Integrating Particle Swarm Optimization Research Articles

VSC-STATCOM Performance Under Different Fault Sensing using PSO Tuned Hybrid SMC

In this paper, we investigate the PSO-tuned hybrid SMC performance based VSC-STATCOM under different conditions of fault using hybrid renewable energy sources (HRES). A hybrid renewable energy resource system (HRES) consists of PV, wind power, and batteries. Here the Irradiance is the PV input and the wind energy is Wind Input. The storage of energy is used for battery. The battery is used for changing weather condition or the changing the condition of the environment. Hybrid VSC-STATCOM controller based on SMC to reduce power quality issues like sag, swell, harmonics etc. associated with HRES system mainly due to non-linear load conditions. The novelty of our proposed PSO-tuned hybrid Sliding Mode Controller (SMC) method lies in its integration of Particle Swarm Optimization (PSO) as a tuning mechanism within the SMC framework. The Harmonic reduction and efficiency improvement is verified by using the Simulation. Therefore, the proposed system performance is simulated and to optimize the terms are real and reactive power, Total Harmonic Distortion (THD) and Voltage Sag.

Research on force-controlled polishing system for robotic arm based on compliance control

This thesis enhances robotic arm control in intricate polishing environments by using impedance control for consistent force regulation between the arm and workpiece. An adaptive impedance control algorithm accommodates contact force variations, boosting system robustness. The PSO-BP variable impedance controller integrates particle swarm optimization and BP neural network for real-time optimization, overcoming limitations of fixed-parameter impedance control on surfaces with curvature changes. Experimental results show a significant reduction in polishing force fluctuations and improved force tracking. These findings support efficient robotic arm utilization in polishing tasks.

Refining Short-Term Power Load Forecasting: An Optimized Model with Long Short-Term Memory Network

Short-term power load forecasting involves the stable operation and optimal scheduling of the power system. Accurate load forecasting can improve the safety and economy of the power grid. Therefore, how to predict power load quickly and accurately has become one of the urgent problems to be solved. Based on the optimization parameter selection and data preprocessing of the improved Long Short-Term Memory Network, the study first integrated particle swarm optimization algorithm to achieve parameter optimization. Then, combined with convolutional neural network, the power load data were processed to optimize the data and reduce noise, thereby enhancing model performance. Finally, simulation experiments were conducted. The PSO-CNN-LSTM model was tested on the GEFC dataset and demonstrated stability of up to 90%. This was 22% higher than the competing CNN-LSTM model and at least 30% higher than the LSTM model. The PSO-CNN-LSTM model was trained with a step size of 1.9×10^4, the relative mean square error was 0.2345×10^-4. However, when the CNN-LSTM and LSTM models were trained for more than 2.0×10^4 steps, they still did not achieve the target effect. In addition, the fitting error of the PSOCNN-LSTM model in the GEFC dataset was less than 1.0×10^-7. In power load forecasting, the PSOCNN- LSTM model's predicted results had an average absolute error of less than 1.0% when compared to actual data. This was an improvement of at least 0.8% compared to the average absolute error of the CNNLSTM prediction model. These experiments confirmed that the prediction model that combined two methods had further improved the speed and accuracy of power load prediction compared to traditional prediction models, providing more guarantees for safe and stable operation of the power system.

A novel decision support system based on computational intelligence and machine learning: Towards zero-defect manufacturing in injection molding

Real-time monitoring solutions have gained popularity across industries due to the advent of Industry 4.0, AI, and big data enhancing the efficiency of industrial production and equipment decisions. Machine learning models that possess computing intelligence and interpretability provide superior predictive capabilities compared to manual adjustments, resulting in cost savings and manufacturing high-quality products. This study proposes a zero-defect manufacturing decision support system based on computational intelligence feature selection combined with interpretable machine learning. The decision support system integrates Particle Swarm Optimization (PSO) and the C4.5 decision tree method, abbreviated as PSO+C4.5, to enable the continuous monitoring of the injection molding process in real-time, considering production parameter information and collected data quality, guiding the decision-making process for implementing zero-defect manufacturing (ZDM). In contrast to existing research, our innovative methodology relies on computational intelligence techniques for extracting features and employs interpretable machine learning prediction models. In terms of quality prediction, our empirical findings show that the suggested method accomplishes the optimal balance between interpretability and predictive performance (Accuracy: 0.9889, Sensitivity: 0.9869, and Specificity: 0.9935). These characteristics can directly support maintenance personnel and operators in optimizing the processing quality process.

Optimizing asphalt foaming using neural network

AbstractThis study uses a three-layer backpropagation neural network combined with particle swarm optimization to control the foamed bitumen in cold recycling technology. The foaming process of bitumen is non-linear and depends on dynamic temperature. By developing a neural network model, this study effectively captures the complex relationships between temperature, water content, air pressure, and the expansion ratio and half-life of foamed bitumen. The integration of particle swarm optimization enhances the accuracy and convergence of the neural network model by optimizing the initial weights. This optimization process improves the model's ability to predict and control the quality of foamed bitumen accurately. It serves as a valuable tool for the rapid development of high-quality cold asphalt design.

Optimizing Heart Failure Detection: A Comparison between Naive Bayes and Particle Swarm Optimization

This research focuses on the importance of early detection of heart failure which is a serious global health problem. Given the variety of symptoms of heart failure, accurate early detection methods are needed with the aim of reducing the impact of this disease. This study uses the Naïve Bayes (NB) method which has been proven effective in classifying heart failure with significant variations in accuracy by integrating Particle Swarm Optimization (PSO) to improve the model. The evaluation model involves a confusion matrix including accuracy, precision, recall, and Area Under the Curve. The research results show that the integration of PSO in NB results in an increase in accuracy of 7.73%, an increase in precision of 6.42%, and an increase in recall of 1.93%. Although there was a small decrease in AUC. This research shows that the success of NB with PSO can help improve the performance of early detection of heart failure. This indicates the importance of this research in developing more accurate and effective detection methods for critical health conditions such as heart failure.

Cancer Detection Using a New Hybrid Method Based on Pattern Recognition in MicroRNAs Combining Particle Swarm Optimization Algorithm and Artificial Neural Network

MicroRNAs (miRNAs) play a crucial role in cancer development, but not all miRNAs are equally significant in cancer detection. Traditional methods face challenges in effectively identifying cancer-associated miRNAs due to data complexity and volume. This study introduces a novel, feature-based technique for detecting attributes related to cancer-affecting microRNAs. It aims to enhance cancer diagnosis accuracy by identifying the most relevant miRNAs for various cancer types using a hybrid approach. In particular, we used a combination of particle swarm optimization (PSO) and artificial neural networks (ANNs) for this purpose. PSO was employed for feature selection, focusing on identifying the most informative miRNAs, while ANNs were used for recognizing patterns within the miRNA data. This hybrid method aims to overcome limitations in traditional miRNA analysis by reducing data redundancy and focusing on key genetic markers. The application of this method showed a significant improvement in the detection accuracy for various cancers, including breast and lung cancer and melanoma. Our approach demonstrated a higher precision in identifying relevant miRNAs compared to existing methods, as evidenced by the analysis of different datasets. The study concludes that the integration of PSO and ANNs provides a more efficient, cost-effective, and accurate method for cancer detection via miRNA analysis. This method can serve as a supplementary tool for cancer diagnosis and potentially aid in developing personalized cancer treatments.

Towards sustainable water pumping systems: Integration of particle swarm optimization and direct torque control PSO-DTC

This research paper focuses on the optimization of solar water pumping systems (SWPS) by combining the particle swarm optimization (PSO) technique on the generator photovoltaic (GPV) side and direct torque control (DTC) on the pump motor side. The integration of a maximum power point tracking system (MPPT-PSO) represents a significant advance, enabling maximum power to be extracted from the GPV whatever the weather conditions. The main objective is to improve the energy efficiency of the SWPS system by maximizing the electrical power dedicated to the pumping system. At the same time, the application of DTC to the pump motor aims to optimize the use of the energy generated by the GPV. This intelligent approach adjusts the motor power according to the power extracted from the GPV, thus avoiding unsuitable profiles for the pumping system. This results in better power quality, longer system life and improved tracking speed, which in turn improves the pump's rotational speed, which in turn improves the flow rate of the water pumped. The results of the study confirm that the combination of PSO-DTC approaches improves the overall performance of the SWPS. These improvements persist under different climatic conditions.

Using Particle Swarm Optimization with Backpropagation Neural Networks and Analytic Hierarchy Process to Optimize the Power Generation Performance of Enhanced Geothermal System (EGS)

The optimization of the production scheme for enhanced geothermal systems (EGS) in geothermal fields is crucial for enhancing heat production efficiency and prolonging the lifespan of thermal reservoirs. In this study, the 4100–4300 m granite diorite stratum in the Zhacang geothermal field was taken as the target stratum to establish a numerical model of water-heat coupling of three vertical wells. However, relying solely on numerical simulation for optimization is time-consuming and challenging for the determination of the globally optimal production plan. The present study proposes a comprehensive evaluation method for optimizing the performance of EGS power generation based on the integration of particle swarm optimization with backpropagation neural network (PSO-BPNN) and analytic hierarchy process (AHP). Five different PSO-BPNN models were constructed based on the numerical simulation data to predict different EGS power generation performance indexes, including the production temperature, the injection pressure, the total electricity generation, the electric energy efficiency and the levelized cost of electricity. Based on these PSO-BPNN models, the weights of various thermal development evaluation indexes were calculated by AHP to conduct a comprehensive evaluation of the power generation performance of the three vertical wells EGS. The results show that the PSO-BPNN model has good prediction accuracy for EGS prediction of various performance indicators, with a coefficient of determination (R2) exceeding 0.999. The AHP evaluation of all production schemes reveals that the optimal power generation scheme entails a well spacing of 580 m, water injection rate of 56 kg/s, injection temperature of 38 °C and fracture permeability of 2.0 × 10−10 m2. Over a span of 30 years, this scheme can provide a total power generation capacity amounting to 1775 GWh, with an associated LCOE value of 0.03837 USD/kWh. This not only provides a reference for the development and optimization of geothermal systems in the Zhacang geothermal field but also provides a new idea for the optimization design of other geothermal projects.

Optimizing Segmentation and Purchase Forecasting in Credit Card Transactions: A PSO-enhanced k-means and ANN Approach

In the rapidly evolving landscape of data-driven marketing, machine learning has emerged as a pivotal tool for analyzing complex consumer behaviors and enhancing strategic decision-making. This paper introduces a novel approach to optimize customer segmentation and purchase forecasting in credit card transactions through the synergistic integration of Particle Swarm Optimization (PSO)-enhanced k-means clustering and Artificial Neural Networks (ANN). The proposed methodology refines customer segmentation by leveraging PSO, resulting in more defined clusters. In the predictive modeling phase, an ANN outperforms conventional methods, providing superior accuracy in purchase forecasting. The study demonstrates the effectiveness of advanced algorithms in enhancing insights from credit card transaction data, offering valuable implications for improved decision-making in the financial domain.

A novel cat swarm optimization‐based fuzzy PI controller for improving dynamic response of converter

AbstractNowadays, the use of fuel cells in various applications has become very widespread. Some of their most important applications are in electric vehicles (EVs) and local off‐grid power systems. An EV works as an electric motor that burns a mixture of fuel and gases to generate electricity. One of the challenges with fuel cells is the slow dynamic response to load power changes. In the past few decades, the modeling and control of DC‐DC converters have undergone extensive research and development. However, the error, settling time, and peak overshoot performance are not reduced by the robust nonlinear controller and current mode controller. To overcome these problems, quantile regressive extreme seeking cat swarm optimized Mamdani fuzzy PI controller (QRESCSO‐MFPIC) approach is developed. The goal of the QRESCSO‐MFPIC approach is to reduce the integral time absolute error (ITAE) for tuning the fuzzy PI controller. Initializing the fuzzy PI controller parameter is an input in the QRESCSO‐MFPIC approach. For every parameter value, the fitness function is determined and quantile regression analysis is carried out. The efficiency of the QRESCSO‐MFPIC approach is measured by evaluating the settling time and peak overshoot. Tuning of the PI controller is carried out with cat swarm optimization (CSO) and particle swarm optimization (PSO) based on the objective function of ITAE. The result analysis shows that the QRESCSO‐MFPIC approach improves the efficiency of an optimized Fuzzy PI controller compared to the existing methods. Peak overshoot reduction and settling time are improved through the QRESCSO‐MFPIC approach than the proportional integral particle swarm optimization controller (PI‐PSO).

Exploratory analysis and evolutionary computing coupled machine learning algorithms for modelling the wear characteristics of AZ31 alloy

The wear resistance of magnesium alloys is one of its key technological properties that could limit their practical application. In accordance with ASTM G99–95a standard, this study used a pin-on-disc method to analyze the wear behavior of ascast AZ31 magnesium alloy under dry-sliding conditions. With a track radius of 37.5 mm, varied sliding velocities of 0.25, 0.5, 1, and 1.5 m/s, and normal loads of 40, 60, and 90 N were employed to quantify wear rate over a fixed sliding distance of 600 m. The surface morphology of the alloy's corroded surface was investigated using a SEM/EDS. The effectiveness of two Evolutionary Computing integrated machine learning algorithms, Particle Swarm Optimization coupled Decision Tree (PSO-DT) and Particle Swarm Optimization coupled Gradient Boosting Regressor (PSO-GBR), is also compared in this study in predicting the particular wear rate of AZ31 magnesium alloy. The experimental observations of wear behavior at various sliding velocities and normal loads make up the dataset used in this study. The algorithms' prediction performance was assessed using the coefficient of determination (R2), mean squared error (MSE), and mean absolute error (MAE). The results show that when it comes to foretelling the precise wear rate of AZ31 magnesium alloy, the PSO-GBR algorithm works better than the PSO-DT algorithm resulting in the R2 value of 0.99970. The PSO-GBR algorithm's successful integration of Particle Swarm Optimization and the gradient-boosting regressor model is responsible for this higher performance. The PSO-GBR algorithm improved accuracy and better captured nonlinear patterns in the data by improving the algorithm's parameters and capturing complex wear mechanisms.

Quality control prediction of electrolytic copper using novel hybrid nonlinear analysis algorithm

Traditional linear regression and neural network models demonstrate suboptimal fit and lower predictive accuracy while the quality of electrolytic copper is estimated. A more dependable and accurate model is essential for these challenges. Notably, the maximum information coefficient was employed initially to discern the non-linear correlation between the nineteen factors influencing electrolytic copper quality and the five quality control indicators. Additionally, the random forest algorithm elucidated the primary factors governing electrolytic copper quality. A hybrid model, integrating particle swarm optimization with least square support vector machine, was devised to predict electrolytic copper quality based on the nineteen factors. Concurrently, a hybrid model combining random forest and relevance vector machine was developed, focusing on primary control factors. The outcomes indicate that the random forest algorithm identified five principal factors governing electrolytic copper quality, corroborated by the non-linear correlation analysis via the maximum information coefficient. The predictive accuracy of the relevance vector machine model, when accounting for all nineteen factors, was comparable to the particle swarm optimization—least square support vector machine model, and surpassed both the conventional linear regression and neural network models. The predictive error for the random forest-relevance vector machine hybrid model was notably less than the sole relevance vector machine model, with the error index being under 5%. The intricate non-linear variation pattern of electrolytic copper quality, influenced by numerous factors, was unveiled. The advanced random forest-relevance vector machine hybrid model circumvents the deficiencies seen in conventional models. The findings furnish valuable insights for electrolytic copper quality management.

Prediction of reinforced concrete walls shear strength based on soft computing-based techniques

AbstractThe precise estimation of the shear strength of reinforced concrete walls is critical for structural engineers. This projection, nevertheless, is exceedingly complicated because of the varied structural geometries, plethora of load cases, and highly nonlinear relationships between the design requirements and the shear strength. Recent related design code regulations mostly depend on experimental formulations, which have a variety of constraints and establish low prediction accuracy. Hence, different soft computing techniques are used in this study to evaluate the shear capacity of reinforced concrete walls. In particular, developed models for estimating the shear capacity of concrete walls have been investigated, based on experimental test data accessible in the relevant literature. Adaptive neuro-fuzzy inference system, the integrated genetic algorithms, and the integrated particle swarm optimization methods were used to optimize the fuzzy model’s membership function range and the results were compared to the outcomes of random forests (RF) model. To determine the accuracy of the models, the results were assessed using several indices. Outliers in the anticipated data were identified and replaced with appropriate values to ensure prediction accuracy. The comparison of the resulting findings with the relevant experimental data demonstrates the potential of hybrid models to determine the shear capacity of reinforced concrete walls reliably and effectively. The findings revealed that the RF model with RMSE = 151.89, MAE = 111.52, and $${R}^2$$ R 2 = 0.9351 has the best prediction accuracy. Integrated GAFIS and PSOFIS performed virtually identically and had fewer errors than ANFIS. The sensitivity analysis shows that the thickness of the wall $$({b_\mathrm{{w}}})$$ ( b w ) and concrete compressive strength $$({f_\mathrm{{c}}})$$ ( f c ) have the most and the least effects on shear strength, respectively.

Temperature Regulation of Hot Vapor Preservation Treatment of Litchi Based on PSO-Fuzzy PID

In this paper, a control method was designed based on particle swarm optimization (PSO)–fuzzy PID (PSO-Fuzzy PID). In order to address the issues of significant nonlinearity and extensive hysteresis in the vapor temperature of litchi hot vapor preservation treatment equipment, as well as the inadequacy of traditional control schemes, a PSO-Fuzzy PID controller was developed through the integration of particle swarm optimization and fuzzy logic algorithms. This controller incorporates a scale factor and quantization factor, building upon the foundation of the Fuzzy PID controller. Additionally, a transfer function model was established for both the heating and disturbance links, utilizing a combination of test data modeling methods and relevant theory. Thirdly, a system simulation model was built to compare and verify PID, Fuzzy PID, and PSO-Fuzzy PID control methods, which were carried out on the MATLAB/Simulink simulation platform. Finally, the litchi load test was conducted to further verify the control performance of the PSO-Fuzzy PID method, where the litchi variety was “Jinggang Hongnuo”. The simulation results showed that the overshoot of the optimized control system was reduced by 93.5% and 42.9% compared with the traditional PID control and Fuzzy PID, and the regulation time was shortened by 46% and 31%, which indicates a better control effect. The test results showed that PSO-Fuzzy PID showed better system stability and accuracy than the fuzzy PID control method in different processing temperature and processing time for litchi’s hot vapor treatment. The maximum temperature difference decreased by 0.56 °C on average and the interference adjustment time was reduced by 7.5 s on average. PSO-Fuzzy PID has a good control effect on the temperature of litchi hot vapor treatment, which can provide a reference for model building and temperature control of other similar fruit and vegetable heat treatment equipment.

Integration of Particle Swarm Optimization (PSO) and Machine Learning to Improve Classification Accuracy During Antenna Design

Integration of Particle Swarm Optimization (PSO) and Machine Learning to Improve Classification Accuracy During Antenna Design

Modeling and performance analysis of elastostatic stiffness of kinematically redundant parallel mechanisms

An analytical elastostatic stiffness modeling approach for kinematically redundant parallel mechanisms (KR-PMs) according to screw theory and the second theorem of Castigliano is proposed. The 2-UPR/PRPU and 2-PUPR/PRPU KR-PMs are used as two examples of KR-PMs to validate the precision of the proposed approach. The relative error is less than 1.55% when the theoretical model is compared to the FEA model. The PM’s stiffness performance is assessed using the minimum linear displacement stiffness performance index, which is based on the theoretical model. By integrating Particle Swarm Optimization (PSO) with polynomial fitting technology, an analytical mapping model of the response surface between the optimal actuating distances and the moving platform (MP)’s given position and orientation is established. It is demonstrated that 2-UPR/PRPU and 2-PUPR/PRPU KR-PMs can significantly enhance the stiffness performance when compared to the stiffness performance of 2-UPR/RPU PM.

Physician scheduling problem in Mobile Cabin Hospitals of China during Covid-19 outbreak.

In this paper, we investigate a novel physician scheduling problem in the Mobile Cabin Hospitals (MCH) which are constructed in Wuhan, China during the outbreak of the Covid-19 pandemic. The shortage of physicians and the surge of patients brought great challenges for physicians scheduling in MCH. The purpose of the studied problem is to get an approximately optimal schedule that reaches the minimum workload for physicians on the premise of satisfying the service requirements of patients as much as possible. We propose a novel hybrid algorithm integrating particle swarm optimization (PSO) and variable neighborhood descent (VND) (named as PSO-VND) to find the approximate global optimal solution. A self-adaptive mechanism is developed to choose the updating operators dynamically during the procedures. Based on the special features of the problem, three neighborhood structures are designed and searched in VND to improve the solution. The experimental comparisons show that the proposed PSO-VND has a significant performance increase than the other competitors.

Improving QoS in Internet of Vehicles Integrating Swarm Intelligence Guided Topology Adaptive Routing and Service Differentiated Flow Control

Internet of Vehicles (IoV) is an evolution of vehicular adhoc network with concepts of internet of things (IOT). Each vehicle in IOV is an intelligent object with various capabilities like sensors, computation, storage, control etc. Vehicles can connect to any other entity in the network using various services like DSRC, C2C-CC etc. Ensuring QoS for vehicle to everything (V2X) communication is a major challenge in IoV. This work applies an integration of hybrid metaheuristics guided routing and service differentiated flow control to ensure QoS in Internet of Vehicles. Clustering based network topology is adopted with clustering based on hybrid metaheuristics integrating particle swarm optimization with firefly algorithm. Over the established clusters routing decision is done using swarm intelligence. Packet flows in the network are service differentiated and flow control is done at cluster heads to reduce the congestion in the network. High congestion in routes is mitigated with back up path satisfying the QoS constraints. Due to optimization in clustering, routing and data forwarding process, the proposed solution is able to achieve higher QoS. Through simulation analysis, the proposed solution is able to achieve 2% higher packet delivery ratio and 9.67% lower end to end packet latency compared to existing works.

Energy Consumption and QoS Optimization Coverage Mechanism in Wireless Sensor Networks Based on Swarm Sensing Algorithm

Objective. To study energy consumption and QoS optimization coverage mechanism in wireless sensor networks based on swarm sensing algorithm. Methods. The swarm sensor algorithm is reported to optimize the configuration of wireless sensor nodes, improve network service performance, and provide feedback on effective sensor placement. Results. Two WSN coverage optimization schemes were proposed in this essay. The first scheme applied drosophila intelligent algorithm to WSN coverage optimization, and the second scheme integrated particle swarm optimization algorithm and improved firefly algorithm to complement each other. Both schemes can achieve good results in practical application. Scheme one can increase the coverage rate by 6.36% compared with previous schemes, while scheme two can increase it by 6.48%. Both schemes are basically the same, so they can be applied in the optimization of WSN node deployment. Conclusion. As an optimization method, group perception algorithm is inspired by biology. Through the continuous enrichment and development of this algorithm, it has been effectively used in the coverage optimization of the wireless sensor networks.