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Swarm Intelligence with Mathematical Fuzzy Logic for Computer Science in...
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Swarm Intelligence with Mathematical Fuzzy Logic for Computer Science in Real-World Applications

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

Deadline for manuscript submissions: closed (20 June 2023) | Viewed by 17061

Special Issue Editor

Dr. Fevrier Valdez

E-Mail Website
Guest Editor
Tijuana Institute of Technology, TecNM, Tijuana 22379, Mexico
Interests: optimization algorithms; swarm intelligence; bio-inspired algorithms; fuzzy logic; neural networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Optimization methods based on swarm intelligence are a recent topic of research based on using bio-inspired behavior to solve complex optimization in computational intelligence.

Several approaches have recently been developed in this area, such as particle swarm optimization, bat algorithm, ant colony optimization, bee colony, dolphin algorithm, wolf search, flower pollination algorithm, firefly, mayfly, ant colony, cuckoo search, termite colony, and cat swarm. However, determining how to design efficient methods and how to use these algorithms for real-world application problems is still an open issue—in particular, path planning of mobile robots by neural networks, the design of neuro-fuzzy models such as fuzzy neural networks, fuzzy parameter adaptation in control systems, and intuitionistic fuzzy neural networks have received some interest. In addition, new emerging neural models have recently been proposed. In all these models, a common problem is determining how to obtain an optimal topology, which can be handled by bio-inspired optimization algorithms.

This Special Issue invites researchers to report their latest research work on the development of new improved bio-inspired algorithms, or new applications of existing methods in the design of topologies of neural models, parameter adaptation  in control systems and path planning of robots, etc., with ultimate goal of exploring future research directions.

Potential themes include but are not limited to the following:

  • Theoretical methods for understanding the behavior of bio-inspired algorithms;
  • Novel nature-inspired or application-inspired optimization algorithms;
  • Statistical approaches for understanding the behavior of nature-inspired methods;
  • Optimization of neuro-fuzzy models;
  • Optimization of mathematical fuzzy logic models;
  • Optimization of emergent neural models with nature-inspired algorithms;
  • Mathematical fuzzy logic and intelligent and automatic control;
  • Methods based on collective intelligence;
  • Fuzzy control design for an autonomous mobile robot using optimization algorithms;
  • Parameter adaptation using mathematical fuzzy models;
  • Other bio-inspired methods and their applications.

Prof. Dr. Fevrier Valdez
Guest Editor

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

  • swarm intelligence
  • mathematical fuzzy logic
  • optimization methods
  • neural networks
  • computational intelligence

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Published Papers (9 papers)

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Research

14 pages, 4030 KiB  
Article
Understanding the Axioms and Assumptions of Logical Mathematical Systems through Raster Images: Application to the Construction of a Likert Scale
by Queralt Viladevall, Salvador Linares-Mustarós, Maria Antonia Huertas and Joan-Carles Ferrer-Comalat
Axioms 2023, 12(12), 1064; https://doi.org/10.3390/axioms12121064 - 21 Nov 2023
Viewed by 1249
Abstract
This article presents different artistic raster images as a resource for correcting misconceptions about different laws and assumptions that underlie the propositional systems of binary logic, Łukasiewicz’s trivalent logic, Peirce’s trivalent logic, Post’s n-valent logic, and Black and Zadeh’s infinite-valent logic. Recognizing similarities [...] Read more.
This article presents different artistic raster images as a resource for correcting misconceptions about different laws and assumptions that underlie the propositional systems of binary logic, Łukasiewicz’s trivalent logic, Peirce’s trivalent logic, Post’s n-valent logic, and Black and Zadeh’s infinite-valent logic. Recognizing similarities and differences in how images are constructed allows us to deepen, through comparison, the laws of bivalence, non-contradiction, and excluded middle, as well as understanding other multivalent logic assumptions from another perspective, such as their number of truth values. Consequently, the first goal of this article is to illustrate how the use of visualization can be a powerful tool for better understanding some logic systems. To demonstrate the utility of this objective, we illustrate how a deeper understanding of logic systems helps us appreciate the necessity of employing Likert scales based on the logic of Post or Zadeh, which is the second goal of the article. Full article
(This article belongs to the Special Issue Swarm Intelligence with Mathematical Fuzzy Logic for Computer Science in Real-World Applications)
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12 pages, 7733 KiB  
Article
Viability Analysis of Tidal Turbine Installation Using Fuzzy Logic: Case Study and Design Considerations
by Ángel M. Rodríguez-Pérez, César A. Rodríguez, Alba Márquez-Rodríguez and Julio J. Caparros Mancera
Axioms 2023, 12(8), 778; https://doi.org/10.3390/axioms12080778 - 11 Aug 2023
Cited by 13 | Viewed by 1710
Abstract
Tidal energy represents a clean and sustainable source of energy generation that can address renewable energy challenges, especially the global challenge of optimizing alternatives for stable supply. Although tidal stream energy extraction technology is in the early stages of development, it shows great [...] Read more.
Tidal energy represents a clean and sustainable source of energy generation that can address renewable energy challenges, especially the global challenge of optimizing alternatives for stable supply. Although tidal stream energy extraction technology is in the early stages of development, it shows great potential compared to other renewable energy sources. The main objective of this research is to provide a digital tool for the optimization of the installation of turbines through fuzzy logic. The methodology in this study includes the design and development of a fuzzy-logic-based tool for this purpose. Design criteria included parameters such as salinity, temperature, currents, depth, and water viscosity, which affect the performance of tidal turbines. These parameters are obtained from the geographic location of the installation. A decision-making system is provided to support the tool. The designed fuzzy logic system evaluates the suitability of different turbine locations and presents the results through graphics and probability of success percentages. The results indicate that currents and temperatures are the most limiting factors in terms of potential turbine locations. The program provides a practical and efficient tool for optimizing the selection of tidal turbines and generating energy from ocean currents. This tool is evaluated and validated through different cases. With this approach, the aim is to encourage the development of tidal energy and its adoption worldwide. Full article
(This article belongs to the Special Issue Swarm Intelligence with Mathematical Fuzzy Logic for Computer Science in Real-World Applications)
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17 pages, 1464 KiB  
Article
An Intelligent Fuzzy MCDM Model Based on D and Z Numbers for Paver Selection: IMF D-SWARA—Fuzzy ARAS-Z Model
by Stanislav Jovanović, Edmundas Kazimieras Zavadskas, Željko Stević, Milan Marinković, Adel F. Alrasheedi and Ibrahim Badi
Axioms 2023, 12(6), 573; https://doi.org/10.3390/axioms12060573 - 8 Jun 2023
Cited by 4 | Viewed by 1646
Abstract
One of the most important challenges when building road infrastructure is the selection of appropriate mechanization, on which the efficiency of construction and the life of exploitation depends largely. As construction machinery, pavers occupy a significant place in civil engineering projects, so their [...] Read more.
One of the most important challenges when building road infrastructure is the selection of appropriate mechanization, on which the efficiency of construction and the life of exploitation depends largely. As construction machinery, pavers occupy a significant place in civil engineering projects, so their selection, depending on a road category, is a very important activity. The objective of this paper is to develop an intelligent Fuzzy MCDM (Multi-Criteria Decision-Making) model, which consists of the integration of D and Z numbers for the selection of construction machinery. The IMF D-SWARA (Improved Fuzzy D Step-Wise Weight Assessment Ratio Analysis) method was used to determine weighting coefficients. A novel Fuzzy ARAS-Z (Additive Ratio Assessment) method has been developed to determine an adequate paver for a lower category of roads (asphalt width up to 5 m), which represents an important contribution and novelty of the paper. A total of 10 alternatives were evaluated based on 16 criteria which were classified into 4 main groups. The results have shown that the alternative A8—SUPER 1300-3 represents a paver with the best characteristics for the considered set of parameters. After that, verification tests were calculated, and they include a comparative analysis with four other MCDM methods based on Z numbers, a change in the normalization procedure, and the impact of changing the size of an initial fuzzy matrix. The tests showed the stability of the developed model with negligible deviations. Full article
(This article belongs to the Special Issue Swarm Intelligence with Mathematical Fuzzy Logic for Computer Science in Real-World Applications)
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27 pages, 2779 KiB  
Article
Pseudo-Quasi Overlap Functions and Related Fuzzy Inference Methods
by Mei Jing and Xiaohong Zhang
Axioms 2023, 12(2), 217; https://doi.org/10.3390/axioms12020217 - 19 Feb 2023
Cited by 6 | Viewed by 1377
Abstract
The overlap function, a particular kind of binary aggregate function, has been extensively utilized in decision-making, image manipulation, classification, and other fields. With regard to overlap function theory, many scholars have also obtained many achievements, such as pseudo-overlap function, quasi-overlap function, semi-overlap function, [...] Read more.
The overlap function, a particular kind of binary aggregate function, has been extensively utilized in decision-making, image manipulation, classification, and other fields. With regard to overlap function theory, many scholars have also obtained many achievements, such as pseudo-overlap function, quasi-overlap function, semi-overlap function, etc. The above generalized overlap functions contain commutativity and continuity, which makes them have some limitations in practical applications. In this essay, we give the definition of pseudo-quasi overlap functions by removing the commutativity and continuity of overlap functions, and analyze the relationship of pseudo-t-norms and pseudo-quasi overlap functions. Moreover, we present a structure method for pseudo-quasi overlap functions. Then, we extend additive generators to pseudo-quasi overlap functions, and we discuss additive generators of pseudo-quasi overlap functions. The results show that, compared with the additive generators generated by overlap functions, the additive generators generated by pseudo-quasi overlap functions have fewer restraint conditions. In addition, we also provide a method for creating quasi-overlap functions by utilizing pseudo-t-norms and pseudo automorphisms. Finally, we introduce the idea of left-continuous pseudo-quasi overlap functions, and we study fuzzy inference triple I methods of residual implication operators induced by left-continuous pseudo-quasi overlap functions. On the basis of the above, we give solutions of pseudo-quasi overlap function fuzzy inference triple I methods based on FMP (fuzzy modus ponens) and FMT (fuzzy modus tollens) problems. Full article
(This article belongs to the Special Issue Swarm Intelligence with Mathematical Fuzzy Logic for Computer Science in Real-World Applications)
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16 pages, 4168 KiB  
Article
Pitch Control of Wind Turbine Blades Using Fractional Particle Swarm Optimization
by Ali Karami-Mollaee and Oscar Barambones
Axioms 2023, 12(1), 25; https://doi.org/10.3390/axioms12010025 - 26 Dec 2022
Cited by 2 | Viewed by 1857
Abstract
To achieve the maximum power from wind in variable-speed regions of wind turbines (WTs), a suitable control signal should be applied to the pitch angle of the blades. However, the available uncertainty in the modeling of WTs complicates calculations of these signals. To [...] Read more.
To achieve the maximum power from wind in variable-speed regions of wind turbines (WTs), a suitable control signal should be applied to the pitch angle of the blades. However, the available uncertainty in the modeling of WTs complicates calculations of these signals. To cope with this problem, an optimal controller is suitable, such as particle swarm optimization (PSO). To improve the performance of the controller, fractional order PSO (FPSO) is proposed and implemented. In order to construct this approach for a two-mass WT, we propose a new state feedback, which was first applied to the turbine. The idea behind this state feedback was based on the Taylor series. Then, a linear model with uncertainty was obtained with a new input control signal. Thereafter, the conventional PSO (CPSO) and FPSO were used as optimal controllers for the resulting linear model. Finally, a comparison was performed between CPSO and FPSO and the fuzzy Takagi–Sugeno–Kang (TSK) inference system. The provided comparison demonstrates the advantages of the Taylor series with combination to these controllers. Notably, without the state feedback, CPSO, FPSO, and TSK fuzzy systems cannot stabilize WTs in tracking the desired trajectory. Full article
(This article belongs to the Special Issue Swarm Intelligence with Mathematical Fuzzy Logic for Computer Science in Real-World Applications)
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14 pages, 2161 KiB  
Article
An Improved Whale Optimization Algorithm for Web Service Composition
by Fadl Dahan
Axioms 2022, 11(12), 725; https://doi.org/10.3390/axioms11120725 - 13 Dec 2022
Cited by 3 | Viewed by 1704
Abstract
In the current circumstance, the Web Service Composition (WSC) was introduced to address complex user needs concerning the Quality of Services (QoS). In the WSC problem, the user needs are divided into a set of tasks. The corresponding web services are retrieved from [...] Read more.
In the current circumstance, the Web Service Composition (WSC) was introduced to address complex user needs concerning the Quality of Services (QoS). In the WSC problem, the user needs are divided into a set of tasks. The corresponding web services are retrieved from the web services discovery according to the functionality of each task, and have different non-functional constraints, such as QoS. The WSC problem is a multi-objective optimization problem and is classified as an NP-hard problem. The whale optimization algorithm (WOA) is proven to solve complex multi-objective optimization problems, and it has the advantage of easy implementation with few control parameters. In this work, we contribute to improving the WOA algorithm, where different strategies are introduced to enhance its performance and address its shortcomings, namely its slow convergence speed, which produces low solution accuracy for the WSC problem. The proposed algorithm is named Improved Whale Optimization Algorithm (IWOA) and has three different strategies to enhance the performance of the WOA. Firstly, the Sine chaos theory is proposed to initiate the WOA’s population and enhance the initialization diversity. Secondly, a Lévy flight mechanism is proposed to enhance the exploitation and exploration of WOA by maintaining the whales’ diversity. Further, a neighborhood search mechanism is introduced to address the trade-off between exploration and exploitation searching mechanisms. Different experiments are conducted with datasets on 12 different scales (small, medium, and large), and the proposed algorithm is compared with standard WOA and five state-of-the-art swarm-based algorithms on 30 different independent runs. Furthermore, four evaluation criteria are used to validate the comparison: the average fitness value, best fitness values, standard deviation, and average execution time. The results show that the IWOA enhanced the WOA algorithm’s performance, where it got the better average and best fitness values with a low variation on all datasets. However, it ranked second regarding average execution time after the WOA, and sometimes third after the WOA and OABC, which is reasonable because of the proposed strategies. Full article
(This article belongs to the Special Issue Swarm Intelligence with Mathematical Fuzzy Logic for Computer Science in Real-World Applications)
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12 pages, 1389 KiB  
Article
Decision Optimization for Water and Electricity Shared Resources Based on Fusion Swarm Intelligence
by Xiaohua Yang, Hao Yang, Jing Bao, Xin Shen, Rong Yan and Nan Pan
Axioms 2022, 11(10), 493; https://doi.org/10.3390/axioms11100493 - 23 Sep 2022
Cited by 3 | Viewed by 1430
Abstract
As one of the most important water conservancy projects, reservoirs use water resources to achieve essential functions, such as irrigation, flood control, and power generation, by intercepting rivers. As climate extremes and global warming increase, the world’s water reserves are being tested, and [...] Read more.
As one of the most important water conservancy projects, reservoirs use water resources to achieve essential functions, such as irrigation, flood control, and power generation, by intercepting rivers. As climate extremes and global warming increase, the world’s water reserves are being tested, and reservoir operators are being challenged. This paper investigates the optimal allocation of shared resources for hydropower to achieve rational decisions for reservoir operations. Firstly, a power resource model is constructed based on the real hydroelectric generator theory. Furthermore, based on the established power resource model combined with the influence of weather type and multi-region heterogeneous demand, this paper constructs a multi-objective hydropower shared resource allocation optimization model, with the lowest hydropower resource supply cost and the shortest time hydropower resource supply time as the optimization objectives. Secondly, for the problem that the traditional population intelligence algorithm easily falls into the local optimum when solving complex problems, the improvement of the MOPSO algorithm is completed by introducing the Levy flight strategy and differential evolution. Finally, simulation experiments were carried out, and cutting-edge algorithms, such as the GA algorithm and WOA algorithm, were selected for simulation comparison to verify the effectiveness of the constructed model and algorithm. The simulation results show that the research in this paper can contribute to effective decision-making for reservoir operators and promote intelligent reservoir operation. Full article
(This article belongs to the Special Issue Swarm Intelligence with Mathematical Fuzzy Logic for Computer Science in Real-World Applications)
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27 pages, 3515 KiB  
Article
A New Discrete Mycorrhiza Optimization Nature-Inspired Algorithm
by Hector Carreon-Ortiz, Fevrier Valdez and Oscar Castillo
Axioms 2022, 11(8), 391; https://doi.org/10.3390/axioms11080391 - 9 Aug 2022
Cited by 5 | Viewed by 2242
Abstract
This paper presents the discrete version of the Mycorrhiza Tree Optimization Algorithm (MTOA), using the Lotka–Volterra Discrete Equation System (LVDES) formed by the Predator–Prey, Cooperative and Competitive Models. The Discrete Mycorrhizal Optimization Algorithm (DMOA) is a stochastic metaheuristic that integrates randomness in its [...] Read more.
This paper presents the discrete version of the Mycorrhiza Tree Optimization Algorithm (MTOA), using the Lotka–Volterra Discrete Equation System (LVDES) formed by the Predator–Prey, Cooperative and Competitive Models. The Discrete Mycorrhizal Optimization Algorithm (DMOA) is a stochastic metaheuristic that integrates randomness in its search processes. These algorithms are inspired by nature, specifically by the symbiosis between plant roots and a fungal network called the Mycorrhizal Network (MN). The communication in the network is performed using chemical signals of environmental conditions and hazards, the exchange of resources, such as Carbon Dioxide (CO2) that plants perform through photosynthesis to the MN and to other seedlings or growing plants. The MN provides water (H2O) and nutrients to plants that may or may not be of the same species; therefore, the colonization of plants in arid lands would not have been possible without the MN. In this work, we performed a comparison with the CEC-2013 mathematical functions between MTOA and DMOA by conducting Hypothesis Tests to obtain the efficiency and performance of the algorithms, but in future research we will also propose optimization experiments in Neural Networks and Fuzzy Systems to verify with which methods these algorithms perform better. Full article
(This article belongs to the Special Issue Swarm Intelligence with Mathematical Fuzzy Logic for Computer Science in Real-World Applications)
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25 pages, 11133 KiB  
Article
Software Defects Prediction Based on Hybrid Beetle Antennae Search Algorithm and Artificial Bee Colony Algorithm with Comparison
by Ya Zhang, Tong Li, Zhen Li, Yu-Mei Wu and Hong Miao
Axioms 2022, 11(7), 305; https://doi.org/10.3390/axioms11070305 - 24 Jun 2022
Cited by 2 | Viewed by 1681
Abstract
Software defects are problems in software that destroy normal operation ability and reflect the quality of the software. Software fault can be predicted by the software reliability model. In this paper, the hybrid algorithm is applied to parameter estimation in software defect prediction. [...] Read more.
Software defects are problems in software that destroy normal operation ability and reflect the quality of the software. Software fault can be predicted by the software reliability model. In this paper, the hybrid algorithm is applied to parameter estimation in software defect prediction. As a biological heuristic algorithm, BAS (Beetle Antennae Search Algorithm) has fast convergence speed and is easy to implement. ABC (Artificial Bee Colony Algorithm) is better in optimization and has strong robustness. In this paper, the BAS-ABC hybrid algorithm is proposed by mixing the two algorithms and the goal of the proposed method is to improve the convergence and stability of the algorithm. Five datasets were used to carry out the experiments, and the data results showed that the hybrid algorithm was more accurate than the single algorithm, with stronger convergence and stability, so it was more suitable for parameter estimation of the software reliability model. Meanwhile, this paper implemented the comparison between hybrid BAS + ABC and hybrid PSO + SSA, and the result shows that BAS + ABC has better performance both in convergence and stability. The comparison result shows the strong ability in estimation and prediction of software defects hybrid BAS and ABC. Full article
(This article belongs to the Special Issue Swarm Intelligence with Mathematical Fuzzy Logic for Computer Science in Real-World Applications)
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