Particle Swarm Optimization PSO Research Articles

Studying the Optimal Frequency Control Condition for Electric Vehicle Fast Charging Stations as a Dynamic Load Using Reinforcement Learning Algorithms in Different Photovoltaic Penetration Levels

This study investigates the impact of renewable energy penetration on system stability and validates the performance of the (Proportional-Integral-Derivative) PID-(reinforcement learning) RL control technique. Three scenarios were examined: no photovoltaic (PV), 25% PV, and 50% PV, to evaluate the impact of PV penetration on system stability. The results demonstrate that while the absence of renewable energy yields a more stable frequency response, a higher PV penetration (50%) enhances stability in tie-line active power flow between interconnected systems. This shows that an increased PV penetration improves frequency balance and active power flow stability. Additionally, the study evaluates three control scenarios: no control input, PID-(Particle Swarm Optimization) PSO, and PID-RL, to validate the performance of the PID-RL control technique. The findings show that the EV system with PID-RL outperforms the other scenarios in terms of frequency response, tie-line active power response, and frequency difference response. The PID-RL controller significantly enhances the damping of the dominant oscillation mode and restores the stability within the first 4 s—after the disturbance in first second. This leads to an improved stability compared to the EV system with PID-PSO (within 21 s) and without any control input (oscillating more than 30 s). Overall, this research provides the improvement in terms of frequency response, tie-line active power response, and frequency difference response with high renewable energy penetration levels and the research validates the effectiveness of the PID-RL control technique in stabilizing the EV system. These findings can contribute to the development of strategies for integrating renewable energy sources and optimizing control systems, ensuring a more stable and sustainable power grid.

Design and Implementation of PID Controller for the Cooling Tower's pH Regulation Based on Particle Swarm Optimization PSO Algorithm

The PH regulation of cooling tower plant in southern fertilizers company (SCF) in Iraq is important for industry pipes protection and process continuity. According to the Mitsubishi standard, the PH of cooling water must be around (7.1 to 7.8). The deviation in PH parameter affects the pipes, such as corrosion and scale. Acidic water causes pipes to corrode, and alkaline water causes pipes to scale. The sulfuric acid solution is used for PH neutralization. The problem is that the sulfuric acid is pumped manually in the cooling tower plant every two or three hours for PH regulation. The manual operation of the sulfuric acid pump makes deviations in the PH parameter. It is very difficult to control the PH manually. To solve this problem, a PID controller for PH regulation was used. The reason for using the PID controller is that the PH response is irregular through the neutralization process. The methodology is to calculate the transfer function of the PH loop using the system identification toolbox of MATLAB, to design and implement a PID controller, to optimize the PID controller response using particle swarm optimization PSO algorithm, and to make a comparison among several tuning methods such as Ziegler Nichols (ZN) tuning method, MATLAB tuner method, and PSO algorithm tuning method. The results showed that the PSO-based PID controller tuning gives a better overshoot, less rise time, and an endurable settling time than the other tuning methods. Hence, the PH response became according to the target range. The experimental results showed that the PH regulation improved using the PSO-based PID controller tuning.

Optimized modular nuclear reactor project utilizing artificial intelligence: Seed-blanket concept

This article proposes a Light Water Small Modular Reactor (LW-SMR) based on the seed-blanket concept with thorium fuel, utilizing Artificial Intelligence (AI) for optimization. The objectives include defining SMR parameters and developing parallel multi-objective AI models using Genetic Algorithm (GA) and Particle Swarm Optimization PSO. A GA has been developed and applied to a simplified fuel assembly, while a PSO algorithm has been used together with the SERPENT code to perform precise calculations for SMR simulation. The results obtained demonstrated a great improvement when compared to classical parametric studies, demonstrating the possibility of designing a fuel assembly using artificial intelligence.

Dynamic Optimization for Mobile Edge Computing: A Comparative Study

Edge computing EC is an advanced technology where a server at the edge resource is allocated at the network’s edge, closely to end users, mobile devices, sensors, and the developing Internet of Things (IoT). To date, a large number application using edge computing concept has developed and equipped in the market. However, “Edge” refer to continuum network resources as well as computing; through cloud center of data and the sources of data. The Edge is not limited to request service or contents; it is extended to perform tasks like computing from the cloud. One main reason to get well acceptance to the research community is; the edge is able to offload computing, store the data, processing and caching, request distribution and service delivery to the user from the cloud. In this paper, extensive experiments are conducted which leads to a comparison of several methods in order to solve two problems e.g. Power Allocation problem using Non-Cooperative Game model based on sub-Gradient NCGG and Nertia-Weighted Particle Swarm Optimization PSO methods. Similarly, Joint Request Offloading and Computing Resource Scheduling JRORS using MO-NSGA and Binary article Swarm Optimization BPSO will explored to check their effectiveness and efficiency to our day to day computing operation.

PSO Optimized CNN-SVM Architecture for Covid -19 Classification

This paper presents a hybrid model that utilizes PSO particle swarm optimization, Convolution Neural Networks (CNN) and (SVM) Support Vector Machine architecture for recognition of Covid19.The planned model extracts optimized structures with particle swarm optimization then passes to Convolution Neural Network for automatic feature extraction, while the SVM serves as a Multi classifier. The dataset comprises Covid 19, Pneumonia and Normal Chest X-Ray pictures used to hone and evaluate the suggested algorithm. The most distinct traits are automatically extracted by the algorithm from these photographs. Experimental results show that the suggested framework is effective, with an average recognition accuracy of 97.42%.The most successful SVM Kernel was RBF.

Power System Stabilizer Design using Genetic Algorithms and Particle Swarm Optimization

in this paper, Meta-heuristic techniques using genetic algorithms GA and particle swarm optimization PSO to tuning optimal design of power system stabilizer PSS proposed. This latter have been used for many years to add damping to electromechanical oscillations of power system, Based on this idea we have proposed multiobjective function composed with tow function, first maximize stability margin by increasing the damping factors while minimizing the real parts of the eigenvalues. Simulation results to comparative study between genetic algorithms and particle swarm optimization obtained by our realized graphical user interface (GUI) proved the efficiency of PSS optimized by genetic algorithms in comparison with Particle Swarm Optimization, showing stable system responses almost insensitive to large parameter variations and under different operating regime (under-excited, nominal and over excited regime).

A novel grey prediction model with structure variability and real domain fractional order and its performance comparisons

PurposeThis paper aims to overcome the problem that the single structure of the driving term of the grey prediction model is not adapted to the complexity and diversity of the actual modeling objects, which leads to poor modeling results.Design/methodology/approachFirstly, the nonlinear law between the raw data and time point is fully mined by expanding the nonlinear term and the range of order. Secondly, through the synchronous optimization of model structure and parameter, the dynamic adjustment of the model with the change of the modeled object is realized. Finally, the objective optimization of nonlinear driving term and cumulative order of the model is realized by particle swarm optimization PSO algorithm.FindingsThe model can achieve strong compatibility with multiple existing models through parameter transformation. The synchronous optimization of model structure and parameter has a significant improvement over the single optimization method. The new model has a wide range of applications and strong modeling capabilities.Originality/valueA novel grey prediction model with structure variability and optimizing parameter synchronization is proposed.HighlightsThe highlights of the paper are as follows:A new grey prediction model with a unified nonlinear structure is proposed.The new model can be fully compatible with multiple traditional grey models.The new model solves the defect of poor adaptability of the traditional grey models.The parameters of the new model are optimized by PSO algorithm.Cases verify that the new model outperforms other models significantly.

A Monte Carlo Enhanced PSO Algorithm for Optimization Design of Heliostat Field

Tower solar thermal power generation technology is a new low-carbon and environmentally friendly clean energy technology. In this paper, based on Monte Carlo algorithm model and particle swarm optimization PSO algorithm, the optimization model of heliostatic mirror field is constructed, which is of great practical significance for effectively improving the collection and conversion efficiency of solar energy and promoting the sustainable development of clean energy.

MULTI-MODAL MEDICAL IMAGE FUSION BASED ON MULTI-BASED BINARY RESIDUALFEATURE FUSION

The term "medical image fusion" refers to the procedure of aligning and blending separate pictures captured using different imaging technologies. By enhancing imaging quality and decreasing unpredictability and redundancy, it aims to boost the clinical usefulness of medical pictures for the diagnosis and evaluation of medical conditions. Accuracy of clinical choices based on medical pictures has been enhanced via the use of multi model medical image fusion algorithms and tools. In this study, we show that combining pictures from chest X-rays and ultrasounds improve visibility. On the other hand, the merged picture is essential for a high-quality display. Improving the quality of the combined photos requires a classification technique. The accurate fusion and integration of medical images from multiple modalities is a critical step in the diagnostic process that leads to clinical activities and the appropriate treatment of patients, and this chapter suggests a Multi-based Binary Residual Feature Fusion (MBRFF) method for this purpose. The images are preprocessed usingthe Histogram equalization algorithm. For the feature extraction of medical images, the Multi- Threshold Contour Method is used. The shift variant decomposition transformation technique Discrete Wavelet Transform DWT is effective and it is used to decompose the medical image. The fused medical images are optimized by using Particle swarm optimization PSO. Finally, the simulation results reveal that the suggested mechanism outperforms the other techniques based on conventional approaches. Using the Origin Pro application, the study's outcomes are represented graphically

Metaheurystic optimization of CFD–multiphase population balance and biokinetics aeration stirrer tank bioreactor of sludge flocs for scale-up study with bio(de/re)flocculation

In this paper, dynamic behavior and optimization of aerated sludge flocs stirrer tank bioreactors in scale-up study have been investigated. Three-dimensional CFD multiphysics model of bioreactor with multiphase population balance and biokinetics with metaheurystic process optimization has been developed. Simulation of hydrodynamics-PBM-kinetic relation on biomass growth have been investigated in full transient CFD analysis. De(re)flocculation event was induced to impact of bioflocculation phenomena, which has an important impact of aeration bioprocess efficiency. In this paper it is evidenced that population balance and bio(de/re)flocculation is a crucial factor affecting on process efficiency. Selected metaheurystic algorithm (genetic GA, ant colony optimization ACO, particle swarm optimization PSO) has been used to bioprocess parameter optimization. The results showed that the best value is achieved by ACO, but PSO and GA also perform satisfactory results of parameter optimization process model.

Performance Comparison of Particle Swarm Optimization and Genetic Algorithm Combined with A* Search for Solving Facility Layout Problem

Optimization metaheuristics have become necessary due to the growing demand for better and more realistic designs. This paper proposes a metaheuristic-based approach for solving design problems in a reasonable time while browsing large spaces of solutions. The objective of this article is to compare the performance of two methods Genetic Algorithm GA and Particle swarm optimization PSO, combined with A* algorithm, in solving a constrained facility layout problem. The two chosen metaheuristics have been successfully applied in many search problems. We consider their speed and performance. The performance of the obtained solutions is measured in terms of the total distance traveled by products in the workshop. In order to determine the shortest path in a realistic way between workstations in a given irregular area (with aisle structure, or material storage areas, lunchrooms and offices), the A* algorithm was integrated with them. The comparison therefore concerns <GA, A*> and <PSO, A*>. GA and PSO algorithms generate configurations for which the shortest path for any couple of machines is identified through the A* search algorithm taking into account of obstacles. The mathematical model used and the parameters of the genetic algorithm are those developed in (Besbes et al. 2019). The numerical results show the feasibility and effectiveness of both approaches. Our results demonstrate that GA yields a better solution than Particle Swarm Optimization in total distance travelled while PSO is faster.

Efficient Single-Stage Bridgeless AC to DC Converter Using Grey Wolf Optimization

Bridgeless single-stage converters are used for efficient (alternative current) AC-(direct current) DC conversion. These converters control generators, like electromagnetic meso- and micro-scale generators with low voltage. Power factor correction helps increase the factor of the power supply. The main advantage of the power factor is it shapes the input current for increasing the real power of the AC supply. In this paper, a two-switch bridgeless rectifier topology is designed with a power factor correction capability. For the proposed converter topology to have good power quality parameters, the closed loop scheme, which uses the grey wolf optimization (GWO) algorithm, is implemented. The successes of GWO encourage this research to implement GWO in the topology. The performance of the proposed topology is analyzed under different load conditions. Simulation is carried out using the MATLAB/Simulink environment, and the results are compared with those of conventional (proportional integral derivative) PID and (particle swarm optimization) PSO controllers. To validate the simulation results, a 350-W hardware prototype is implemented, and the voltage ripple, efficiency, and power factor under different load conditions are analyzed and tabulated. The comparative study clearly indicates that the proposed converter topology with a closed loop control scheme using the GWO algorithm improves the power factor to 0.9732 and reduces the voltage ripple to 0.12% with a conversion efficiency of 98.25%.

Application of Network Security Event Correlation Analysis and Situation Evaluation Technology

Network security situational awareness can integrate all aspects of network security elements. Through correlation analysis, information fusion, situation prediction and other technologies to realize the intelligent analysis and comprehensive decision-making of complex information systems, network security situation awareness can improve the management efficiency and effect of complex networks. In order to solve the problem of parameter optimization of existing situation assessment methods, the parameters of SVM model are optimized based on Particle Swarm Optimization PSO algorithm. This paper presents a network security situation assessment method based on PSO and SVM. Using this algorithm can get a better balance between time-consuming and improving accuracy. At the same time, the index weight is determined according to grey correlation analysis, and the training samples are input to support vector machine for training. In this paper, the improved particle swarm optimization algorithm is used to optimize the parameters of support vector machine to improve the effect of situation assessment. Simulation test results show that the evaluation method improves the effectiveness and accuracy of situation assessment.

A Proposed Channel Estimation Based on Enhanced Sub-carrier Index Modulation and Packet-Discrete Wavelet Transform to Minimize Bit Error Rate

For Orthogonal Frequency Division Multiplexing (OFDM) and other communication systems, many estimating approaches have been developed to estimate the channel state information and lower the Bit Error Rate (BER). These estimating methods, however, are still subject to the influence of large peak powers compared to average powers. Reduced computational complexity is one of the most significant factors to consider while developing a new estimate algorithm. This study aims to provide a novel design of the Packet-Discrete Wavelet Transform (P-DWT) algorithm for channel estimation in wireless OFDM instead of the fast Fourier transform (FFT). It is presented to retrieve the code of a spread spectrum signal and transmitted data bits, and it is compared to particle swarm optimization PSO and least mean square (LMS) optimization. The suggested approach reduces the computing cost of DWT by recognizing the Packet Wavelet Transform (PWT) coefficients and local points, findings utilizing P-DWT channels generated from both models and measurements show that the proposed technique outperforms pilot-based channel estimation in terms of bit error rate under sparseness conditions BER. Moreover, as compared to typical semi-blind approaches, the estimation accuracy is enhanced while computing cost is reduced.

New hybrid evolutionary models for spatial prediction of soil properties in Kurdistan

AbstractWith intense human activity causing constant environmental change, there is a greater need than ever to have accurate and frequently updated soil information. Traditional soil maps are large‐scale polygon maps of soil type and implicitly assume no variation in soil properties within polygons. DSM approaches can increase the accuracy of modelling. Therefore, the current study was conducted to evaluate the applicability of a classic adaptive neuro‐fuzzy inference system (ANFIS) and three hybridized ANFIS models (i.e. particle swarm optimization—PSO, the genetic model—GA and the artificial bee colony model—ABC) for predicting eight soil properties in western Iran. The ANFIS+ABC and ANFIS+PSO approaches showed the best performance in predicting soil properties such as soil organic carbon (SOC) and total nitrogen (N). The ABC+ANFIS‐hybrid model increased R2 values by 0.32, 0.24, 0.23 and 0.37 and decreased RMSEs by 11%, 32.7 mg kg−1, 0.08 mmhos cm−1 and 0.3% when predicting SOC, K, EC and SP, respectively, compared with the ANFIS model alone. Also, the use of the PSO+ANFIS‐hybrid model led to an increase in R2 values of 0.44, 0.29, 0.34 and 0.36 and a decrease in RMSEs of 0.02, 0.29 ppm, 0.34% and 0.36% for predicting N, P, pH and TNV, respectively, compared with the ANFIS model alone. In general, the ANFIS+ABC and ANFIS+PSO improved the prediction accuracies for SOC and N by 32% and 24%, respectively, compared with the classic ANFIS model. As a whole, our findings demonstrated the ability of hybrid models for improving the accuracy of the ANFIS approach for predicting soil features in western Iran, which supports our main hypothesis.

A single-branch impedance compression network (icn) optimized by particle swarm optimization algorithm for rf energy harvesting system

Most previous works regarding the impedance compression network ICNs reported in the literature consist of dual branches to carry out the compression functionalities. Although this type of circuits are efficient, they are complex and have large sizes. This work proposes a complex impedance compression network with only a single branch for wireless power transfer (WPT) circuits, operating at 2.4GHz under a wide input power range and a variable load. The design size is about 4x4.2mm2 which thereby, produces a simple, and small design. The simulation results demonstrate that the improvement in efficiency is by 10%, as compared to a design without ICN. The improvement can increased by utilizing the particle swarm optimization PSO algorithm where the best dimensions of the ICN components will be selected relying on the suitable fitness function in the PSO algorithm to the given desired goals. The impedance matching is also enhanced in which it stands behind the conversion efficiency increment. The circuit size is very slightly increased and it becomes 4.2x4.3mm2. The final design has a conversion efficiency about 76%, with more than 15% improvement to a design with only a matching circuit or without using the automation techniques. Eventually, results show that the enhancement is obtained not only the designated input power but also along a range of the input power.

Neural Network Optimization Algorithms to Predict Wind Turbine Blade Fatigue Life under Variable Hygrothermal Conditions

Moisture and temperature are the most important environmental factors that affect the degradation of wind turbine blades, and their influence must be considered in the design process. They will first affect the resin matrix and then, possibly, the interface with the fibers. This work is the first to use a series of metaheuristic approaches to analyze the most recent experimental results database and to identify which resins are the most robust to moisture/temperature in terms of fatigue life. Four types of resin are compared, representing the most common types used for wind turbine blades manufacturing. Thermoset polymer resins, including polyesters and vinyl esters, were machined as coupons and tested for the fatigue in air temperatures of 20 °C and 50 °C under “dry” and “wet” conditions. The experimental fatigue data available from Sandia National Laboratories (SNL) for wind turbine-related materials have been used to build, train, and validate an artificial neural network (ANN) to predict fatigue life under different environmental conditions. The performances of three algorithms (Backpropagation BP, Particle Swarm Optimization PSO, and Cuckoo Search CS) are compared for adjusting the synaptic weights of the ANN and evaluating the efficiency in predicting the fatigue life of the materials studied, under the conditions mentioned above. For accuracy evaluation, the mean square error (MSE) is used as an objective function to be optimized by the three algorithms.

Design an Optimal Fractional Order PID Controller Based on New Algorithms and a Fuzzy Logic Controller to Regulate Type 1 Diabetes Patients

Diabetes is one of the most important diseases that researchers have focused on in scientific research since the time, because of the seriousness of this disease if it is not properly dealt with, especially with the emergence of some global epidemics such as Corona Virus (COVID 19), as the pancreas is the organ responsible for regulating sugar in the blood by secreting the insulin enzyme, insulin is widely used to control blood sugar. Therefore, it is important that the required insulin value is constant and controlled. The aim of this study is to control the blood glucose value that is achieved as a desired value and to maintain it as a constant value using a proportional, integral, and derivative control unit (FOPID) fractional order of the control parameters. In this research, the new control unit is applied to Bergman's mathematical model as a non-linear and simple model that simulates the mechanism of the interaction of glucose and insulin in the blood, and based on this, a closed control loop was designed to regulate the level of blood sugar to be an automatic control of blood glucose using the measured data from Special sensor. The contribution in this scientific paper is to define the (FOPID) parameters according to the closed loop responses of the system, and these parameters were adjusted using new meta-heuristic algorithms including the Invasive Weed Optimization (IWO), the PSO Particle Swarm optimization, the Genetic Algorithm (GA), The bat optimization algorithm (BA) and (ACO). As a result, the results of the five modern algorithms were compared based on several criteria to find out which one was better using MATLAB / SIMULINK simulation. It was found that the IWO algorithm performs better than PSO. The simulation results of the closed-loop system of this controller at the time of settling, overshoot and control inputs indicate very positive results compared to previous results. In addition, a new method has been proposed which is to design a pump in the form of a valve to control insulin pumping by controlling it with the fuzzy logic control unit, which in turn, we obtained better results, compared to the results of other previous studies.

Experimental modeling and optimization of surface quality and thrust forces in drilling of high-strength Al 7075 alloy: CRITIC and meta-heuristic algorithms

Al 7075 is a renowned high-strength engineering material used in automotive and aerospace applications, wherein many functional cylindrical parts are subjected to internal or external loads. Engineered parts with form errors (cylindricity CE and circularity error Ce) result in undesirable vibration and high deformation in rotating parts. In addition, reduced surface roughness (SR) and thrust forces (TF) are essential to limit the secondary process (namely, polishing) and power consumption. Experiments are performed based on central composite design considering drilling parameters (point angle, cutting speed, and feed rate) as inputs and output performances as CE, Ce, TF, and SR. It is noted that, except feed rate for Ce, all other parameters are found significant toward the output performance. Also, prediction accuracy with ten random experimental cases resulted with the percent error of 8.4% for SR, 5.41% for TF, 10.64% for Ce, and 10.35% for CE, respectively. Continuous ribbon-like chips at higher cutting speed, loose fragmented chips at higher feed rate, and increased arc length and radius at higher point angle were observed from the chip morphology analysis. Criteria importance through inter-criteria correlation (CRITIC) method applied to determine the weight fractions for Ce, CE, TF, and SR was found equal to 0.2802, 0.1991, 0.3293, and 0.1914, respectively. Four algorithms (genetic algorithm GA, particle swarm optimization PSO, teaching learning-based optimization TLBO, and JAYA algorithm) were applied to determine the optimal drilling conditions. JAYA algorithm determined optimized drilling conditions ensure predicted output values found close to experimental values with an acceptable percent error of 10.8% for Ce, 8.9% for CE, 6.73% for SR, and 3.51% for TF, respectively.

Intelligent Approach for Enhancing Prediction Issues in Scalable Data Mining

Support vector regression (SVR) is one of the supervised machine learning algorithms that can be exploited for prediction issues. The main enhancement issue of SVR is attempting to select a reliable parameter to assure the high performance of SVR. In this paper, the intelligent approach is based on integrating the enhanced particle swarm optimization PSO with the SVR to achieve the proper SVR parameters that are used to improve SVR performance. The enhanced PSO is performed by implementing parallelized linear time-variant acceleration coefficients (TVAC) and inertia weight (IW) of PSO, called PLTVACIW-PSO. The proposed approach is evaluated by performing the experimental comparisons of the proposed algorithm with eleven different algorithms. These comparisons are performed by applying the proposed algorithm and these algorithms to 21 different datasets varying in their scales.