Fuzzy Optimization Research Articles

Aquila Optimized Fuzzy Deep Belief Network for Secure Data Transmission in WSN

A wireless sensor network (WSN) is made up of several independent sensor nodes that are able to interpret, analyze, and work with data. It is generally recognized that security and limited energy are the two challenging tasks with WSNs. To address these challenges, a novel Aquila-optimized fuzzy deep belief network (AO-FDBN) model has been proposed in this paper. The suggested AO-FDBN framework consists of three stages. Initially, the Aggregator has been selected by using the Aquila optimization algorithm. Secondly, the data from the aggregator are encrypted by using the blowfish algorithm. Finally, the optimal route has been selected by using the fuzzy-deep belief network (DBN). Packet delivery ratio (PDR), transport delay, energy usage, and network lifetime are evaluated between the suggested framework and current methods. Experimental results specify that the suggested AO-FDBN approach achieves higher performance of 22.617%, 14.22%, and 15.64% than TEEFCA, HESC, and SEPC methods. This system is more effective and secure for real-time applications.

Green production of airport terminals considering carbon footprint and indoor air quality: A multi-objective optimization perspective

The escalating carbon emissions from global civil aviation, exceeding 70 % of the International Civil Aviation Organization’s (ICAO) forecasted levels between 2013 and 2019, underscore an urgent need for sustainable practices in airport terminal production. To address this challenge, the present study has formulated a multi-objective optimization model that comprehensively incorporates two crucial aspects: carbon emissions from airport terminals and passenger comfort. Specifically, models for carbon emissions and indoor environmental air quality are developed, taking into account factors such as ambient temperature, light intensity, noise, and ventilation quality concerning both airport terminal emissions and passenger comfort. Building upon these models, a multi-objective programming framework is devised, considering practical operational constraints. To contend with uncertainties in model parameters, the model is evolved into a fuzzy optimization framework and thereafter solved by the fuzzy optimization theory. The model’s viability is tested through a case study of Mianyang Airport’s terminal in China’s southwestern region. In the context of concurrently considering carbon emission reduction and optimizing indoor environmental quality, winter carbon emissions increased by 212.09 % compared to summer, while indoor environmental quality declined by 5.68 %.

FSRW: fuzzy logic-based whale optimization algorithm for trust-aware routing in IoT-based healthcare

The Internet of Things (IoT) is an extensive system of interrelated devices equipped with sensors to monitor and track real world objects, spanning several verticals, covering many different industries. The IoT's promise is capturing interest as its value in healthcare continues to grow, as it can overlay on top of challenges dealing with the rising burden of chronic disease management and an aging population. To address difficulties associated with IoT-enabled healthcare, we propose a secure routing protocol that combines a fuzzy logic system and the Whale Optimization Algorithm (WOA) hierarchically. The suggested method consists of two primary approaches: the fuzzy trust strategy and the WOA-inspired clustering methodology. The first methodology plays a critical role in determining the trustworthiness of connected IoT equipment. Furthermore, a WOA-based clustering framework is implemented. A fitness function assesses the likelihood of IoT devices acting as cluster heads. This formula considers factors such as centrality, range of communication, hop count, remaining energy, and trustworthiness. Compared with other algorithms, the proposed method outperformed them in terms of network lifespan, energy usage, and packet delivery ratio by 47%, 58%, and 17.7%, respectively.

Intrusion detection system and fuzzy ant colony optimization based secured routing in wireless sensor networks

Intrusion detection system and fuzzy ant colony optimization based secured routing in wireless sensor networks

Optimizing environmental costs and economic benefits in new energy vehicle production: A case study of FAW Hongqi in China

This study focuses on the environmental cost accounting and economic benefit optimization of China’s FAW Hongqi New Energy Vehicle manufacturing enterprise under uncertain conditions, within the context of the emission permit system This study calculates the pollution situation throughout the manufacturing and production process of FAW Hongqi new energy vehicles, and constructs a multi-level environmental cost evaluation system for FAW Hongqi new energy vehicle manufacturing projects. Through the interval fuzzy model of FAW Hongqi new energy vehicle manufacturing projects, the maximum economic benefits of the enterprise are simulated. The research results indicate that the pollution emissions of enterprises are mainly concentrated in the three processes of welding, painting, and final assembly. Enterprises use their own exhaust gas and wastewater treatment devices to meet the standards for pollution emissions. At the same time, solid waste generated during the automobile manufacturing process is handed over to third-party companies for treatment. Secondly, based on the accounting results of enterprise pollution source intensity and a multi-layer environmental cost evaluation system, the environmental costs of enterprises are accounted for, and the environmental costs are represented in interval form to reduce uncertainty in the accounting process. According to the accounting results of enterprise environmental costs, the main environmental costs of enterprises are environmental remediation costs caused by normal pollution discharge and purchase costs of environmental protection facilities. Pollutant emission taxes and routine environmental monitoring costs are relatively low. Enterprises can adopt more scientific solutions from the aspects of environmental remediation and environmental protection facilities to reduce environmental costs. After optimization by the fuzzy interval uncertainty optimization model, the economic benefits of the FAW Hongqi new energy vehicle manufacturing project were [101,254.71, 6278.5413] million yuan. Compared with the interval uncertainty optimization model, the lower bound of economic benefits increased by 57.68%, and the upper bound decreased by 12.08%, shortening the results of the economic benefits interval. Clarify the current environmental pollution situation of FAW Hongqi’s new energy vehicle manufacturing enterprise, provide data support for sustainable development of the enterprise, and provide reasonable decision-making space for enterprise decision-makers.

Fuzzy random sensitivity analysis for the overall structure reliability of reinforced concrete freezing wellbores in deep alluvium based on hidden Markov model

To address the shortcomings of traditional reliability theory in characterizing the stability of deep underground structures, the advanced first order second moment of reliability was improved to obtain fuzzy random reliability, which is more consistent with the working conditions. The traditional sensitivity analysis model was optimized using fuzzy random optimization, and an analytical calculation model of the mean and standard deviation of the fuzzy random reliability sensitivity was established. A big data hidden Markov model and expectation-maximization algorithm were used to improve the digital characteristics of fuzzy random variables. The fuzzy random sensitivity optimization model was used to confirm the effect of concrete compressive strength, thick-diameter ratio, reinforcement ratio, uncertainty coefficient of calculation model, and soil depth on the overall structural reliability of a reinforced concrete double-layer wellbore in deep alluvial soil. Through numerical calculations, these characteristics were observed to be the main influencing factors. Furthermore, while the soil depth was negatively correlated, the other influencing factors were all positively correlated with the overall reliability. This study provides an effective reference for the safe construction of deep underground structures in the future.

Fuzzy logic in the problems of determining the economic parameters of project implementation

The goal of this work is to forecast project execution terms and its main economic parameters using graph theory and fuzzy logic methods. The subject of research in the article is the method of calculating the main properties or parameters of the project, forecasting the terms of its implementation and the possibility of taking into account force majeure situations. The article discusses the task of finding the optimal plan for the project of creating an e-commerce site and calculating its main economic parameters. The work uses methods of the theory of network planning and management. Fuzzy logic methods are used to solve the fuzzy optimization problem. Graph theory methods, namely the CPM critical path method, are used to forecast project deadlines. The following results were obtained: the main economic parameters were calculated for two versions of the project to create an e-commerce site – sequential, when the team does not have many developers to implement it (or enough resources), and parallel execution of work, which allows you to optimize the execution time by involving additional workers. The cost of both projects was also calculated, which in the future can help managers draw conclusions regarding the implementation of one or another planning option for this type of project. For the first project, the project work plan will be completed in a time not exceeding 230 days, with a reliability of 30%. Or does not exceed 295 days with a reliability of 80% percent. For the second project, the project work plan will be completed in a time not exceeding 230 days, with a reliability of 30%. Or does not exceed 278 days with a reliability of 70% percent. Conclusions: the paper proposes a fuzzy mathematical model for finding the optimal plan and calculating the main economic parameters of the project of creating an e-commerce site with a fuzzy set of plans and a vaguely defined goal. The work also presents a method for solving this problem. The obtained results of the research are of great value for planning the project, for solving the question of the feasibility of its initiation, for forecasting the resources that will be needed for its implementation. These indicators are essential for improving processes and correct allocation of work, which can help strengthen competitiveness and increase project profits.

A Recurrent Neural Network Approach for Constrained Distributed Fuzzy Convex Optimization.

This article investigates a class of constrained distributed fuzzy convex optimization problems, where the objective function is the sum of a set of local fuzzy convex objective functions, and the constraints include partial order relation and closed convex set constraints. In undirected connected node communication network, each node only knows its own objective function and constraints, and the local objective function and partial order relation functions may be nonsmooth. To solve this problem, a recurrent neural network approach based on differential inclusion framework is proposed. The network model is constructed with the help of the idea of penalty function, and the estimation of penalty parameters in advance is eliminated. Through theoretical analysis, it is proven that the state solution of the network enters the feasible region in finite time and does not escape again, and finally reaches consensus at an optimal solution of the distributed fuzzy optimization problem. Furthermore, the stability and global convergence of the network do not depend on the selection of the initial state. A numerical example and an intelligent ship output power optimization problem are given to illustrate the feasibility and effectiveness of the proposed approach.

Employing Novel Ranking Function for Solving Fully Fuzzy Fractional Linear Programming Problems

البرمجة الضبابية مفيدة بشكل خاص في الحالات التي تكون فيها المعاملات غامضة. بسبب هذه الميزة ، في السنوات الأخيرة ، ظهرت العديد من التقنيات لمعالجة عدم اليقين. يقترح هذا البحث تقنية دالة ترتيب جديدة مع متغيرات من نوع الأرقام الضبابية العشرية لحل مشاكل البرمجة الخطية الكسرية الضبابية التامة (FFFLP). تعتمد هذه التقنية على إدخال دالة رتبية جديدة لرقم ضبابي عشري ، واستخدام الطريقة المبسطة الضبابية التامة. بعد تحويل مشكلة FFFLP إلى مشكلة البرمجة الخطية الضبابية التامة (FFLP) باستخدام الطريقة التكميلية ، ثم يتم حلها باستخدام الجداول المبسطة الضبابية التامة ، والتي تكون فيها جميع القيم أرقامًا عشرية ضبابية. بمساعدة العمليات الحسابية للأرقام العشرية ، يتم الوصول إلى التكرار الجديد للجدول البسيط. يتم تكرار الخطوات لحين الوصول إلى الحل الضبابي الأمثل. ولبيان الطريقة المقترحة، يتم تقديم مثال عددي يوضح خطوات ايجاد الحل الضبابي الأمثل للمشكلة.

A hybrid approach to obesity level determination with decision tree and pelican optimization algorithm

Approximately 2 billion people in the world struggle with "obesity" and factors like eating lifestyle, habits, health conditions and mode of transport affect obesity. In this study, an artificial intelligence and machine learning-based model has been developed to predict obesity levels. It is proposed to create a hybrid model by combining the Decision Tree (DT) algorithm with the Pelican Optimization Algorithm (POA) on the obesity dataset of 2111 patients in SSggle. These models emphasize the critical role of parameters, aiming to achieve high performance. To solve the classification problem of multi-class obesity level determination, fuzzy logic-based parameter optimization is used to achieve high performance. While obesity rates are increasing worldwide, the study, which aims to globalize the parameters with the random discovery strategy of POA, is thought to be helpful for health professionals and decision-makers by successfully predicting obesity levels.

Oral Cancer Detection Using Deep Learning

Oral cancer is a very serious, complex, and common type of cancer. Oral cancer ranks eighth globally in terms of cancer incidence in India, with 130,000 deaths reported annually. The tumor affects the tonsils, salivary glands, neck, face, and mouth. Numerous methods, including screening procedures and biopsies—which entail taking a small sample of body tissue and analyzing it under a microscope—can be used to identify oral cancer. The disadvantage of cancer cells is that they are hard to identify and quantify. For this reason, digital processing technology will be employed in this study to identify and classify cancer cells that have affected the oral cavity. State-of-the-art technology and an in-depth learning algorithm can be employed for early detection and categorization. This work employs the Zernike Moment, wavelet features, and the bag histogram of directed gradients as three techniques for character extraction. After the characteristics are obtained, the best texture characteristic is chosen using the fuzzy particle swarm optimization technique (FPSO). In the end, these features were classified using the Faster Region-based Convolution Neural Network (faster RCNN) classifier. to evaluate the efficiency, error, recall rate, precision rate, and classification accuracy of the recommended approach.

A novel combined fuzzy VIKOR and mathematical optimization programming for online advertising effectiveness

A novel combined fuzzy VIKOR and mathematical optimization programming for online advertising effectiveness

Short-term load forecasting method based on fuzzy optimization combined model of load feature recognition

Short-term load forecasting method based on fuzzy optimization combined model of load feature recognition

Enhancing resource utilization and privacy in IoT data placement through fuzzy logic and PSO optimization

Enhancing resource utilization and privacy in IoT data placement through fuzzy logic and PSO optimization

Brain image segmentation with fuzzy entropy clustering and PSO-GWO optimization techniques

In the field of brain MRI analysis, image segmentation serves various purposes such as quantifying and visualizing anatomical structures, analyzing brain changes, delineating pathological regions, and aiding in surgical planning and image-guided interventions. Over the past few decades, diverse segmentation techniques with varying degrees of accuracy and complexity have been developed. Real-world brain MRI images often encounter intensity in homogeneity, posing a significant challenge in accurate segmentation. The prevailing image segmentation algorithms, predominantly region-based, typically rely on the homogeneity of image intensities in specific regions of interest. However, these methods often fall short of providing precise segmentation results due to intensity in homogeneity. To address these challenges and enhance segmentation performance, this paper introduce a novel objective function named Fuzzy Entropy Clustering with Local Spatial Information and Bias Correction (FECSB). Additionally, we propose a novel hybrid algorithm that combines Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) to maximize the effectiveness of the FECSB function in MRI brain image segmentation. The proposed algorithm undergoes rigorous evaluation using benchmark MRI brain images, including those from the McConnell Brain Imaging Center (BrainWeb). The experimental results unequivocally demonstrate the superiority of the PSO-GWO clustering method over the traditional Fuzzy C Means (FCM) method. Across various image slices, the PSO-GWO method consistently outperforms FCM in terms of accuracy, showing improvements ranging from 1.28% to 1.46%, approximately achieving 99.37% accuracy.

Advancing gasoline desulfurization: Multi-objective fuzzy optimization in systems technology

Ultrasonic-assisted oxidative desulfurization (UAOD) is utilized to lessen environmental problems due to sulfur emissions. The process uses immiscible polar solvents and ultrasonic waves to enhance desulfurization efficiency. Prior research focused on comparing the effectiveness of UAOD for gasoline using response surface methodology. This study evaluates the desulfurization efficiency and operating costs, including ultrasonic power, irradiation time, and oxidant amount to determine optimal conditions. The study used a multi-objective fuzzy optimization (MOFO) approach to evaluate the economic viability of UAOD for gasoline. It identified upper and lower boundaries and then optimized the desulfurization efficiency and operating costs while considering uncertainty errors. The fuzzy model employed max-min aggregation to optimize the degree of satisfaction on a scale from 0 (unsatisfied) to 1 (satisfied). Optimal conditions for gasoline UAOD were found at 445.43 W ultrasonic power, 4.74 min irradiation time, and 6.73 mL oxidant, resulting in a 66.79 % satisfaction level. This yielded a 78.64 % desulfurization efficiency (YA) at an operating cost of 13.49 USD/L. Compared to existing literature, gasoline desulfurization was less efficient and less costly. The solutions provided by MOFO demonstrate not only economic viability through decreased overall operating costs and simplified process conditions, but also offer valuable insights for optimizing prospective future industrial-scale UAOD processes.

DMET-Based Fuzzy Optimized Consensus Control for Nonlinear MASs With Quantized Reference

DMET-Based Fuzzy Optimized Consensus Control for Nonlinear MASs With Quantized Reference

Multi-objective fuzzy green scheduling optimization method of special vehicle body-in-white prototype shop considering equipment preventive maintenance

Based on the advantages of non-contact, flexibility, and small heat-affected zone, laser processing technology is widely used in material removal, addition, modification and other manufacturing fields. However, the emission of large amounts of smoke during laser processing of metal materials will result in environmental pollution in the job shop. Additionally, the processing time of laser equipment becomes uncertain as the power decays with prolonged use. Therefore, this paper is mainly focus on a multi-objective flexible job shop fuzzy green scheduling problem to minimize the fuzzy makespan, fuzzy total energy consumption and fuzzy total smoke emission. To solve it, an improved multi-objective particle swarm optimization algorithm has been designed. Firstly, a preventive maintenance strategy is proposed to reduce the makespan and equipment failure frequency by considering periodic power attenuation of laser equipment. Next, different particle updating strategies are employed to enhance particles’ exploration capabilities. Subsequently, five neighborhood structures are introduced to improve the performance of local search. Finally, comparison experiments are conducted with an expanded common benchmark, and a production case study in a special vehicle body-in-white prototype job shop. The results effectively demonstrate the feasibility and effectiveness of the developed model and improved multi-objective particle swarm optimization algorithm.

Optimal Fuzzy Precompensated TID controller for Nonlinear Dynamical Systems

Optimal Fuzzy Precompensated TID controller for Nonlinear Dynamical Systems

A Probability-based Fuzzy Multi-objective Optimization for Material Selection

Abstract: In the present paper, a rational fuzzy multi-objective optimization for material selection is developed in respect of probabilistic method for multi-objective optimization, which agrees with the viewpoint of system theory for the whole optimization of a system and is the novelty of this work. The basic ideas and algorithms of fuzzy theory together with probability theory are taken as the cornerstone to perform the formulation. In the treatment, the intersection of the membership function of fuzzy numbers of alternative material performance and the membership function of fuzzy numbers of desired material performance is used as the utility of the material performance index. Thereafter, the utility of each material performance index is further used to conduct the assessment of its partial preferable probability and formulate the multi-objective optimization by means of probability theory. Moreover, a typical example is presented to provide the rational process of the probabilistic fuzzy multi-objective optimization for material selection.