Fuzzy Particle Swarm Optimization Research Articles

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.

A credit scoring ensemble model incorporating fuzzy clustering particle swarm optimization algorithm

The emergence of credit has generated a wealth of data on consumer lending behavior. In recent years, financial institutions have also started to use such data to make informed lending decisions based on fine-grained customer data, but conventional risk assessment models are inadequate in meeting the risk control requirements of the financial industry. Therefore, this paper proposes a credit scoring ensemble model incorporating fuzzy clustering particle swarm optimization (PSO) algorithm to obtain better credit risk prediction capability. First, a weighted outlier detection method based on the Induced Ordered Weighted Average Operator is proposed to preprocess the data to reduce noisy data’s misleading effect on model training. Then, an undersampling method combined with fuzzy clustering PSO is proposed to overcome the negative effect of category imbalance on model training by resampling the data. In addition, a hyperparameter optimization framework is introduced to adaptively adjust important parameters in the ensemble model considering the impact of parameter settings on the training performance of the model. Based on the evaluation metrics of F-score, AUC, and Kappa coefficient, an empirical analysis was conducted on five credit risk datasets. The results show that the proposed method outperforms the comparative model with an improvement of 10% to 50% in terms of F-score and AUC. The highest achieved F-score is 0.9488, and the maximum AUC is 0.9807, demonstrating the effectiveness of the proposed method. The kappa coefficient results indicate a high level of consistency in the predicted classification results of the model.

Hybrid approach of type-2 fuzzy inference system and PSO in asthma disease

This research work presents a hybrid approach combining a type-2 fuzzy inference system with particle swarm optimization (PSO) to develop a type-2 fuzzy optimized inference system, specifically tailored for asthma patient data. Addressing the inherent uncertainty in medical diagnostics, this model enhances traditional type-1 fuzzy logic by incorporating ambiguity into linguistic variables and utilizing type-2 fuzzy if-then rules. The system is trained to minimize diagnostic error in asthma disease identification. Applied to a dataset comprising eight medical entities from asthma patients, the model demonstrates substantial accuracy improvements. Numerical computations validate the system, showing a decrease in error rate from 1.445 to 0.03, indicating a significant enhancement in diagnostic precision. These results underscore the potential of our model in medical diagnostic problems, providing a novel and effective tool for tackling the complexities of asthma diagnosis.

Application and Performance Evaluation of Industrial Internet Platform in Power Generation Equipment Industry

The development and application of the industrial Internet platform (IIP) has brought enterprises huge benefits, such as improving the efficiency of resource management and equipment maintenance, achieving the precipitation and reuse of industrial knowledge, and enhancing the development ability of industrial APPs, etc. Therefore, in order to accelerate the application of the IIP, promote the digital transformation of enterprises, and enhance the application effect of the IIP, it is necessary to evaluate the application level and performance of the enterprise IIP. In this paper, taking the IIP in the power generation equipment industry as the research object, the background, architecture, and implementation process of the platform is described, and an evaluation method is proposed. First, a third-level evaluation index system is proposed via reference analysis and the Delphi method from four dimensions—platform basic condition, platform technology capability, platform application service capability, and platform input–output capability—and a total of sixty-five bottom indexes are designed in particular. Then, the fuzzy analytic hierarchy process (FAHP) and particle swarm optimization (PSO) algorithm are used to determine the weights of indexes, where FAHP is used to construct a fuzzy judgment matrix and PSO is adopted to adjust the consistency of the fuzzy judgment matrix in the FAHP. Finally, through the weights of indexes and questionnaire scores, the evaluation results are obtained, and the application level and performance of the IIP in the power generation equipment industry is analyzed. Through analysis, the important indexes that have a significant influence on the evaluation are determined, and the weaknesses that need to be enhanced are also pointed out, which can guide enterprises to make targeted improvements on the IIP in the power generation equipment industry so as to ensure the sustainable development of the power generation equipment industry.

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.

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.

Implementation of PV Based Multilevel Inverter to Improve Power Quality using Fuzzy PI and PSO PI Controllers

Power quality is the primary issue to be taken into consideration in modern electrical systems, particularly on the distribution side, to protect sensitive loads. Long-term uses can never run out of renewable resources. Connecting STATCOM at the distribution side enhances the power factor by improving the quality of the current waveform. The reactive power range that needs adjustment by utilising PV arrays on the STATCOM's DC side. Increases due to the large rise in terms of the PV power plants' size and capacity. Cascaded H Bridge multilevel topologies can increase the flexibility and effectiveness of PV modules. The suggested approach in this paper gives outcome of reduced peak over shoot, rise time, settling time, steady state error, percentage of ripples and total harmonic distortion which improves the power quality. PV system with cascaded H bridge multilevel inverters STATCOM for harmonic suppression and to make reactive powerless. To produce modulation indices for H Bridge switching, synchronised reference frame theory is modified. H bridges' average DC voltage is managed by a PI controller, and controller gains are obtained using fuzzy, PSO, and the trial-and-error approach. To confirm the viability of the suggested approach, simulation results using MATLAB/SIMULINK are shown. The experimental setup of proposed method is carried out to validate the inverter response.

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.

Web Robot Detection Based On Fuzzy System and PSO Algorithm

Web Robot Detection Based On Fuzzy System and PSO Algorithm

PSOα: A Fragmented Swarm Optimisation for Improved Load Frequency Control of a Hybrid Power System Using FOPID

Particle swarm optimisation (PSO) is one of the widely adopted meta-heuristic methods for solving real-life problems. Its practical utility can be further enhanced by improving its performance. In order to acheive this, academics have presented several variants of the original PSO over the past few years, including the quantum PSO (QPSO), bare-bones PSO (BB-PSO), hybrid PSO, fuzzy PSO, etc. In this paper, the performance of PSO is improved by proposing a fragmented swarm optimisation approach known as the PSOα. The PSOα is tested and compared with PSOs over 14 different benchmarking cost functions to validate its efficacy. The analysis is also carried out to see the impact of α on its performance. It is observed that the average value of the cost function over 50 simulations obtained using the fragmented swarm approach is lower than that obtained using the standard PSO in 12 out of 14 benchmark functions. Similarly, the fragmented approach outperforms the standard PSO in 13 out of 14 benchmark functions when compared with the best fitness value achieved out of 50 simulations. Finally, the proposed approach is applied to solve the well-known real-life optimisation problem of load frequency control (LFC) in power systems. A test system comprising both renewable and traditional power sources is considered to evaluate the efficacy of the proposed technique. A fractional order proportional-integral-differential (FOPID) controller is used, whose parameters are optimised using the proposed PSO for achieving the LFC. The proposed fragmentation approach can be applied with other optimisation techniques to improve their performance.

Robust Control of SEDCM by Fuzzy-PSO

Industries have many rotational operations that are used for design, transport, lift, drilling, rolling, robotics, and many other applications. These rotating applications require a proper controller for accurate control of the operation. Separately excited DC motors (SEDCMs) are versatile and have various industrial operations because of their specific speed control characteristics. So, for smooth and accurate operation of an SEDC motor, controllers should be used. PI and PID controllers are used in many cases, but they are ineffective for nonlinear load operation. A fuzzy controller is a heuristic controller and can provide automatic control of the operation. Its operation depends on the selection of the correct membership values. This work proposes a novel particle swarm optimization (PSO) technique that would provide the optimum value of the membership for fuzzy controllers for optimum control of the industrial processes. To obtain SEDC results, MATLAB simulation was performed, and the fuzzy controller with novel PSO was implemented. A fuzzy PSO controller used for motor speed control operation obtains a rise time of 0.00026 s, settling time of 0.000214 s, maximum overshoot of zero, and delay time of 0.016 s, which are the best values when compared to PID and PID-Fuzzy controllers. It is observed that the results obtained from the separately excited DC motor using a fuzzy PSO controller improve the dynamic behavior of the motor that so it smoothly tracks the required speed without any more overshoot or oscillation than the PID controller. Such dynamic, stable operation of the motor makes it perfect for industrial as well as household operations.

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.

IoT Smart Devices Risk Assessment Model Using Fuzzy Logic and PSO

IoT Smart Devices Risk Assessment Model Using Fuzzy Logic and PSO

Novel approach to design matched digital filter with Abelian group and fuzzy particle swarm optimization vector quantization

This paper presents a new method for designing matched digital filters with discrete valued coefficients. The fuzzy particle swarm optimization vector quantization (FPSOVQ) has been applied to obtain the optimum codebook in design of matched wavelet function. Abelian group has been used to extract the similarity present in the input voiced signal. Fuzzy particle swarm optimization (FPSO) process is used to find approximate ideal vector quantization (VQ) codebook to be carried out for compression of data. FPSOVQ scheme utilises features of fuzzy inference method (FIM) and expert particle swarm optimization (PSO). The generated codebook consists of set of highly ideal features, which are considered as the filter coefficients. These coefficients are used in the designing of the filter. All of the phonemes in the American English language were included in the 30 sentences that were chosen from the IEEE database. The sentences were originally down-sampled from 25 kHz to 8 kHz. The magnitude responses of each filter have been drawn, which indicates the characteristics of the filter. A comparison has been provided using energy compaction ratio as a parameter to judge the performance of matched designed filter with db4 filter. Experimental results show the advantage of the developed algorithm as the average value of energy compaction ratio for sampled voice signals is 2.8046 times lower for matched designed filter.

Research on Control Strategy of Buck Converter Based on Particle Swarm Optimization Fuzzy PID

Because buck converter is a typical non-minimum phase system, the traditional PID algorithm has the disadvantages of low control accuracy and high system overshoot, while fuzzy PID control only slightly improves the control effect. Therefore, this paper designs a control strategy based on particle swarm optimization fuzzy PID algorithm to enhance the control effect of the buck converter and combines chaotic variables to optimize the quality of the initial particle swarm. Finally, Matlab simulation experiments verify that the control strategy of fuzzy PID optimization using particle swarm optimization combined with chaotic variables has the advantages of short response time, high stability, and low system overshoot compared with the traditional PID algorithm and fuzzy PID algorithm.

Parameter identification of permanent magnet synchronous motor based on modified- fuzzy particle swarm optimization

Accurate estimation of PMSM parameters is beneficial to the high performance operation of PMSM. In order to prevent PSO from falling into the local optimal solution in the PMSM parameter identification process, so as to improve the accuracy of identification results, a modified fuzzy particle swarm optimization (MDFPSO) is proposed, which changes the speed of each particle from only affected by the optimal particle to affected by the surrounding particles, This improvement guarantees the identification accuracy of the algorithm, and introduce the convergence factor to ensure that the MDFPSO can converge. Simulation results show that the MDFPSO algorithm is effective in PMSM parameter identification.

Identification of fractional order Wiener-Hammerstein systems based on adaptively fuzzy PSO and data filtering technique

Identification of fractional order Wiener-Hammerstein systems based on adaptively fuzzy PSO and data filtering technique

Bias-compensated least squares and fuzzy PSO based hierarchical identification of errors-in-variables Wiener systems

This paper investigates the parameter estimation of errors-in-variables Wiener (EIV-W) nonlinear systems. In such nonlinear systems, both input and output contain interference noises, and some intermediate processes are also interfered by noises. The hierarchical technology is applied to decompose the whole system into two subsystems firstly. For the linear subsystem, in order to obtain unbiased estimates of model parameters, a bias compensation method is introduced. Then, the bias-compensated least squares (BLS) algorithm is proposed. For the nonlinear subsystem, on the basis of particle swarm optimisation (PSO), the fuzzy control technology is added to improve the ability of jumping out of the local optimum. Thus, a bias-compensated least squares and fuzzy PSO based hierarchical (BLS-FPSO-H) method is derived at last. In simulation, a numerical example and a case study about the carbon fibre stretching process are implemented. Results indicate that the BLS-FPSO-H algorithm can effectively identify EIV-W nonlinear systems, the convergence speed and identification accuracy are greatly improved than the basic PSO method and some other PSO variants.