Fuzzy Particle Swarm Research Articles

Hyperparameter Control Using Fuzzy Logic: Evolving Policies for Adaptive Fuzzy Particle Swarm Optimization Algorithm.

Heuristic optimization methods such as Particle Swarm Optimization depend on their parameters to achieve optimal performance on a given class of problems. Some modifications of heuristic algorithms aim at adapting those parameters during the optimization process. We present a novel approach to design such adaptation strategies using continuous fuzzy feedback control. Fuzzy feedback provides a simple interface where probes are sampled in the optimization process and parameters are fed back to the optimizer. The probes are turned into parameters by a fuzzy process optimized beforehand to maximize performance on a training benchmark. Utilizing this framework, we systematically established 127 different Fuzzy Particle Swarm Optimization algorithms featuring a maximum of 7 parameters under fuzzy control. These newly devised algorithms exhibit superior performance compared to both traditional PSO and some of its best parameter control variants. The performance is reported in the single-objective bound-constrained numerical optimization competition of CEC 2020. Additionally, two specific controls, highlighted for their efficacy and dependability, demonstrated commendable performance in real-world scenarios from CEC 2011.

Solar PV system with maximum power tracking

The paper investigates the state-of-the-art architecture of photovoltaic systems (PVS) by evaluating the performance of the developed maximum power point tracking (MPPT) algorithm of fuzzy particle swarm optimization (FPSO) for temperate continental latitudes. The material of the paper gives an evaluation of traditional MPPT algorithms in relation to the state-of-the-art FPSO algorithm. The study summarizes the problems of On-Grid PV systems and analyzes methods of solving them by combining them with hydrogen technologies. The paper summaries the experience of observations of climatic factors and solar resources on the example of the Russian Federation. Thus, in 2023, the average winter temperature exceeded the norm by 4.7 °C, and the average summer temperature exceeded the norm by 4.9 °C compared to 2022. The paper analyses the level of insolation, which increased by 0.03 kWh/m2 by regions of Russia for the period 2022–2023. The experimental part of the work was carried out at the solar power plant (SPP) «Kalmykskaya» with coordinates 53.422832 north latitude and 55.266895 east longitude. The watt-volt field characteristic of photovoltaic modules (PVM) connected to the inverter was obtained experimentally. When compared, the correlation coefficient between the experimental power (P) and voltage (U) of the panels was found to be higher than that of the ideal panels, 0.933 versus 0.914. The correlation coefficient between the ideal P(U) function and the experimental one is (−0.475). The data for calculating the coefficient of performance (COP) of the implemented MPPT algorithm was also obtained experimentally, which was about 98.7%. The reliability of the data used in the calculations was confirmed by two independent means of measurement, the difference of the obtained results was less than 1%. In the last part of the experiment of this study, the dependence of insolation in a given geographical point on the generated PV field power was evaluated. The correlation coefficient was 0.47, while the inverter output voltage was maintained in the nominal range of (600 ± 20%)V. Thus, the authors of the study experimentally proved the efficiency of using MPPT on FPSO in temperate continental climate with duration of sunshine T = 1850 h per year. The effectiveness of the FPSO algorithm under the conditions of inverter distance from the common point of the DC switching cabinet (DСCC) has been confirmed. The effectiveness of the MPPT algorithm based on FPSO under conditions of partial shading, increased cloud cover and increased air temperature is concluded. Using the description of the current architecture of On-Grid SPP, the authors draw attention to the impossibility of operation of such systems without the presence of voltage in the reference network. In addition, the impossibility of the system operation at the value of PVM power over 1500 kW and at the voltage of panels less than 900 V is noted. To modernize the existing PVS architecture, for the first time, the use of a DCCC and an inverter with implemented MPPT on the FPSO in combination with a hydrogen (H2) production unit and nickel-hydrogen (Ni–H2) batteries is proposed. The researchers propose that when the PVM field voltage is below 900 V and when the PVM field power exceeds 1500 kW, energy can be diverted to H2 generation or to charge Ni–H2 batteries via a DCCC controller. Such an architecture will improve the continuity and efficiency of the PVS, reduce the carbon footprint, and allow the PVS to be used as an industrial uninterruptible power supply (UPS). The authors see the lack of standardization of implemented projects based on the ESG principle as the main problem of alternative energy development.

Safeguarding Healthcare Data Privacy in the Cloud using Modified Fuzzy Particle Swarm Optimization and Two Fish Encryption Algorithm

Safeguarding Healthcare Data Privacy in the Cloud using Modified Fuzzy Particle Swarm Optimization and Two Fish Encryption Algorithm

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.

Empowering Light Fidelity-Enabled Internet of Things with Hybrid Fuzzy-Particle Swarm Optimization Power Allocation for Enhanced Energy Efficiency and Quality of Service

This paper proposes a hybrid fuzzy-Particle Swarm Optimization (PSO) approach for power allocation in bidirectional Light Fidelity (LiFi)-enabled Internet of Things (IoT) communication systems. The approach integrates fuzzy logic and PSO to achieve efficient and robust power allocation, considering energy efficiency and Quality of Service (QoS) requirements. The hybrid approach leverages flexibility of fuzzy logic to handle uncertainties and variations in system parameters, such as channel conditions and QoS requirements. Fuzzy sets and membership functions are defined to represent linguistic variables, and fuzzy rules are formulated based on expert knowledge and system-specific considerations. This allows the approach to adaptively adjust power allocation based on varying channel conditions and QoS requirements, enhancing the robustness of the power allocation strategy. The PSO algorithm is employed for iterative optimization to explore for ideal power allocation solution. The particles in swarm signify possible power allocation configuration, and its position in the search space corresponds to a specific power allocation. The particles update their positions based on local best-known positions and globally best-known positions, allowing the swarm to converge toward the optimal solution. Through extensive simulations and analysis, the proposed hybrid fuzzy-PSO approach is evaluated with regard to energy efficiency and QoS satisfaction. The results demonstrate its effectiveness in achieving energy-efficient power allocation while meeting diverse QoS requirements. The proposed approach outperforms existing methods such as channel- and Orthogonal Multiple Access (OMA)-based power allocation schemes.

Efficiency improvement in energy consumption: A novel deep learning based model for leading a greener Economic recovery

This research focuses on elevating energy consumption efficiency through an innovative predictive model designed for urban industrial and residential sectors. Leveraging the Deep Q Network (DQN) framework, the study introduces the Dueling DQN architecture, a breakthrough approach that separates state value and advantage function estimations. This segregation significantly enhances learning efficiency, providing a more precise representation of the state's value and, consequently, optimizing energy consumption predictions. In tandem with architectural advancements, the research proposes a unique fuzzy Particle Swarm Optimization (PSO) approach to fine-tune DQN setting parameters during training. This integration of fuzzy logic into PSO ensures adaptability and robustness, addressing challenges associated with diverse environmental and operational conditions. For the goal of efficiency improvement in energy consumption, this study not only introduces a state-of-the-art solution for energy consumption prediction and optimization but also aligns with the broader goal of fostering a greener economic recovery. The model's promising results signify a significant step toward more sustainable and resource-efficient urban development, contributing to a resilient and environmentally conscious economic future. This research lays the foundation for a transformative approach to energy consumption, vital for steering economic recovery toward a greener and more sustainable trajectory. The simulation results advocate the high performance of the proposed deep learning prediction model.

Comparison of a Novel AFPSO-SFS & PSO-SFS Approaches for Islanding Protection of Inverter-Based DGs

The most significant of the active islanding detection techniques is “Sandia Frequency Shift” (SFS), and this employs frequency drifting to identify extra Nondetection Zone. Inappropriate tuning parameters, such as the acceleration factor and chopping factor, result in frequency drifting, which degrades the quality of the power and causes the continuous power mode to fail. The failure can be overcome by considering optimized parameters to identify the nondetection zone (NDZ) in small DG system (120 kVA) at constant power/current mode. This article discusses, a new Adaptive Fuzzy Particle Swarm Optimization-SFS (AFPSO-SFS) technique is recommended to attain optimal parameters of SFS anti-islanding detection technique for validation of the proposed algorithm Particle Swarm Optimization–SFS (PSO-SFS) is also implemented. Furthermore, the ability of this method is to identify an islanding situation which deteriorates the major scientific issues for small DG system. Thus, the optimized parameter helps to attain an anti-islanding as well as less impact on power quality. Moreover, this article concerns serious issue of the grid interconnected DG model in an islanding condition, when the utility line would be unintentionally isolated. The grid detachment is generally considered as a fault. When an islanding occurs, the DG protection system must be inactive to detect the fault. For the investigation of power mismatches, the output of considered system using the proposed methods is contrast with results obtained from PSO-SFS and conventional SFS. The recommended strategy analyzes islands more effectively and diagnoses nondetection zones more accurately. The recommended method’s reliability is calculated quantitatively and emulated with MATLAB/Simulink.

Gesture Recognition Technology of VR Piano Playing Teaching Game based on Hidden Markov Model

Virtual Reality (VR) is a kind of simulation environment generated by computer simulation. It is a kind of isolation of users from the physical environment, and users are fully engaged in the simulation environment. In music teaching, especially in the learning of piano performance, VR technology can simulate the real playing experience without physical piano, so that learners can learn and practice piano anywhere. At present, the traditional way of learning piano often needs the guidance of professional piano teachers, and learners must study and practice beside the piano, which undoubtedly brings limitations to the time and space of learners. VR technology provides a more convenient way for learners to learn and practice without a physical piano, making learning more flexible and free. Firstly, a piano playing teaching game platform based on VR technology is constructed to provide a virtual piano playing scene. Then, by collecting a large number of piano players' gesture data, they are accurately marked. Finally, the Hidden Markov Model (HMM) is used to train and model the gesture data, and the key features of the gesture sequence are extracted to realize the recognition and real-time feedback of the piano player's gesture. According to the findings, the recognition accuracy of the Principal Component Analysis-HMM (PCA-HMM) without playing correction is very low after playing correction. Compared to the logarithmic likelihood ratio test, the recognition rate of the quadratic statistical model is only 15%. The results show that this method has good iterative convergence and convergence performance, which can effectively avoid falling into local optima. Compared with the traditional hidden Markov algorithm, it reduces 96.05% and 89.48% respectively. Compared with the fuzzy hidden Markov algorithm, it reduces 90.20% and 73.35% respectively. The average time and standard deviation for real-time positioning and map construction are 0.251s and 0.152s, respectively. The fitness of HMM based on fuzzy Particle Swarm Optimization (PSO) is 6.8% and 2.6% higher than that of the hidden Markov algorithm and the fuzzy hidden Markov algorithm, respectively. At the same time, in the comparison between the research model and other algorithm models, the accuracy and other parameter performance of the research model are better, with the highest error value of only 3.5, which is 5.9 lower than the lowest Convolutional Neural Network (CNN) algorithm model. It can be seen that the error value of the model used in the study is lower and the performance is better. Learners can receive real-time guidance and a more immersive playing experience through this technology. This provides useful reference for further exploring the combination of music teaching and VR technology

Application of Intelligent Algorithms for Optimal Allocation of Accounting Education Resources in Higher Education Environments

Abstract Inadequate allocation of accounting education resources and uneven spatial distribution are the key issues affecting the high-quality development of accounting education, and the arrival of the era of intelligent technology provides ideas for the rational allocation of higher education resources and transformation and upgrading. This paper constructs a multi-objective planning model for accounting education resource allocation and explores the solution method based on the fuzzy particle swarm algorithm. Population initialization is achieved by combining input-output fuzzification processing and particle swarm optimization search in fuzzy control. Subsequently, the fitness of every particle is assessed. Next, according to the formula of PSO algorithm update iteration, the position and speed are updated, the optimal solution of itself and the optimal solution of the population are updated, and the optimization search is continuously iterated. After the model is put into use the institutions in the western region with low and lower levels of efficiency in the allocation of accounting education resources decreased by 7%, and all the regions with low levels of efficiency have decreased. The number of configuration rounds of this paper’s method is smaller than the number of rounds of configuration conflicts of the PCA method and SVM method, which indicates that this paper’s method has good performance and optimizes the allocation of accounting education resources, making accounting education develop in a balanced way.

Improved Particle Swarm Fuzzy PID Temperature Control for the Pellet Grills

Improved Particle Swarm Fuzzy PID Temperature Control for the Pellet Grills

Research and Application of Improved Particle Swarm Fuzzy PID Algorithm Based on Self- Disturbance Rejection in Temperature Control System of Plastic Extruder

Research and Application of Improved Particle Swarm Fuzzy PID Algorithm Based on Self- Disturbance Rejection in Temperature Control System of Plastic Extruder

Fuzzy particle swarm for the right-first-time of fused deposition

Right-first-time production enables manufacturing companies to be profitable as well as competitive. Ascertaining such a concept is not as straightforward as it may seem in many industries, including 3D printing. Therefore, in this research paper, a right-first-time framework based on the integration of fuzzy logic and multi-objective swarm optimization is proposed to reverse-engineer the radial based integrated network. Such a framework was elicited to represent the fused deposition modelling (FDM) process. Such a framework aims to identify the optimal FDM parameters that should be used to produce a 3D printed specimen with the desired mechanical characteristics right from the first time. The proposed right-first-time framework can determine the optimal set of the FDM parameters that should be used to 3D print parts with the required characteristics. It has been proven that the right-first-time model developed in this paper has the ability to identify the optimal set of parameters successfully with an average error percentage of 4.7%. Such a framework is validated in a real medical case by producing three different medical implants with the desired mechanical characteristics for a 21-year-old patient.

A fuzzy particle swarm optimization method with application to shape design problem

In this study, we focus on a specific class of bilateral free boundaries problems. We approach this problem by formulating it as a shape optimization problem using a defined cost functional. The existence of an optimal solution for the optimization problem is proved. To tackle this problem, we propose an iterative approach that combines the particle swarm optimization and fuzzy logic methods. Additionally, we employ the finite element method as a discretization technique for the state equation. To validate our approaches, we investigate various types of domains. Furthermore, we compare the performance of these approaches in two scenarios: one with exact measurements used for identification and another with noisy measurements.

Analysis of microgrid configuration with optimal power injection from grid using point estimate method embedded fuzzy-particle swarm optimization

This paper aims to analyze the effect of integration of combined heat and power (CHP) units, renewables, shunt capacitors, and a heat boiler in a grid connected microgrid (GMG) system. In addition to that, a comparative study is conducted between the GMG and zero bus concept based microgrid (ZBMG) systems. In ZBMG, a predefined amount of power is injected from the grid. Therefore, one of the dispatchable distributed energy resources (DERs) in the ZBMG maintains the power balance and the corresponding DER unit is entitled as zero bus DER. In this paper, eight different microgrid configurations are formed by several combinations of renewable and non renewable sources, which are categorized under GMG and ZBMG systems. The multiple objectives such as total cost, emission, and energy loss are combined and solved using fuzzy technique and particle swarm optimization (PSO) algorithm. Also, point estimate method (PEM) is implemented to handle the uncertainty in renewables. The effect of grid, dispatchable units, renewables, and heat boiler on the objectives in each microgrid configuration is inspected. The results show that the microgrid configuration with non renewables is attained minimum cost and has a minimum emission with renewables. The integration of all types of sources in a microgrid configuration leads to a decrement in all the objectives. The proposed method is tested on a 33-bus grid connected microgrid system considering electrical and heat loads.

Performance evaluation of fuzzy genetic, fuzzy particle swarm and similar insects’ optimization algorithms on denoising problem based on novel combined filter for digital X-ray and CT images in Pelvic Region

Performance evaluation of fuzzy genetic, fuzzy particle swarm and similar insects’ optimization algorithms on denoising problem based on novel combined filter for digital X-ray and CT images in Pelvic Region

Hierarchical optimal allocation of BESS using APT-FPSO based on stochastic programming model considering voltage sensitivity and eigenvalues analyses

Integration of energy storage systems (ESSs) to microgrids (MGs) due to the growing penetration of intermittent energy resources is a great motivation to develop studies in the context of optimal sizing and placement of these systems. In this paper, a multi-objective bi-level optimization problem is addressed based on the stochastic nature and dynamic model of power system elements.As a preliminary step, the optimal size and capacity of the battery energy storage system (BESS) are determined to minimize frequency fluctuations. This level has been performed based on three approaches considering the daily intra-hour stochastic data. In the secondary level, the dynamic behaviour of all MG elements has been considered and correspondingly a dynamic objective function based on system eigenvalues and voltage sensitivity index (VSI) has been developed. According to the proposed objective function, the secondary step for sizing and placement of the battery units has been performed by adaptive particularly tuneable fuzzy particle swarm optimization (APT-FPSO) algorithm to reduce power losses, improve the voltage profile/ stability, and mitigate low-frequency fluctuations. Finally, the effectiveness of the proposed approach is evaluated using time-domain simulations, voltage sensitivity studies, and eigenvalue analysis.

An intelligent fuzzy-particle swarm optimization supervisory-based control of robot manipulator for industrial welding applications

The propensity of manufacturers to produce goods at affordable cost, with more accuracy, and at a faster rate force them to search for novel solutions, such as deploying robots in place of people in a sector that can accommodate their needs. Welding is one of the most crucial processes in the automotive industry. This process is time-consuming, subject to error, and demands skilled professionals. The robotic application can improve this area of production and quality. Other industries, such as painting and material handling, can also profit from the use of robots. This work describes the fuzzy DC linear servo controller, which functions as a robotic arm actuator. Robots have been widely employed in most productive sectors in recent years, including assembly plates, welding, tasks at higher temperatures, etc. Controlling a robot accurately is a difficult undertaking as a robot is very nonlinear with many joints that are often organized and unstructured. To carry out the effective task, an effective PID control based on fuzzy logic has been employed together with the method of Particle Swarm Optimization (PSO) approach for the estimate of the parameter. This offline technique determines the lowest number of optimal robotic arm control parameters. To verify the controller design with computer simulation, a comparative assessment of controllers is given by means of a fuzzy surveillance controller with PSO which improves the parameter gain to provide a rapid climb, a smaller overflow, no steady condition error signal, and effective torque control of the robot arm.

An Ensemble Classification and Hybrid Feature Selection Approach for Fake News Stance Detection

The developments in Internet and notions of social media have revolutionised representations and disseminations of news. News spreads quickly while costing less in social media. Amidst these quick distributions, dangerous or seductive information like user generated false news also spread equally. on social media. Distinguishing true incidents from false news strips create key challenges. Prior to sending the feature vectors to the classifier, it was suggested in this study effort to use dimensionality reduction approaches to do so. These methods would not significantly affect the result, though. Furthermore, utilising dimensionality reduction techniques significantly reduces the time needed to complete a forecast. This paper presents a hybrid feature selection method to overcome the above mentioned issues. The classifications of fake news are based on ensembles which identify connections between stories and headlines of news items. Initially, data is pre-processed to transform unstructured data into structures for ease of processing. In the second step, unidentified qualities of false news from diverse connections amongst news articles are extracted utilising PCA (Principal Component Analysis). For the feature reduction procedure, the third step uses FPSO (Fuzzy Particle Swarm Optimization) to select features. To efficiently understand how news items are represented and spot bogus news, this study creates ELMs (Ensemble Learning Models). This study obtained a dataset from Kaggle to create the reasoning. In this study, four assessment metrics have been used to evaluate performances of classifying models.

Simulation of Medical Data Cloud Information Storage Encryption Based on Fuzzy Particle Swarm Optimization

Abstract In order to reduce the information storage load of network library and improve the security of medical data cloud information storage, information encryption design is needed. Therefore, a cloud information storage encryption simulation based on fuzzy particle swarm optimization algorithm is proposed. Firstly, a complex database access model based on fuzzy particle swarm optimization algorithm is constructed, the encrypted statistical features of cloud storage information are extracted, and the random scrambling matching method is used for coding correction. Fuzzy particle swarm clustering realizes the fusion of cloud information output results and realizes the optimization of cloud information storage encryption.

Multi-homed abnormal behavior detection algorithm based on fuzzy particle swarm cluster in user and entity behavior analytics

User and entity behavior analytics (UEBA) is an anomaly detection technique that identifies potential threat events in the enterprise's internal threat analysis and external intrusion detection. One limitation of existing methods in UEBA is that many algorithms use deterministic algorithms only for one category labeling and only compare with other samples within this category. In order to improve the efficiency of potential threat identification, we propose a model to detect multi-homed abnormal behavior based on fuzzy particle swarm clustering. Using the behavior frequency-inverse entities frequency (BF-IEF) technology, the method of measuring the similarity of entity and user behavior is optimized. To improve the iterative speed of the fuzzy clustering algorithm, the particle swarm is introduced into the search process of the category centroid. The entity's nearest neighbor relative anomaly factor (NNRAF) in multiple fuzzy categories is calculated according to the category membership matrix, and it is combined with boxplot to detect outliers. Our model solves the problem that the sample in UEBA is evaluated only in one certain class, and the characteristics of the particle swarm optimization algorithm can avoid clustering results falling into local optimal. The results show that compared with the traditional UEBA approach, the abnormal behavior detection ability of the new method is significantly improved, which can improve the ability of information systems to resist unknown threats in practical applications. In the experiment, the accuracy rate, accuracy rate, recall rate, and F1 score of the new method reach 0.92, 0.96, 0.90, and 0.93 respectively, which is significantly better than the traditional abnormal detections.