Combining Particle Swarm Optimization Research Articles

Multi-objective optimization of switched reluctance motor driving electric vehicles based on vehicle operating condition

To improve the comprehensive performance of the switched reluctance motor (SRM) for electric vehicles (EVs) under actual driving conditions, a novel geometrical optimization method based on the time-varying phase current characteristics and the comprehensive indicator with justified weight coefficients is proposed in this paper. First, based on the established dynamic models of EV and SRM, the actual operating phase currents of the motor working under the New European Driving Cycle (NEDC) are extracted and classified to develop the representative optimization conditions. Then, considering the requirements of EV, five indicators are defined to evaluate the comprehensive performance of SRM, and a new multi-objective synchronization optimization function is proposed with five weight coefficients obtained by the statistical analysis method for the sample data of the indicators. Furthermore, the geometrical optimization for a four-phase 8/6 SRM is carried out by the combination of Particle Swarm Optimization (PSO) and BP neural network algorithms, after analyzing the effects of key geometrical parameters on the indicators. Finally, the work performance of the optimized SRM is evaluated and compared with that of the initial prototype, and the results show that the optimized SRM with the proposed multi-objective synchronization optimization method can improve the comprehensive indicator by 59.59%, and can significantly enhance the vehicle actual working economy by 50.74% under NEDC cycle.

Exploring Gene Expression Biclustering with Integrated PSO-SA-Fuzzy Logic Methodology and its Application to Air Pollution Analysis

<p style="line-height:150%;margin-bottom:10.0pt;text-align:justify;"><span style="font-family:"Times New Roman",serif;font-size:12.0pt;line-height:150%;" lang="EN-US">Gene expression data analysis is crucial for understanding complex biological mechanisms, yet current biclustering techniques struggle with noise, unpredictability, and intrinsic uncertainty. This study proposes an innovative biclustering method combining particle swarm optimization (PSO), simulated annealing (SA), and fuzzy logic to improve gene expression analysis. By integrating natural language processing (NLP) semantic similarity into the PSO framework, the method enhances the capture of intricate gene interactions. Additionally, environmental factors, particularly air pollution, are incorporated to explore their impact on gene expression patterns. The approach leverages the complementary strengths of PSO's exploration capabilities, SA's exploitation efficiency, and fuzzy logic's ability to handle data ambiguity. Comparative assessments on benchmark datasets reveal that this integrated strategy significantly outperforms individual methods in accuracy and resilience. The results demonstrate the PSO-SA-Fuzzy Logic method's superior capacity to detect nuanced and context-dependent gene expression patterns, offering a robust solution to the limitations of existing biclustering techniques. The method not only improves precision and computational efficiency but also enhances the detection of significant gene expression patterns under varying conditions, including environmental stressors. This advancement represents a notable contribution to computational biology, providing a more effective tool for gene expression data analysis.</span></p>

A Mechanical Fault Diagnosis Method for UCG-Type On-Load Tap Changers in Converter Transformers Based on Multi-Feature Fusion

The On-Load Tap Changer (OLTC) is the only movable mechanical component in a converter transformer. To ensure the reliable operation of the OLTC and to promptly detect mechanical faults in OLTCs to prevent them from developing into electrical faults, this paper proposes a fault diagnosis method for OLTCs based on a combination of Particle Swarm Optimization (PSO) algorithm and Least Squares Support Vector Machine (LSSVM) with multi-feature fusion. Firstly, a multi-feature extraction method based on time/frequency domain statistics, synchrosqueezed wavelet transform, singular value decomposition, and multi-scale modal decomposition is proposed. Meanwhile, the random forest algorithm is used to screen features to eliminate the influence of redundant features on the accuracy of fault diagnosis. Secondly, the PSO algorithm is introduced to optimize the hyperparameters of LSSVM to obtain optimal parameters, thereby constructing an optimal LSSVM fault diagnosis model. Finally, different types of feature combinations are utilized for fault diagnosis, and the impact of these feature combinations on the fault diagnosis results is compared. Experimental results indicate that features of different types can complement each other, making the OLTC state information carried by multi-dimensional features more comprehensive, which helps to improve the accuracy of fault diagnosis. Compared with four traditional fault diagnosis methods, the proposed method performs better in fault diagnosis accuracy, achieving the highest accuracy of 98.58%, which can help to detect mechanical faults in the OLTC early and reduce the system’s downtime.

A hybrid, nonlinear programming approach for optimizing passive shimming in MRI.

In magnetic resonance imaging (MRI), maintaining a highly uniform main magnetic field (B0) is essential for producing detailed images of human anatomy. Passive shimming (PS) is a technique used to enhance B0 uniformity by strategically arranging shimming iron pieces inside the magnet bore. Traditionally, PS optimization has been implemented using linear programming (LP), posing challenges in balancing field quality with the quantity of iron used for shimming. In this work, we aimed to improve the efficacy of passive shimming that has the advantages of balancing field quality, iron usage, and harmonics in an optimal manner and leads to a smoother field profile. This study introduces a hybrid algorithm that combines particle swarm optimization with sequential quadratic programming (PSO-SQP) to enhance shimming performance. Additionally, a regularization method is employed to reduce the iron pieces' weight effectively. The simulation study demonstrated that the magnetic field was improved from 462 to 3.6ppm, utilizing merely 1.2kg of iron in a 40cm diameter spherical volume (DSV) of a 7T MRI magnet. Compared to traditional optimization techniques, this method notably enhanced magnetic field uniformity by 96.7% and reduced the iron weight requirement by 81.8%. The results indicated that the proposed method is expected to be effective for passive shimming.

Path planning algorithm design using particle swarms optimization and artificial potential fields

AbstractOne of the most important challenges in an autonomous and robotics system is the path planning in which the system finds the optimal path from start point to goal point. The traditional path planning algorithms may have large memory requirements which scale with the size and resolution of the configuration space. To address these challenges, this paper introduces a novel path planning algorithm that combines Particle Swarm Optimization and Artificial Potential Field in the form of a path planning algorithm for mobile robots. The biological and physical concepts from Particle Swarm Optimization and Artificial Potential Field algorithms are combined to yield an algorithm which minimizes instances of getting stuck in local minima and generates a smooth but feasible path. The developed method requires memory which scales only with the number of particles and the time taken to reach the goal. This results in a memory‐efficient solution that generates smooth and feasible paths for mobile robots navigating in a 2D space.

Parameter analysis and prediction of crack propagation for thermoplastic composite pipes under lateral pressure

ABSTRACT The research investigates the process of crack propagation in thermoplastic composite pipes (TCPs) with initial racks subjected to lateral pressure, through a combination of numerical simulations and experimental studies. In the numerical analysis, a TCP is subjected to lateral pressure, considering parameters such as initial crack length, initial crack angle, and lateral displacement. This analysis employs the extended finite element method (XFEM). The results reveal that crack propagation predominantly occurs perpendicular to the fibre winding angle. Moreover, it is observed that longer initial cracks result in lower crack initiation loads, while the final extent of crack propagation increases with the initial crack length. The experimental study considers three distinct initial crack angles and verifies the conclusions drawn from the numerical simulations. Furthermore, a crack propagation prediction model is developed that combines particle swarm optimization (PSO) with backpropagation (BP) neural networks. The predictive results demonstrate a high level of accuracy.

Computational design of residential units’ floor layout: A heuristic algorithm

Computer-aided intelligent design can improve the quality and efficiency of residential building design. However, the design conditions are diverse, and numerous combinations of spatial elements exist. We propose a heuristic algorithm—the combined Monte Carlo tree search (MCTS) and particle swarm optimization (PSO) algorithm (PSO-MCTS)—for the automated design of residential floor layouts. The MCTS algorithm deals with discrete variables (room locations). It considers architectural design experience and design standards during node pruning to compress the search space and simplify the computation. The PSO algorithm deals with continuous variables (room sizes) and accelerates the computing speed due to parallel processing. The PSO-MCTS algorithm achieves a good balance between computational efficiency and accuracy. It is suitable for various design conditions and requirements. The input parameters include the daylight façade, boundary scope, entrance location, room type and size, and relationships with adjacent rooms. The PSO-MCTS provides 100 %, 80 %, and 75 % better results than heuristic algorithms, designs by architects, and a state-of-the-art deep learning model, respectively.

Dual-loop control and state prediction analysis of QUAV trajectory tracking based on biological swarm intelligent optimization algorithm

Quadrotor unmanned aerial vehicles (QUAVs) have attracted significant research focus due to their outstanding Vertical Take-Off and Landing (VTOL) capabilities. This research addresses the challenge of maintaining precise trajectory tracking in QUAV systems when faced with external disturbances by introducing a robust, two-tier control system based on sliding mode technology. For position control, this approach utilizes a virtual sliding mode control signal to enhance tracking precision and includes adaptive mechanisms to adjust for changes in mass and external disruptions. In controlling the attitude subsystem, the method employs a sliding mode control framework that secures system stability and compliance with intermediate commands, eliminating the reliance on precise models of the inertia matrix. Furthermore, this study incorporates a deep learning approach that combines Particle Swarm Optimization (PSO) with the Long Short-Term Memory (LSTM) network to foresee and mitigate trajectory tracking errors, thereby significantly enhancing the reliability and safety of mission operations. The robustness and effectiveness of this innovative control strategy are validated through comprehensive numerical simulations.

Piecewise combinatorial particle swarm optimization

The particle swarm optimization (PSO) is easy to fall into local optima in the search process, and usually the optimum stops updating at the later stage of the iteration, especially for complex problems. In this paper, a novel piecewise combinatorial particle swarm optimization is proposed, where a new piecewise combinatorial strategy and a new exploitation space search strategy are designed. The piecewise combinatorial strategy increases population diversity through copying, crossover, and mutation operations. The exploitation space search strategy improves particles search speed by setting the exploitation particle. This algorithm is tested and compared with 11 popular PSO variants on 30 benchmark functions. The results show that the performance of this algorithm is better than many existing PSO variants, especially for complex optimization problems. In addition, the piecewise combinatorial strategy provides a new idea for integrating excellent algorithms with different focuses. In the end, the algorithm is applied to a traveling salesman problem, and it is found that the algorithm exhibits excellent performance.

Development of laminar burning velocity prediction model and correlation of iso-octane air mixtures using artificial neural network

Surrogate fuels provide an economical alternative for forecasting the combustion properties of transportation fuels like diesel, gasoline, and kerosene. Iso-octane (2,2,4-trimethylpentane), a primary reference fuel for gasoline, is extensively used as a surrogate component. This study utilizes a Feed-Forward Artificial Neural Network (FFANN) with back-propagation (BP) to predict the laminar burning velocity (LBV) of iso-octane/air mixtures. The ANN model was developed using a dataset of 6339 data points, including 3788 experimental data points from literature since 2004 and 2551 simulation data points from reduced kinetic reaction mechanism (RKM) CHEMKIN simulations. The grid search cross-validation (CV) method was employed to optimize the ANN hyperparameters. Implemented in Python using Keras, the ANN model addresses research gaps by forecasting LBV for hydrocarbons beyond n-heptane and modelling LBV under conditions reflective of actual engine operations. Unlike prior studies, we optimized various ML models, their complexity, size, and hyperparameters to achieve high prediction accuracy. We also integrated a hybrid model combining Particle Swarm Optimization (PSO) with Genetic Algorithms (GA) for hyperparameter optimization, ensuring efficient configuration of the ANN. The constructed ANN model was compared to other ML models, including generalized linear regression (GLR), support vector machine (SVM), random forest (RF), and XGBoost regression. For the testing set (20 % of the total dataset), the ANN model outperformed all other ML models and empirical correlations, achieving a coefficient of determination (R2) of 0.9903, root mean square error (RMSE) of 1.911, and mean absolute error (MAE) of 1.206. Optimizing the ANN model with PSO further enhanced prediction accuracy, achieving a correlation coefficient of 0.9973 and a reduced mean absolute error of 0.753. To evaluate the ANN model's predictive ability, a reduced mechanism from the Lawrence Livermore National Laboratory (LLNL) was used. Compared to the LLNL RKM, the ANN predicted values showed lower percentage deviation from experimental data. Additionally, computation cost comparisons revealed that while RKM simulations in CHEMKIN took 244.2 s per case, the PSO-optimized ANN model required only 900 s for 150 cases. A 16-term correlation for laminar burning velocity (LBV) was derived from ANN predictions, serving as a practical tool to predict LBV based on pressure, temperature, and equivalence ratio. Unlike existing literature correlations, this newly developed correlation is valid across a wide range of operating conditions, offering a fundamental database for real-time combustion applications.

Synergistic Application of Particle Swarm Optimization and Gravitational Search Algorithm for Solar PV Performance Improvement

This study aims to optimize photovoltaic systems by developing a novel hybrid metaheuristic approach for maximum power point tracking (MPPT). The proposed method eclectically combines particle swarm optimization (PSO) and gravitational search algorithm (GSA) to overcome individual limitations and leverage complementary strengths. PSO, while surpassing in exploration, may suffer from premature convergence. GSA demonstrates strong exploitation capabilities but can struggle with slow convergence. A simulation model is developed to evaluate the hybrid algorithm’s performance in optimizing PV systems’ duty cycle. The approach utilizes the exploitation capabilities of PSO and GSA to navigate the search space effectively. Results demonstrate that the hybrid algorithm outperforms traditional techniques and standalone metaheuristics, achieving improved convergence time, faster settling time, and enhanced MPPT tracking efficiency. Under varying irradiance conditions, the proposed method consistently delivers higher power generation and improved overall PV system efficiency, offering a promising solution for optimizing PV systems and maximizing energy generation.

R32-Al5W: A new stable high-temperature alloy

Due to the high melting point, exceptional mechanical properties, and remarkable corrosion resistance exhibited by Al5W, researchers have conducted extensive research on the structural characteristics of Al5W. To identify the lowest energy structures and find new structures, a combination of particle swarm optimization and first-principles calculations was employed to identify energy-favorable Al5W structures under ambient pressure. The experimental P63 phase and the previously predicted R3¯c phase were identified. Notably, a new R32-Al5W phase was proposed. A comparative analysis on the structural stability, elastic, thermodynamic, and electronic properties of these three Al5W phases were conducted. The results indicate that R32-Al5W exhibits favorable thermodynamic stability, with a predicted bulk modulus close to that of R3¯c-Al5W. All the three Al5W phases demonstrate brittle behavior. The P63-Al5W phase displays larger shear anisotropy in the (010) and (001) planes, while the R32-Al5W phase shows greater shear anisotropy on the (100) plane compared to other two phases. Furthermore, employing Clarke’s and Cahill’s models, the minimum thermal conductivity of Al5W phases was predicted, revealing a magnitude order of P63 >R3¯c > R32. Our calculated melting points also conform to this trend.

Research on proportional valve spool wear diagnostic method hybrid-driven by the valve port energy loss mechanism model and data

High performance proportional valves are commonly utilized for precise control of aircraft actuators to ensure flight safety. Fault diagnosis play a crucial role in maintaining equipment reliability. However, traditional diagnostic methods using vibration signals face challenges such as inability to directly measure failure points, fault characteristics being influenced by sensor position, and large data processing volume, limiting engineering application scope. This paper introduces a valve spool wear fault diagnostic method based on energy loss model and data hybrid drive, leveraging throttling loss characteristics. An energy loss failure mechanism model, incorporating differential pressure and flow rate of valve port, was established, and experimentally verified. This method effectively addresses the limitations of vibration sensors in diagnosing aviation hydraulic systems. By combining particle swarm optimization algorithm with deep extreme learning machine, the number of neurons, weight distribution, and data set ratio are rapidly optimized, reducing data processing complexity and enhancing diagnostic efficiency. Comparative tests demonstrate a significant increase in average diagnostic accuracy rate, with more than 8% improvement after integrating energy loss information and particle swarm optimization, reaching over 98%. The proposed method exhibits superior performance in average diagnostic accuracy rate and stability compared to other methods and can be served as a valuable reference for predicting faults in aviation hydraulic valves.

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

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

Wear parameters identification of hydrodynamic bearing based on operational modal analysis and on-rotor sensing technology

Abrasive wear is a predominant fault in hydrodynamic bearings that can deteriorate bearing performance and damage the rotating machine eventually. The present work proposes a novel wear parameters identification method for hydrodynamic bearings based on operational modal analysis (OMA) and on-rotor sensing (ORS) technology. With this framework, the objective function is defined as the sum of squared errors between the theoretical modal frequencies and the identified modal frequencies. For the experiment verification, the ORS technology is used to collect rotor vibration directly. Additionally, the cepstrum editing procedure (CEP) is applied to enhance modal information. The different wear parameters are successfully identified using combined stochastic subspace identification (SSI) and particle swarm optimization (PSO) algorithms in both simulation and experimental signals.

Fatigue-resistant design of carbon/epoxy composites based on a failure tensor polynomial model by particle swarm optimization-sequential quadratic programming algorithm

This article introduces a design procedure to find the optimum fiber orientations of carbon/epoxy composite laminates for fatigue life advancement. The approach incorporates a fatigue failure tensor polynomial model and employs a hybrid algorithm, combining particle swarm optimization and sequential quadratic programming. Firstly, material properties of quasi-static and fatigue of the carbon/epoxy composites, fabricated by the vacuum-assisted resin transfer molding method, were determined to be used in the model. Various design problems involving two optimization scenarios were then solved using the hybrid algorithm. The algorithm’s performance was also evaluated by specific test problems, confirming its speed and robustness. The optimally fiber-oriented carbon/epoxy composite laminates having maximum fatigue lives were obtained for many critical in-plane cyclic loading cases. To validate the proposed design procedure, two optimum designs were experimentally verified under uniaxial loading conditions. The results indicated a good correlation between the estimated fatigue life of the optimally designed laminates and experimental data. This methodology offers a promising approach for the design of carbon/epoxy composite laminates with superior fatigue strength, particularly significant in specific industrial applications.

Sensorless fuzzy control algorithm for permanent magnet synchronous motor based on particle swarm optimization parameter identification and harmonic extraction

ABSTRACT In sensorless control of permanent magnet synchronous motor (IPMSM) with sliding mode observer, the problem of parameter robustness and poor stability seriously affects the control effect of sensorless operation. Therefore, a fuzzy sliding mode observer (SMO) phaselocked loop (PLL) combining particle swarm optimization (PSO) parameter identification and recursive least squares adaptive linear (RLS-Adaline) harmonic extractor is proposed. First, the fuzzy controller is used to process the control parameters of the sliding mode observer and the phaselocked loop. Secondly, the RLS-Adaline harmonic extractor is used to effectively filter the higher harmonic component of the back electromotive force (EMF), thus significantly improving the sensorless control effect. Thirdly, the PSO algorithm is used to identify the parameters of the motor, and the effect of parameter identification is judged by the performance index. The experimental results show that the proposed control method can effectively reduce the speed error and rotor position error.

Research on Deformation Prediction of VMD-GRU Deep Foundation Pit Based on PSO Optimization Parameters.

As a key guarantee and cornerstone of building quality, the importance of deformation prediction for deep foundation pits cannot be ignored. However, the deformation data of deep foundation pits have the characteristics of nonlinearity and instability, which will increase the difficulty of deformation prediction. In response to this characteristic and the difficulty of traditional deformation prediction methods to excavate the correlation between data of different time spans, the advantages of variational mode decomposition (VMD) in processing non-stationary series and a gated cycle unit (GRU) in processing complex time series data are considered. A predictive model combining particle swarm optimization (PSO), variational mode decomposition, and a gated cyclic unit is proposed. Firstly, the VMD optimized by the PSO algorithm was used to decompose the original data and obtain the Internet Message Format (IMF). Secondly, the GRU model optimized by PSO was used to predict each IMF. Finally, the predicted value of each component was summed with equal weight to obtain the final predicted value. The case study results show that the average absolute errors of the PSO-GRU prediction model on the original sequence, EMD decomposition, and VMD decomposition data are 0.502 mm, 0.462 mm, and 0.127 mm, respectively. Compared with the prediction mean square errors of the LSTM, GRU, and PSO-LSTM prediction models, the PSO-GRU on the PTB0 data of VMD decomposition decreased by 62.76%, 75.99%, and 53.14%, respectively. The PTB04 data decreased by 70%, 85.17%, and 69.36%, respectively. In addition, compared to the PSO-LSTM model, it decreased by 8.57% in terms of the model time. When the prediction step size increased from three stages to five stages, the mean errors of the four prediction models on the original data, EMD decomposed data, and VMD decomposed data increased by 28.17%, 3.44%, and 14.24%, respectively. The data decomposed by VMD are more conducive to model prediction and can effectively improve the accuracy of model prediction. An increase in the prediction step size will reduce the accuracy of the deformation prediction. The PSO-VMD-GRU model constructed has the advantages of reliable accuracy and a wide application range, and can effectively guide the construction of foundation pit engineering.

PSO BASED DEEP BELIEF NETWORKS LEARNING FOR IOT BASED CROP DISEASE DETECTION ON PADDY LEAVES USING CLOUD

The Internet of Things (IoT) with advanced machine learning techniques presents significant potential for agricultural applications, particularly in the domain of crop disease detection. Paddy, a staple food for millions, is highly susceptible to various diseases that can drastically affect yield and quality. Early and accurate disease detection is crucial for effective management and mitigation. Traditional methods are often labor-intensive and less reliable, underscoring the need for automated, accurate, and scalable solutions. The primary challenge lies in developing a robust system capable of accurately identifying diseases in paddy leaves using IoT-collected data. This task is complicated by the variability in disease manifestation and environmental conditions, which can affect the quality and consistency of the collected data. Efficient feature extraction and classification techniques are essential to address these issues and ensure high accuracy. This study proposes a novel approach combining Particle Swarm Optimization (PSO) for feature extraction with Deep Belief Networks (DBNs) for classification. IoT devices capture high-resolution images of paddy leaves, which are then processed in the cloud. PSO is employed to optimize the feature extraction process by selecting the most relevant features from the image data. These optimized features are fed into a DBN, which is trained to classify the images into healthy or diseased categories. The use of cloud computing ensures the scalability and computational efficiency of the system. The proposed method demonstrates significant improvements in accuracy and processing speed. The PSO-based feature extraction enhances the relevance of features, reducing the dimensionality and improving the DBN''s performance. Experimental results show an accuracy rate of 96.3%, with a reduction in processing time by 35% compared to traditional methods. The system''s precision and recall rates are 95.8% and 94.7%, respectively, highlighting its effectiveness in real-world applications.

Evalution of neurodiagnostic insights for enhanced evaluation and optimization of badminton players' physical function via data mining technique

This research presents a novel method for objectively evaluating college badminton players' physical function levels. It examines current evaluation methods before proposing a novel model that combines Particle Swarm Optimization (PSO) with Backpropagation (BP) neural networks and data mining. The model establishes an evaluation index system that considers physical form, function, quality, and neural mechanisms. The study uses PSO-BP neural networks to adjust indicator weights for more accurate ratings. This recurrent improvement reduces errors while increasing prediction ability, resulting in accurate assessments of athletes' physical talents and neurological insights. The model's efficiency is proved by low mistakes and high accuracy results, which are critical for training optimization and injury avoidance. The combination of PSO optimization and BP neural networks offers robustness across various athlete profiles and training scenarios. This method improves physical function evaluation in badminton and has wider implications for sports science and performance analytics. This study uses bio-inspired computing and machine learning to emphasize the relevance of data-driven techniques in enhancing athlete assessments for better training outcomes and general well-being.