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Axioms
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Softcomputing: Theories and Applications II
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Softcomputing: Theories and Applications II

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 18176

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

E-Mail Website
Guest Editor
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

E-Mail Website
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 2400 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

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

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Research

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27 pages, 682 KiB  
Article
Three Hybrid Scatter Search Algorithms for Multi-Objective Job Shop Scheduling Problem
by 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 6 | Viewed by 3128
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. [...] Read more.
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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20 pages, 368 KiB  
Article
SAIPO-TAIPO and Genetic Algorithms for Investment Portfolios
by 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 4 | Viewed by 3496
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 [...] Read more.
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)
10 pages, 766 KiB  
Article
Rough Approximation Operators on a Complete Orthomodular Lattice
by Songsong Dai
Axioms 2021, 10(3), 164; https://doi.org/10.3390/axioms10030164 - 27 Jul 2021
Cited by 4 | Viewed by 2364
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 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 study the relationship among the distributive law, rough approximation and orthomodular lattice-valued relation. Full article
(This article belongs to the Special Issue Softcomputing: Theories and Applications II)
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18 pages, 678 KiB  
Article
Transdisciplinary Scientific Strategies for Soft Computing Development: Towards an Era of Data and Business Analytics
by 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 3075
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 [...] Read more.
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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Review

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18 pages, 1085 KiB  
Review
The State of the Art of Data Mining Algorithms for Predicting the COVID-19 Pandemic
by 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 3 | Viewed by 3905
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 [...] Read more.
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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