designing investment portfolios is an optimization problem with two objectives: maximize returns and minimize risk. Various alternatives and improvements have been proposed by different authors, who have contributed to the theory of portfolio selection. One of the most important contributions is the Sharpe Ratio, which allows comparison of the expected return of portfolios. Another important concept for investors is diversification, measured through the average correlation. In this measure, a high correlation indicates a low level of diversification, while a low correlation represents a high degree of diversification. In this work, three algorithms developed to solve the portfolio problem are presented. These algorithms used the Sharpe Ratio as the main metric to solve the problem of the aforementioned two objectives into only one objective: maximization of the Sharpe Ratio. The first, GENPO, used a Genetic Algorithm (GA). In contrast, the second and third algorithms, SAIPO and TAIPO used Simulated Annealing and Threshold Accepting algorithms, respectively. We tested these algorithms using datasets taken from the Mexican Stock Exchange. The findings were compared with other mathematical models of related works, and obtained the best results with the proposed algorithms. Full article

(This article belongs to the Special Issue Softcomputing: Theories and Applications II)

Open AccessArticle

Smart Competence Management Using Business Analytics with Fuzzy Predicates

Roberto Peña Luna

Gregorio Garza Rodríguez

Liliana Angélica Guerrero Ramos

Rafael Alejandro Espín Andrade

Sandra Rodríguez Figueredo

and

Victor de-León-Gómez

Axioms 2021, 10(4), 280; https://doi.org/10.3390/axioms10040280 - 28 Oct 2021

Cited by 1 | Viewed by 1920

Abstract

Organizations consider human capital as one of their most important assets. Experts in the field have focused on the research and development of human talent management skills. At present, companies are giving high importance to the management of this intangible resource. Management by competencies and skills is basic in the selection and development of the most valuable asset the organization has: its human capital. A conceptual framework of the intelligent management of human capital and some more advanced knowledge discovery techniques are presented in this paper. A methodology for smart detection of core competencies based on fuzzy logic predicates and business analytics is proposed. The proposed methodology allows: (1) the evaluation of the importance of competencies, (2) the identification of competencies achievement level of each employee, (3) the identification of competencies with difficulties, (4) the identification of competencies that have influence on others, and (5) a hierarchization of the competencies to select the most appropriated for the employee recruitment plan. Furthermore, an analysis is proposed using knowledge discovery, which allows one to identify which competences have influence on a specific one. All of the above is useful to build an ideal profile for a position. A case study was carried out in order to show the implementation and interpretation of our proposal. Full article

(This article belongs to the Special Issue Softcomputing: Theories and Applications II)

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Open AccessArticle

Rough Approximation Operators on a Complete Orthomodular Lattice

Songsong Dai

Axioms 2021, 10(3), 164; https://doi.org/10.3390/axioms10030164 - 27 Jul 2021

Cited by 3 | Viewed by 1430

Abstract

This paper studies rough approximation via join and meet on a complete orthomodular lattice. Different from Boolean algebra, the distributive law of join over meet does not hold in orthomodular lattices. Some properties of rough approximation rely on the distributive law. Furthermore, we [...] Read more.

(This article belongs to the Special Issue Softcomputing: Theories and Applications II)

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Open AccessArticle

Transdisciplinary Scientific Strategies for Soft Computing Development: Towards an Era of Data and Business Analytics

Rafael A. Espin-Andrade

Witold Pedrycz

Efrain Solares

and

Laura Cruz-Reyes

Axioms 2021, 10(2), 93; https://doi.org/10.3390/axioms10020093 - 18 May 2021

Cited by 1 | Viewed by 1686

Abstract

This research is a review and analysis paper that offers a transdisciplinary, methodological, and strategic vision for soft computing development towards a wider favorable impact in data analytics. Strategies are defined, explained, and illustrated by examples. The paper also shows how these strategies are expressed in three dimensions of an ambitious actions plan. They are all integrated into a master strategy called wide knowledge discovery, which offers a way towards the augmented analytics paradigm. Some contributions of this work are defining what kind of mathematical elements should be introduced into soft computing towards a better impact on the area of data analytics, offering orientation towards building new mathematical elements, and defining why and how they can be introduced. Full article

(This article belongs to the Special Issue Softcomputing: Theories and Applications II)

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Open AccessArticle

A Compensatory Fuzzy Logic Model in Technical Trading

Norma P. Rodríguez-Cándido

Rafael A. Espin-Andrade

Efrain Solares

and

Witold Pedrycz

Axioms 2021, 10(1), 36; https://doi.org/10.3390/axioms10010036 - 18 Mar 2021

Cited by 1 | Viewed by 2522

Abstract

This work presents a novel approach to prediction of financial asset prices. Its main contribution is the combination of compensatory fuzzy logic and the classical technical analysis to build an efficient prediction model. The interpretability properties of the model allow its users to incorporate and consider virtually any set of rules from technical analysis, in addition to the investors’ knowledge related to the actual market conditions. This knowledge can be incorporated into the model in the form of subjective assessments made by investors. Such assessments can be obtained, for example, from the graphical analysis commonly performed by traders. The effectiveness of the model was assessed through its systematic application in the stock and cryptocurrency markets. From the results, we conclude that when the model shows a high degree of recommendation, the actual financial assets show high effectiveness. Full article

(This article belongs to the Special Issue Softcomputing: Theories and Applications II)

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Review

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Open AccessReview

The State of the Art of Data Mining Algorithms for Predicting the COVID-19 Pandemic

Keila Vasthi Cortés-Martínez

Hugo Estrada-Esquivel

Alicia Martínez-Rebollar

Yasmín Hernández-Pérez

and

Javier Ortiz-Hernández

Axioms 2022, 11(5), 242; https://doi.org/10.3390/axioms11050242 - 23 May 2022

Cited by 2 | Viewed by 1696

Abstract

Current computer systems are accumulating huge amounts of information in several application domains. The outbreak of COVID-19 has increased rekindled interest in the use of data mining techniques for the analysis of factors that are related to the emergence of an epidemic. Data mining techniques are being used in the analysis and interpretation of information, which helps in the discovery of patterns, planning of isolation policies, and even predicting the speed of proliferation of contagion in a viral disease such as COVID-19. This research provides a comprehensive study of various data mining algorithms that are used in conjunction with epidemiological prediction models. The document considers that there is an opportunity to improve or develop tools that offer an accurate prognosis in the management of viral diseases through the use of data mining tools, based on a comparative study of 35 research papers. Full article

(This article belongs to the Special Issue Softcomputing: Theories and Applications II)

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