Softcomputing: Theories and Applications

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

Deadline for manuscript submissions: closed (30 September 2019) | Viewed by 27717

Special Issue Editors

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

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
Guest Editors

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Keywords

  • softcomputing
  • computational intelligence
  • fuzzy logic
  • data analytics
  • decision support

Published Papers (8 papers)

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Research

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13 pages, 291 KiB  
Article
Anti-Intuitionistic Fuzzy Soft a-Ideals Applied to BCI-Algebras
by G. Muhiuddin, D. Al-Kadi and M. Balamurugan
Axioms 2020, 9(3), 79; https://doi.org/10.3390/axioms9030079 - 08 Jul 2020
Cited by 8 | Viewed by 1840
Abstract
The notion of anti-intuitionistic fuzzy soft a-ideals of B C I -algebras is introduced and several related properties are investigated. Furthermore, the operations, namely; AND, extended intersection, restricted intersection, and union on anti-intuitionistic fuzzy soft a-ideals are discussed. Finally, characterizations of anti-intuitionistic fuzzy [...] Read more.
The notion of anti-intuitionistic fuzzy soft a-ideals of B C I -algebras is introduced and several related properties are investigated. Furthermore, the operations, namely; AND, extended intersection, restricted intersection, and union on anti-intuitionistic fuzzy soft a-ideals are discussed. Finally, characterizations of anti-intuitionistic fuzzy soft a-ideals of B C I -algebras are given. Full article
(This article belongs to the Special Issue Softcomputing: Theories and Applications)
18 pages, 450 KiB  
Article
Facility Location Selection for B-Schools in Indian Context: A Multi-Criteria Group Decision Based Analysis
by Sanjib Biswas and Dragan Pamucar
Axioms 2020, 9(3), 77; https://doi.org/10.3390/axioms9030077 - 08 Jul 2020
Cited by 26 | Viewed by 3778
Abstract
Facility location is one of the critical strategic decisions for any organization. It not only carries the organization’s identity but also connects the point of origin and point of consumption. In the case of higher educational institutions, specifically B-Schools, location is one of [...] Read more.
Facility location is one of the critical strategic decisions for any organization. It not only carries the organization’s identity but also connects the point of origin and point of consumption. In the case of higher educational institutions, specifically B-Schools, location is one of the primary concerns for potential students and their parents while selecting an institution for pursuing higher education. There has been a plethora of research conducted to investigate the factors influencing the B-School selection decision-making. However, location as a standalone factor has not been widely studied. This paper aims to explore various location selection criteria from the viewpoint of the candidates who aspire to enroll in B-Schools. We apply an integrated group decision-making framework of pivot pairwise relative criteria importance assessment (PIPRECIA), and level-based weight assessment LBWA is used wherein a group of student counselors, admission executives, and educators from India has participated. The factors which influence the location decision are identified through qualitative opinion analysis. The results show that connectivity and commutation are the dominant issues. Full article
(This article belongs to the Special Issue Softcomputing: Theories and Applications)
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16 pages, 5356 KiB  
Article
Genetic Algorithm for Scheduling Optimization Considering Heterogeneous Containers: A Real-World Case Study
by Gilberto Rivera, Luis Cisneros, Patricia Sánchez-Solís, Nelson Rangel-Valdez and Jorge Rodas-Osollo
Axioms 2020, 9(1), 27; https://doi.org/10.3390/axioms9010027 - 04 Mar 2020
Cited by 24 | Viewed by 4050
Abstract
In this paper, we develop and apply a genetic algorithm to solve surgery scheduling cases in a Mexican Public Hospital. Here, one of the most challenging issues is to process containers with heterogeneous capacity. Many scheduling problems do not share this restriction; because [...] Read more.
In this paper, we develop and apply a genetic algorithm to solve surgery scheduling cases in a Mexican Public Hospital. Here, one of the most challenging issues is to process containers with heterogeneous capacity. Many scheduling problems do not share this restriction; because of this reason, we developed and implemented a strategy for the processing of heterogeneous containers in the genetic algorithm. The final product was named “genetic algorithm for scheduling optimization” (GAfSO). The results of GAfSO were tested with real data of a local hospital. Said hospital assigns different operational time to the operating rooms throughout the week. Also, the computational complexity of GAfSO is analyzed. Results show that GAfSO can assign the corresponding capacity to the operating rooms while optimizing their use. Full article
(This article belongs to the Special Issue Softcomputing: Theories and Applications)
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23 pages, 5366 KiB  
Article
GRSA Enhanced for Protein Folding Problem in the Case of Peptides
by Juan Frausto-Solís, Juan Paulo Sánchez-Hernández, Fanny G. Maldonado-Nava and Juan J. González-Barbosa
Axioms 2019, 8(4), 136; https://doi.org/10.3390/axioms8040136 - 04 Dec 2019
Cited by 2 | Viewed by 3599
Abstract
Protein folding problem (PFP) consists of determining the functional three-dimensional structure of a target protein. PFP is an optimization problem where the objective is to find the structure with the lowest Gibbs free energy. It is significant to solve PFP for use in [...] Read more.
Protein folding problem (PFP) consists of determining the functional three-dimensional structure of a target protein. PFP is an optimization problem where the objective is to find the structure with the lowest Gibbs free energy. It is significant to solve PFP for use in medical and pharmaceutical applications. Hybrid simulated annealing algorithms (HSA) use a kind of simulated annealing or Monte Carlo method, and they are among the most efficient for PFP. The instances of PFP can be classified as follows: (a) Proteins with a large number of amino acids and (b) peptides with a small number of amino acids. Several HSA have been positively applied for the first case, where I-Tasser has been one of the most successful in the CASP competition. PEP-FOLD3 and golden ratio simulated annealing (GRSA) are also two of these algorithms successfully applied to peptides. This paper presents an enhanced golden simulated annealing (GRSA2) where soft perturbations (collision operators), named “on-wall ineffective collision” and “intermolecular ineffective collision”, are applied to generate new solutions in the metropolis cycle. GRSA2 is tested with a dataset for peptides previously proposed, and a comparison with PEP-FOLD3 and I-Tasser is presented. According to the experimentation, GRSA2 has an equivalent performance to those algorithms. Full article
(This article belongs to the Special Issue Softcomputing: Theories and Applications)
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11 pages, 257 KiB  
Article
New Entropy-Based Similarity Measure between Interval-Valued Intuitionstic Fuzzy Sets
by Saida S. Mohamed, Areeg Abdalla and Robert I. John
Axioms 2019, 8(2), 73; https://doi.org/10.3390/axioms8020073 - 18 Jun 2019
Cited by 2 | Viewed by 2565
Abstract
In this paper, we propose a new approach to constructing similarity measures using the entropy measure for Interval-Valued Intuitionistic Fuzzy Sets. In addition, we provide several illustrative examples to demonstrate the practicality and effectiveness of the proposed formula. Finally, we use the new [...] Read more.
In this paper, we propose a new approach to constructing similarity measures using the entropy measure for Interval-Valued Intuitionistic Fuzzy Sets. In addition, we provide several illustrative examples to demonstrate the practicality and effectiveness of the proposed formula. Finally, we use the new proposed similarity measure to develop a new approach for solving problems of pattern recognition and multi-criteria fuzzy decision-making. Full article
(This article belongs to the Special Issue Softcomputing: Theories and Applications)
19 pages, 5187 KiB  
Article
Comparative Study of the Conventional Mathematical and Fuzzy Logic Controllers for Velocity Regulation
by Fevrier Valdez, Oscar Castillo, Camilo Caraveo and Cinthia Peraza
Axioms 2019, 8(2), 53; https://doi.org/10.3390/axioms8020053 - 01 May 2019
Cited by 7 | Viewed by 3475
Abstract
Currently, we are in the digital era, where robotics, with the help of the Internet of Things (IoT), is exponentially advancing, and in the technology market we can find multiple devices for achieving these systems, such as the Raspberry Pi, Arduino, and so [...] Read more.
Currently, we are in the digital era, where robotics, with the help of the Internet of Things (IoT), is exponentially advancing, and in the technology market we can find multiple devices for achieving these systems, such as the Raspberry Pi, Arduino, and so on. The use of these devices makes our work easier regarding processing information or controlling physical mechanisms, as some of these devices have microcontrollers or microprocessors. One of the main challenges in speed control applications is to make the decision to use a fuzzy logic control (FLC) system instead of a conventional controller system, such as a proportional integral (PI) or a proportional integral-derivative (PID). The main contribution of this paper is the design, integration, and comparative study of the use of these three types of controllers—FLC, PI, and PID—for the speed control of a robot built using the Lego Mindstorms EV3 kit. The root mean square error (RMSE) and the settling time were used as metrics to validate the performance of the speed control obtained with the controllers proposed in this paper. Full article
(This article belongs to the Special Issue Softcomputing: Theories and Applications)
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30 pages, 1942 KiB  
Article
A New gH-Difference for Multi-Dimensional Convex Sets and Convex Fuzzy Sets
by Luciano Stefanini and Barnabas Bede
Axioms 2019, 8(2), 48; https://doi.org/10.3390/axioms8020048 - 24 Apr 2019
Cited by 11 | Viewed by 4170
Abstract
In the setting of Minkowski set-valued operations, we study generalizations of the difference for (multidimensional) compact convex sets and for fuzzy sets on metric vector spaces, extending the Hukuhara difference. The proposed difference always exists and allows defining Pompeiu-Hausdorff distance for the space [...] Read more.
In the setting of Minkowski set-valued operations, we study generalizations of the difference for (multidimensional) compact convex sets and for fuzzy sets on metric vector spaces, extending the Hukuhara difference. The proposed difference always exists and allows defining Pompeiu-Hausdorff distance for the space of compact convex sets in terms of a pseudo-norm, i.e., the magnitude of the difference set. A computational procedure for two dimensional sets is outlined and some examples of the new difference are given. Full article
(This article belongs to the Special Issue Softcomputing: Theories and Applications)
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Review

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21 pages, 3926 KiB  
Review
A Study Concerning Soft Computing Approaches for Stock Price Forecasting
by Chao Shi and Xiaosheng Zhuang
Axioms 2019, 8(4), 116; https://doi.org/10.3390/axioms8040116 - 18 Oct 2019
Cited by 9 | Viewed by 3219
Abstract
Financial time-series are well known for their non-linearity and non-stationarity nature. The application of conventional econometric models in prediction can incur significant errors. The fast advancement of soft computing techniques provides an alternative approach for estimating and forecasting volatile stock prices. Soft computing [...] Read more.
Financial time-series are well known for their non-linearity and non-stationarity nature. The application of conventional econometric models in prediction can incur significant errors. The fast advancement of soft computing techniques provides an alternative approach for estimating and forecasting volatile stock prices. Soft computing approaches exploit tolerance for imprecision, uncertainty, and partial truth to progressively and adaptively solve practical problems. In this study, a comprehensive review of latest soft computing tools is given. Then, examples incorporating a series of machine learning models, including both single and hybrid models, to predict prices of two representative indexes and one stock in Hong Kong’s market are undertaken. The prediction performances of different models are evaluated and compared. The effects of the training sample size and stock patterns (viz. momentum and mean reversion) on model prediction are also investigated. Results indicate that artificial neural network (ANN)-based models yield the highest prediction accuracy. It was also found that the determination of optimal training sample size should take the pattern and volatility of stocks into consideration. Large prediction errors could be incurred when stocks exhibit a transition between mean reversion and momentum trend. Full article
(This article belongs to the Special Issue Softcomputing: Theories and Applications)
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