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Setting the Future of Smart Industry 4.0 with Metaheuristic Algorithms
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Setting the Future of Smart Industry 4.0 with Metaheuristic Algorithms

A special issue of Mathematical and Computational Applications (ISSN 2297-8747). This special issue belongs to the section "Engineering".

Deadline for manuscript submissions: closed (20 January 2023) | Viewed by 13513

Special Issue Editors

Dr. Wali Khan Mashwani

E-Mail Website
Guest Editor
Institute of Numerical Sciences, Academic Block-III, Kohat University of Science & Technology (KUST), Kohat 26000, KPK, Pakistan
Interests: multi-objective evolutionary algorithm
Dr. Atila Göktaş

E-Mail Website
Guest Editor
Department of Statistics, Faculty of Science, Mugla Sıtkı Koçman University, Mugla 48000, Turkey
Interests: robust methods; big data analytics
Dr. Zakia Hammouch

E-Mail Website
Guest Editor
Ecole Normale Supéerieure de Meknés, Université Moulay Ismail, Meknes 50000, Morocco
Interests: fractional partial differential equations; fractional calculus; modeling; simulation methods
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent days, Industry 4.0 has been powering the acquisition of precise data from diverse phytoconstituents and technologies, allowing them to connect with others. Industry 4.0 has been subject to much pressure to change and improve digital manufacturing. By linking virtual elements, it continually updates old industrial methods and opens unparalleled opportunities for its success. In the interconnected world of industrialised societies, Industry 4.0 is part of the plans to re-establish economic cost structures via streamlining of output using common rooms. Industry 4.0 is the latest technological software for interactive connectivity of equipment and industrial activities. It is based on developments in computer technology and the combination of massive volumes of data.

Industry 4.0 transforms manufacturing operations from a controlled to a federated instance of strategic planning by providing technological capabilities through integrating of those same highly integrated devices. The widespread use of intelligent devices in increasingly adaptable and interconnected production or manufacturing ecosystems characterises Industry 4.0. Industry 4.0 is defined by constant connectivity among devices and companies throughout the factory. The requirement for money allows preparing for the adoption of Industry 4.0 networks at the neighbourhood scale. The difficulty of determining that payback is a fundamental impediment to digitalisation, but this drives many businesses to invest. The protection of digital information is another potential concern for businesses. Some protocols would be required to enable interaction amongst expert machines while preventing outside interference. Firms need to ensure that their processes remain secure such that they avoid leaks of data that might damage their profitability and result in a lack of privacy regarding user information. Thus, areas for future research focus include the following: increasing the effectiveness of metaheuristics by decreasing the number of devices designed to accomplish for every mission; extending modelling through separating probability vibrations under distinct distress aspects so the initial network of broadcast media is extensive and might lead to a massive rise in computation; and additional sophisticated artificial intelligence techniques as the concept of an expanded range of attributes, which will include explorations of planning and reprogramming processes using algorithms and deep learning, a topic that has attracted our particular interest. Another problem is the lack of explicit guidelines about information methodological approaches. Industry 4.0, the industrial revolution, has been implemented in several highly developed countries. The deployment of an Industry 4.0 policy for organisations represents a violent uprising from a range of disciplinary perspectives. We invite papers that set the future of Smart Industry 4.0, with metaheuristics being an overarching theme.

Specific topics of interest include the following.

  • Intel core computers using industrial internet software in development around a digital production process
  • Company issues and remedies for the information technology and application of disruptive forces in the fourth industrial revolution (4.0)
  • Metaheuristic algorithms improving the region of the component level for wise scale functionality production
  • Power system techniques in emerging brains with information systems analysis
  • Longitudinal 5G research and exploration: Industry 4.0, implementation of digital
  • Towards technology, a multiple metaheuristic algorithm for adaptive truck path optimisation
  • Development using cloud and data processing at containers at a low cost in industry 4.0
  • Industry 4.0: algorithms and automation for manufacturing schedule
  • Programming issues within Industry 4.0: approaching smart industrial production
  • Economic forecast using multiple sensors and algorithms: Developments and prospects throughout Industry 4.0
  • Integrated evaluation and the potential for process improvement from the standpoint of experts in Industry 4.0
  • Corporation’s business existence and the continuous improvement of functions in the digital age
  • A hardware resources scheduling strategy based on metaheuristics on the internet using a wildlife optimization method

Dr. Wali Khan Mashwani
Dr. Atila Göktaş
Dr. Zakia Hammouch
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. Mathematical and Computational Applications is an international peer-reviewed open access semimonthly 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 1400 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.

Published Papers (4 papers)

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Research

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21 pages, 6550 KiB  
Article
Analysis of Multi-Stacked Dielectric Resonator Antenna with Its Equivalent R-L-C Circuit Modeling for Wireless Communication Systems
by Ram Krishna, Agbotiname Lucky Imoize, Rajveer Singh Yaduvanshi, Harendra Singh, Arun Kumar Rana and Subhendu Kumar Pani
Math. Comput. Appl. 2023, 28(1), 4; https://doi.org/10.3390/mca28010004 - 29 Dec 2022
Cited by 2 | Viewed by 1702
Abstract
The dielectric resonator antenna (DRA) can be modeled as a series and parallel combination of electrical networks consisting of a resistor (R), inductor (L), and capacitor (C) to address peculiar challenges in antennas suitable for application in emerging wireless communication systems for higher [...] Read more.
The dielectric resonator antenna (DRA) can be modeled as a series and parallel combination of electrical networks consisting of a resistor (R), inductor (L), and capacitor (C) to address peculiar challenges in antennas suitable for application in emerging wireless communication systems for higher frequency range. In this paper, a multi-stacked DRA has been proposed. The performance and characteristic features of the DRA have been analyzed by deriving the mathematical formulations for dynamic impedance, input impedance, admittance, bandwidth, and quality factor for fundamental and high-order resonant modes. Specifically, the performance of the projected multi-stacked DRA was analyzed in MATLAB and a high-frequency structure simulator (HFSS). Generally, results indicate that variation in the permittivity of substrates, such as high and low, can potentially increase and decrease the quality factor, respectively. In particular, the impedance, radiation fields and power flow have been demonstrated using the proposed multi-stacked electrical network of R, L, and C components coupled with a suitable transformer. Overall, the proposed multi-stacked DRA network shows an improved quality factor and selectivity, and bandwidth is reduced reasonably. The multi-stacked DRA network would find useful applications in radio frequency wireless communication systems. Additionally, for enhancing the impedance, BW of DRA a multi-stacked DRA is proposed by the use of ground-plane techniques with slots, dual-segment, and stacked DRA. The performance of multi-stacked DRA is improved by a factor of 10% as compared to existing models in terms of better flexibility, moderate gain, compact size, bandwidth, quality factor, resonant frequency, frequency impedance at the resonance frequency, and the radiation pattern with Terahertz frequency range. Full article
(This article belongs to the Special Issue Setting the Future of Smart Industry 4.0 with Metaheuristic Algorithms)
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28 pages, 7117 KiB  
Article
Multi-Strategy Improved Sparrow Search Algorithm and Application
by Xiangdong Liu, Yan Bai, Cunhui Yu, Hailong Yang, Haoning Gao, Jing Wang, Qing Chang and Xiaodong Wen
Math. Comput. Appl. 2022, 27(6), 96; https://doi.org/10.3390/mca27060096 - 17 Nov 2022
Cited by 4 | Viewed by 1796
Abstract
The sparrow search algorithm (SSA) is a metaheuristic algorithm developed based on the foraging and anti-predatory behavior of sparrow populations. Compared with other metaheuristic algorithms, SSA also suffers from poor population diversity, has weak global comprehensive search ability, and easily falls into local [...] Read more.
The sparrow search algorithm (SSA) is a metaheuristic algorithm developed based on the foraging and anti-predatory behavior of sparrow populations. Compared with other metaheuristic algorithms, SSA also suffers from poor population diversity, has weak global comprehensive search ability, and easily falls into local optimality. To address the problems whereby the sparrow search algorithm tends to fall into local optimum and the population diversity decreases in the later stage of the search, an improved sparrow search algorithm (PGL-SSA) based on piecewise chaotic mapping, Gaussian difference variation, and linear differential decreasing inertia weight fusion is proposed. Firstly, we analyze the improvement of six chaotic mappings on the overall performance of the sparrow search algorithm, and we finally determine the initialization of the population by piecewise chaotic mapping to increase the initial population richness and improve the initial solution quality. Secondly, we introduce Gaussian difference variation in the process of individual iterative update and use Gaussian difference variation to perturb the individuals to generate a diversity of individuals so that the algorithm can converge quickly and avoid falling into localization. Finally, linear differential decreasing inertia weights are introduced globally to adjust the weights so that the algorithm can fully traverse the solution space with larger weights in the first iteration to avoid falling into local optimum, and we enhance the local search ability with smaller weights in the later iteration to improve the search accuracy of the optimal solution. The results show that the proposed algorithm has a faster convergence speed and higher search accuracy than the comparison algorithm, the global search capability is significantly enhanced, and it is easier to jump out of the local optimum. The improved algorithm is also applied to the Heating, Ventilation and Air Conditioning (HVAC) system control optimization direction, and the improved algorithm is used to optimize the parameters of the HVAC system Proportion Integral Differential (PID) controller. The results show that the PID controller optimized by the improved algorithm has higher control accuracy and system stability, which verifies the feasibility of the improved algorithm in practical engineering applications. Full article
(This article belongs to the Special Issue Setting the Future of Smart Industry 4.0 with Metaheuristic Algorithms)
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32 pages, 8839 KiB  
Article
Shadowed Type-2 Fuzzy Sets in Dynamic Parameter Adaption in Cuckoo Search and Flower Pollination Algorithms for Optimal Design of Fuzzy Fault-Tolerant Controllers
by Himanshukumar R. Patel and Vipul A. Shah
Math. Comput. Appl. 2022, 27(6), 89; https://doi.org/10.3390/mca27060089 - 28 Oct 2022
Cited by 10 | Viewed by 1418
Abstract
In recent, various metaheuristic algorithms have shown significant results in control engineering problems; moreover, fuzzy sets (FSs) and theories were frequently used for dynamic parameter adaption in metaheuristic algorithms. The primary reason for this is that fuzzy inference system (FISs) can be designed [...] Read more.
In recent, various metaheuristic algorithms have shown significant results in control engineering problems; moreover, fuzzy sets (FSs) and theories were frequently used for dynamic parameter adaption in metaheuristic algorithms. The primary reason for this is that fuzzy inference system (FISs) can be designed using human knowledge, allowing for intelligent dynamic adaptations of metaheuristic parameters. To accomplish these tasks, we proposed shadowed type-2 fuzzy inference systems (ST2FISs) for two metaheuristic algorithms, namely cuckoo search (CS) and flower pollination (FP). Furthermore, with the advent of shadowed type-2 fuzzy logic, the abilities of uncertainty handling offer an appealing improved performance for dynamic parameter adaptation in metaheuristic methods; moreover, the use of ST2FISs has been shown in recent works to provide better results than type-1 fuzzy inference systems (T1FISs). As a result, ST2FISs are proposed for adjusting the Lèvy flight (P) and switching probability (P′) parameters in the original cuckoo search (CS) and flower pollination (FP) algorithms, respectively. Our approach investigated trapezoidal types of membership functions (MFs), such as ST2FSs. The proposed method was used to optimize the precursors and implications of a two-tank non-interacting conical frustum tank level (TTNCFTL) process using an interval type-2 fuzzy controller (IT2FLC). To ensure that the implementation is efficient compared with the original CS and FP algorithms, simulation results were obtained without and then with uncertainty in the main actuator (CV1) and system component (leak) at the bottom of frustum tank two of the TTNCFLT process. In addition, the statistical z-test and non-parametric Friedman test are performed to analyze and deliver the findings for the best metaheuristic algorithm. The reported findings highlight the benefits of employing this approach over traditional general type-2 fuzzy inference systems since we get superior performance in the majority of cases while using minimal computational resources. Full article
(This article belongs to the Special Issue Setting the Future of Smart Industry 4.0 with Metaheuristic Algorithms)
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24 pages, 1912 KiB  
Review
A Review of Artificial Intelligence and Machine Learning for Incident Detectors in Road Transport Systems
by Samuel Olugbade, Stephen Ojo, Agbotiname Lucky Imoize, Joseph Isabona and Mathew O. Alaba
Math. Comput. Appl. 2022, 27(5), 77; https://doi.org/10.3390/mca27050077 - 13 Sep 2022
Cited by 15 | Viewed by 7893
Abstract
Road transport is the most prone to accidents, resulting in significant fatalities and injuries. It also faces a plethora of never-ending problems, such as the frequent loss of lives and valuables during an accident. Appropriate actions need to be taken to address these [...] Read more.
Road transport is the most prone to accidents, resulting in significant fatalities and injuries. It also faces a plethora of never-ending problems, such as the frequent loss of lives and valuables during an accident. Appropriate actions need to be taken to address these problems, such as the establishment of an automatic incident detection system using artificial intelligence and machine learning. This article explores the overview of artificial intelligence and machine learning in facilitating automatic incident detector systems to decrease road accidents. The study examines the critical problems and potential remedies for reducing road traffic accidents and the application of artificial intelligence and machine learning in road transportation systems. More, new, and emerging trends that reduce frequent accidents in the transportation sector are discussed extensively. Specifically, the study organized the following sub-topics: an incident detector with machine learning and artificial intelligence and road management with machine learning and artificial intelligence. Additionally, safety is the primary concern of road transport; the internet of vehicles and vehicle ad hoc networks, including the use of wireless communication technologies such as 5G wireless networks and the use of machine learning and artificial intelligence for road transportation systems planning, are elaborated. Key findings from the review indicate that route optimization, cargo volume forecasting, predictive fleet maintenance, real-time vehicle tracking, and traffic management are critical to safeguarding road transportation systems. Finally, the paper summarizes the challenges facing the application of artificial intelligence in road transport systems, highlights the research trends, identifies the unresolved questions, and highlights the essential research takeaways. The work can serve as reference material for road transport system planning and management. Full article
(This article belongs to the Special Issue Setting the Future of Smart Industry 4.0 with Metaheuristic Algorithms)
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