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Mathematics
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Fuzzy Optimization and Decision Making
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Fuzzy Optimization and Decision Making

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Fuzzy Sets, Systems and Decision Making".

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 13479

Special Issue Editors

Dr. Kanak Kalita

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi 600062, India
Interests: fuzzy optimization; finite element; optimization; MCDM; composite structures; machine learning
Prof. Dr. Robert Čep

E-Mail Website1 Website2
Guest Editor
VSB-TU Ostrava, Faculty of Mechanical Engineering, 17. listopadu 2172/15, 708 00 Ostrava, Czech Republic
Interests: machining; surface integrity; cutting tools; engineering metrology; fuzzy optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Special Issue entitled "Fuzzy Optimization and Decision Making" aims to present the latest research and developments in the field of fuzzy optimization and decision-making. Fuzzy optimization and decision-making techniques provide a flexible and robust approach to solving complex real-world problems that involve uncertainty, imprecision and incomplete information. These techniques have found widespread application in a variety of fields, including engineering, management, economics, finance and environmental science.

We invite contributions from researchers and practitioners working on fuzzy optimization and decision-making, as well as related topics, such as fuzzy control, fuzzy scheduling, fuzzy risk analysis and management and fuzzy data-driven optimization. We welcome both theoretical and practical contributions that advance the state of the art in fuzzy optimization and decision-making and that have the potential to impact real-world problems.

We hope that this Special Issue will serve as a forum for exchanging ideas and knowledge among researchers and practitioners in the field and that it will inspire new research directions and applications of fuzzy optimization and decision-making. We welcome submissions from researchers and practitioners around the world and we look forward to receiving high-quality papers that contribute to the advancement of this exciting and important field.

Dr. Kanak Kalita 
Prof. Dr. Robert Čep
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. Mathematics 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 2600 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

  • mathematical modeling
  • decision-making
  • optimization algorithms
  • heuristics and metaheuristics
  • fuzzy optimization
  • combinatorial optimization
  • robust optimization
  • mathematical optimization
  • optimization applications
  • fuzzy sets
  • fuzzy multi-criteria method
  • soft computing methods
  • hybrid fuzzy optimization
  • fuzzy ranking

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Published Papers (7 papers)

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Research

23 pages, 1038 KiB  
Article
Selection of Optimal Approach for Cardiovascular Disease Diagnosis under Complex Intuitionistic Fuzzy Dynamic Environment
by Dilshad Alghazzawi, Maryam Liaqat, Abdul Razaq, Hanan Alolaiyan, Umer Shuaib and Jia-Bao Liu
Mathematics 2023, 11(22), 4616; https://doi.org/10.3390/math11224616 - 10 Nov 2023
Cited by 7 | Viewed by 1442
Abstract
Cardiovascular disease (CVD) is a leading global health concern. There is a critical need for accurate and reliable decision-making tools to select the optimal approach for diagnosing cardiovascular disease (CVD). In this study, we have addressed this pressing issue. Complex intuitionistic fuzzy set [...] Read more.
Cardiovascular disease (CVD) is a leading global health concern. There is a critical need for accurate and reliable decision-making tools to select the optimal approach for diagnosing cardiovascular disease (CVD). In this study, we have addressed this pressing issue. Complex intuitionistic fuzzy set (CIFS) theory is adept at encapsulating vagueness due to its capability to encompass comprehensive problem specifications characterized by both intuitionistic uncertainty and periodicity. Within the scope of this article, we present two novel aggregation operators: the complex intuitionistic fuzzy dynamic weighted averaging (CIFDWA) operator and the complex intuitionistic fuzzy dynamic weighted geometric (CIFDWG) operator. Some intriguing characteristics of these operators are elucidated, and important special cases are also defined in detail. We devise an enhanced score function to rectify the deficiencies observed in the existing score function under complex intuitionistic fuzzy knowledge. Furthermore, these operators are employed in the development of a systematic approach for the handling of multiple attribute decision-making (MADM) scenarios involving complex intuitionistic fuzzy data. Moreover, we undertake the resolution of an MADM problem, wherein we ascertain the optimal approach for diagnosing cardiovascular disease (CVD) through the utilization of the proposed operators, thereby substantiating their utility in decision-making processes. Finally, we conduct a comprehensive comparative analysis, pitting the presented operators against an array of existing counterparts, in order to demonstrate the reliability and stability inherent in the derived methodologies. Full article
(This article belongs to the Special Issue Fuzzy Optimization and Decision Making)
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18 pages, 2216 KiB  
Article
A Novel Interval-Valued Decision Theoretic Rough Set Model with Intuitionistic Fuzzy Numbers Based on Power Aggregation Operators and Their Application in Medical Diagnosis
by Wajid Ali, Tanzeela Shaheen, Iftikhar Ul Haq, Hamza Ghazanfar Toor, Tmader Alballa and Hamiden Abd El-Wahed Khalifa
Mathematics 2023, 11(19), 4153; https://doi.org/10.3390/math11194153 - 3 Oct 2023
Cited by 5 | Viewed by 1225
Abstract
Intuitionistic fuzzy information is a potent tool for medical diagnosis applications as it can represent imprecise and uncertain data. However, making decisions based on this information can be challenging due to its inherent ambiguity. To overcome this, power aggregation operators can effectively combine [...] Read more.
Intuitionistic fuzzy information is a potent tool for medical diagnosis applications as it can represent imprecise and uncertain data. However, making decisions based on this information can be challenging due to its inherent ambiguity. To overcome this, power aggregation operators can effectively combine various sources of information, including expert opinions and patient data, to arrive at a more accurate diagnosis. The timely and accurate diagnosis of medical conditions is crucial for determining the appropriate treatment plans and improving patient outcomes. In this paper, we developed a novel approach for the three-way decision model by utilizing decision-theoretic rough sets and power aggregation operators. The decision-theoretic rough set approach is essential in medical diagnosis as it can manage vague and uncertain data. The redesign of the model using interval-valued classes for intuitionistic fuzzy information further improved the accuracy of the diagnoses. The intuitionistic fuzzy power weighted average (IFPWA) and intuitionistic fuzzy power weighted geometric (IFPWG) aggregation operators are used to aggregate the attribute values of the information system. The established operators are used to combine information within the intuitionistic fuzzy information system. The outcomes of various alternatives are then transformed into interval-valued classes through discretization. Bayesian decision rules, incorporating expected loss factors, are subsequently generated based on this foundation. This approach helps in effectively combining various sources of information to arrive at more accurate diagnoses. The proposed approach is validated through a medical case study where the participants are classified into three different regions based on their symptoms. In conclusion, the decision-theoretic rough set approach, along with power aggregation operators, can effectively manage vague and uncertain information in medical diagnosis applications. The proposed approach can lead to timely and accurate diagnoses, thereby improving patient outcomes. Full article
(This article belongs to the Special Issue Fuzzy Optimization and Decision Making)
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16 pages, 2431 KiB  
Article
A Comparative Study of Fuzzy Domination and Fuzzy Coloring in an Optimal Approach
by Annamalai Meenakshi, Adhimoolam Kannan, Miroslav Mahdal, Krishnasamy Karthik and Radek Guras
Mathematics 2023, 11(18), 4019; https://doi.org/10.3390/math11184019 - 21 Sep 2023
Cited by 1 | Viewed by 1635
Abstract
An optimal network refers to a computer or communication network designed, configured, and managed to maximize efficiency, performance, and effectiveness while minimizing cost and resource utilization. In a network design and management context, optimal typically implies achieving the best possible outcomes between various [...] Read more.
An optimal network refers to a computer or communication network designed, configured, and managed to maximize efficiency, performance, and effectiveness while minimizing cost and resource utilization. In a network design and management context, optimal typically implies achieving the best possible outcomes between various factors. This research investigated the use of fuzzy graph edge coloring for various fuzzy graph operations, and it focused on the efficacy and efficiency of the fuzzy network product using the minimal spanning tree and the chromatic index of the fuzzy network product. As a network made of nodes and vertices, measurement with vertices is a parameter for domination, and edge measurement is a parameter for edge coloring, so we used these two parameters in the algorithm. This paper aims to identify an optimal network that can be established using product outcomes. This study shows a way to find an optimal fuzzy network based on comparative optimal parameter domination and edge coloring, which can be elaborated with applications. An algorithm was generated using an optimal approach, which was subsequently implemented in the form of applications. Full article
(This article belongs to the Special Issue Fuzzy Optimization and Decision Making)
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21 pages, 9916 KiB  
Article
A Meta-Classification Model for Optimized ZBot Malware Prediction Using Learning Algorithms
by Shanmugam Jagan, Ashish Ashish, Miroslav Mahdal, Kenneth Ruth Isabels, Jyoti Dhanke, Parita Jain and Muniyandy Elangovan
Mathematics 2023, 11(13), 2840; https://doi.org/10.3390/math11132840 - 24 Jun 2023
Cited by 5 | Viewed by 1898
Abstract
Botnets pose a real threat to cybersecurity by facilitating criminal activities like malware distribution, attacks involving distributed denial of service, fraud, click fraud, phishing, and theft identification. The methods currently used for botnet detection are only appropriate for specific botnet commands and control [...] Read more.
Botnets pose a real threat to cybersecurity by facilitating criminal activities like malware distribution, attacks involving distributed denial of service, fraud, click fraud, phishing, and theft identification. The methods currently used for botnet detection are only appropriate for specific botnet commands and control protocols; they do not endorse botnet identification in early phases. Security guards have used honeypots successfully in several computer security defence systems. Honeypots are frequently utilised in botnet defence because they can draw botnet compromises, reveal spies in botnet membership, and deter attacker behaviour. Attackers who build and maintain botnets must devise ways to avoid honeypot traps. Machine learning methods support identification and inhibit bot threats to address the problems associated with botnet attacks. To choose the best features to feed as input to the machine learning classifiers to estimate the performance of botnet detection, a Kernel-based Ensemble Meta Classifier (KEMC) Strategy is suggested in this work. And particle swarm optimization (PSO) and genetic algorithm (GA) intelligent optimization algorithms are used to establish the ideal order. The model covered in this paper is employed to forecast Internet cyber security circumstances. The Binary Cross-Entropy (loss), the GA-PSO optimizer, the Softsign activation functions and ensembles were used in the experiment to produce the best results. The model succeeded because Forfileless malware, gathered from well-known datasets, achieved a total accuracy of 93.3% with a True Positive (TP) Range of 87.45% at zero False Positive (FP). Full article
(This article belongs to the Special Issue Fuzzy Optimization and Decision Making)
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14 pages, 2013 KiB  
Article
Cloud Services User’s Recommendation System Using Random Iterative Fuzzy-Based Trust Computation and Support Vector Regression
by Janjhyam Venkata Naga Ramesh, Syed Khasim, Mohamed Abbas, Kareemulla Shaik, Mohammad Zia Ur Rahman and Muniyandy Elangovan
Mathematics 2023, 11(10), 2332; https://doi.org/10.3390/math11102332 - 17 May 2023
Cited by 3 | Viewed by 1551
Abstract
Cloud computing is now a fundamental type of computing due to technological innovation and it is believed to be a benefit for mid-scale enterprises. The use of cloud computing is increasing daily, which improves service quality but also gives rise to security concerns. [...] Read more.
Cloud computing is now a fundamental type of computing due to technological innovation and it is believed to be a benefit for mid-scale enterprises. The use of cloud computing is increasing daily, which improves service quality but also gives rise to security concerns. Finding trustworthy service can be very challenging, take a great deal of time, or produce subpar services. Due to these difficulties, the client needs a service that is dependable, suitable, time-saving, and trustworthy. As a result, from the end user’s perspective, adopting a cloud service’s trustworthiness becomes crucial. Trust is a measure of how well users’ expectations about a service’s capabilities are realized. In this research, a recommendation system for cloud service customers based on random iterative fuzzy computation (RIFTC) is proposed. RIFTC focuses on the assessment of trust using Quality of Service (QoS) characteristics. RIFTC calculates trust using the machine learning approach Support Vector Regression (SVR). RIFTC can helpfully recommend a cloud service to the end user and anticipate the trust values of cloud services.. Precision (97%), latency (51%), throughput (25.99 mbps), mean absolute error (54%), and re-call (97%) rates are used to assess how well this recommendation system performs. RIFTC’s average F-measure rate is calculated by adjusting the number of users from 200 to 300, and it is 93.46% more accurate on average with less time spent than the current methodologies. Full article
(This article belongs to the Special Issue Fuzzy Optimization and Decision Making)
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25 pages, 7204 KiB  
Article
A Novel Decomposition-Based Multi-Objective Symbiotic Organism Search Optimization Algorithm
by Narayanan Ganesh, Rajendran Shankar, Kanak Kalita, Pradeep Jangir, Diego Oliva and Marco Pérez-Cisneros
Mathematics 2023, 11(8), 1898; https://doi.org/10.3390/math11081898 - 17 Apr 2023
Cited by 39 | Viewed by 2311
Abstract
In this research, the effectiveness of a novel optimizer dubbed as decomposition-based multi-objective symbiotic organism search (MOSOS/D) for multi-objective problems was explored. The proposed optimizer was based on the symbiotic organisms’ search (SOS), which is a star-rising metaheuristic inspired by the natural phenomenon [...] Read more.
In this research, the effectiveness of a novel optimizer dubbed as decomposition-based multi-objective symbiotic organism search (MOSOS/D) for multi-objective problems was explored. The proposed optimizer was based on the symbiotic organisms’ search (SOS), which is a star-rising metaheuristic inspired by the natural phenomenon of symbioses among living organisms. A decomposition framework was incorporated in SOS for stagnation prevention and its deep performance analysis in real-world applications. The investigation included both qualitative and quantitative analyses of the MOSOS/D metaheuristic. For quantitative analysis, the MOSOS/D was statistically examined by using it to solve the unconstrained DTLZ test suite for real-parameter continuous optimizations. Next, two constrained structural benchmarks for real-world optimization scenario were also tackled. The qualitative analysis was performed based on the characteristics of the Pareto fronts, boxplots, and dimension curves. To check the robustness of the proposed optimizer, comparative analysis was carried out with four state-of-the-art optimizers, viz., MOEA/D, NSGA-II, MOMPA and MOEO, grounded on six widely accepted performance measures. The feasibility test and Friedman’s rank test demonstrates the dominance of MOSOS/D over other compared techniques and exhibited its effectiveness in solving large complex multi-objective problems. Full article
(This article belongs to the Special Issue Fuzzy Optimization and Decision Making)
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17 pages, 1926 KiB  
Article
A Study of a Two Storage Single Product Inventory System with Ramp Type Demand, N-Phase Prepayment and Purchase for Exigency
by Jagadeesan Viswanath, Rajamanickam Thilagavathi, Krishnasamy Karthik and Miroslav Mahdal
Mathematics 2023, 11(7), 1728; https://doi.org/10.3390/math11071728 - 4 Apr 2023
Cited by 5 | Viewed by 1692
Abstract
This model considers a two-warehouse inventory system of deteriorated items with ramp-type demand and a constant rate of deterioration. It is maintained a rental warehouse (RW) of infinite capacity to load the excess items of replenished goods after filling the [...] Read more.
This model considers a two-warehouse inventory system of deteriorated items with ramp-type demand and a constant rate of deterioration. It is maintained a rental warehouse (RW) of infinite capacity to load the excess items of replenished goods after filling the items of finite capacity in the own warehouse (OW). Retailers are encouraged to opt for the prepayment option of paying their purchase cost in equal installments prior to the delivery of the ordered items with a considerable discount, which will ensure the purchase guarantee of their orders. The slotted backlog interval of the stock out period is handled in two different ways to retain the customers and ease their impatience. Customers in the first slot of the stock out period are satisfied by the emergency purchases from local suppliers with high purchasing costs to avoid losing customers. Customers in the next slot are satisfied immediately after the next replenishment point. Essential measures of the system are derived: optimal ordering quantities from both regular and local suppliers; replenishment cycle length; and a partitioned backlog interval. A numerical example is given along with the optimal solutions for a particular environment with sensitive analysis in order to validate the model’s efficacy. Full article
(This article belongs to the Special Issue Fuzzy Optimization and Decision Making)
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