Combining Particle Swarm Optimization Research Articles

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.

Groundwater level prediction using an improved ELM model integrated with hybrid particle swarm optimisation and grey wolf optimisation

Groundwater, the world's most abundant source of freshwater, is rapidly depleting in many regions due to a variety of factors. Groundwater resources (GWR) play a crucial role in daily life, economic progress, and agricultural crop production, which makes groundwater level (GWL) prediction much more significant for enhanced management. However, one of the major challenges in investigating and estimating groundwater reduction and managing its resources is the absence of complete and reliable data. Therefore, the application of artificial intelligence (AI) models with the necessity for less data and better prediction accuracy is inevitable. The performances of hybrid models including the combination of particle swarm optimisation and grey wolf optimisation with extreme learning machine (ELM-PSOGWO) in estimating the GWL are investigated and evaluated against ELM-GWO, ELM-PSO, ELM, Twin-support vector regression (T-SVR) and SVR models based on the coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), and Nash-Sutclife efficiency coefficient (NSC). Outcomes of this study showed that applied hybridised ELM-PSOGWO (R2 = 0.9969, RMSE = 0.0088, NSC = 0.9961, MAE = 0.0047) performed best, followed by ELM-GWO, ELM-PSO, ELM, TSVR, and SVR in predicting GWL. Based on the obtained results, it is evident that the ELM-PSOGWO model illustrates an excellent agreement with observed GWL data. To this end, this work reveals that hybrid optimisation algorithms can enhance performance of ELM model significantly in GWL prediction. Utilization of advanced AI techniques can well estimate groundwater systems, which saves resources and labor, employed conventionally.

Multi-Strategy Improved Particle Swarm Optimization Algorithm and Gazelle Optimization Algorithm and Application

In addressing the challenges associated with low convergence accuracy and unstable optimization results in the original gazelle optimization algorithm (GOA), this paper proposes a novel approach incorporating chaos mapping termed multi-strategy particle swarm optimization with gazelle optimization algorithm (MPSOGOA). In the population initialization stage, segmented mapping is integrated to generate a uniformly distributed high-quality population which enhances diversity, and global perturbation of the population is added to improve the convergence speed in the early iteration and the convergence accuracy in the late iteration. By combining particle swarm optimization (PSO) and GOA, the algorithm leverages individual experiences of gazelles, which improves convergence accuracy and stability. Tested on 35 benchmark functions, MPSOGOA demonstrates superior performance in convergence accuracy and stability through Friedman tests and Wilcoxon signed-rank tests, surpassing other metaheuristic algorithms. Applied to engineering optimization problems, including constrained implementations, MPSOGOA exhibits excellent optimization performance.

Prediction of the Permeability Tensor of Marine Clayey Sediment during Cyclic Loading and Unloading of Confinement Pressure Using Physical Tests and Machine Learning Techniques

In this study, a method was introduced to validate the presence of a Representative Elementary Volume (REV) within marine clayey sediment containing cracks during cyclic loading and unloading of confinement pressure. Physical testing provided the basis for this verification. Once the existence of the REV for such sediment was confirmed, we established a machine-learning predictive model. This model utilizes a hybrid algorithm combining Particle Swarm Optimization (PSO) with a Support Vector Machine (SVM). The model was trained using a database generated from the aforementioned physical tests. The machine-learning model demonstrates favorable predictive performance based on several statistical metrics, including the coefficient of determination (R2), mean residual error (MSE), mean relative residual error (MRSE), and the correlation coefficient R during the verification process. Utilizing the established machine-learning predictive model, one can effortlessly obtain the permeability tensor of marine clayey sediment containing cracks during cyclic loading and unloading of confinement pressure by inputting the relevant stress condition parameters. The original research cannot estimate the permeability tensor under similar loading and unloading conditions through REV. In this study, the physical model test was used to determine the REV of marine cohesive sediments with cracks by cyclic-constrained pressure loading and unloading. Referring to the results of physical tests, we developed a machine-learning prediction model that can easily estimate the permeability tensor of marine cohesive sediments with cracks under cyclic loading and constrained pressure unloading conditions. This method greatly saves time and computation and provides a direct method for engineering and technical personnel to predict the permeability tensor in this case.

Biochar-enhanced concrete mixes: Pioneering multi-objective optimization

The incorporation of biochar (BC) as a partial substitute for cement in concrete formulations provides a promising pathway for mitigating the environmental impacts associated with carbon dioxide emissions during cement production. To achieve optimal composition of BC-enhanced concrete, multiple objectives including mechanical strength, economic factors, and embodied CO2 must be balanced while considering a multitude of variables constrained by complex non-linear relationships. Here, we introduced an intelligent hybrid optimization algorithm that combines Particle Swarm Optimization (PSO), Least Squares Support Vector Machine (LSSVM), and Non-dominated Sorting Genetic Algorithm II (NSGAII) to predict the performance of BC-enhanced concrete and optimize its mix proportions for multiple objectives. Our results demonstrate that the PSO optimization algorithm for searching LSSVM hyper parameters outperforms other optimization algorithms, exhibiting higher generalization performance and improved overall accuracy (R2 = 0.95). Furthermore, the proposed model framework effectively presents a complete Pareto front for the BC-enhanced concrete mix ratio. This triangular model system for concrete mix ratio can determine the optimal solution, tailored to the preferences of the owner's unit. Ultimately, PSO-LSSVM-NSGAII intelligent hybrid optimization algorithm enhances the efficiency of mix proportion design for BC-enhanced concrete.

Collaborative Task Allocation and Optimization Solution for Unmanned Aerial Vehicles in Search and Rescue

Earthquakes pose significant risks to national stability, endangering lives and causing substantial economic damage. This study tackles the urgent need for efficient post-earthquake relief in search and rescue (SAR) scenarios by proposing a multi-UAV cooperative rescue task allocation model. With consideration the unique requirements of post-earthquake rescue missions, the model aims to minimize the number of UAVs deployed, reduce rescue costs, and shorten the duration of rescue operations. We propose an innovative hybrid algorithm combining particle swarm optimization (PSO) and grey wolf optimizer (GWO), called the PSOGWO algorithm, to achieve the objectives of the model. This algorithm is enhanced by various strategies, including interval transformation, nonlinear convergence factor, individual update strategy, and dynamic weighting rules. A practical case study illustrates the use of our model and algorithm in reality and validates its effectiveness by comparing it to PSO and GWO. Moreover, a sensitivity analysis on UAV capacity highlights its impact on the overall rescue time and cost. The research results contribute to the advancement of vehicle-routing problem (VRP) models and algorithms for post-earthquake relief in SAR. Furthermore, it provides optimized relief distribution strategies for rescue decision-makers, thereby improving the efficiency and effectiveness of SAR operations.

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.

Profit-effective component sizing for electric delivery trucks with dual motor coupling powertrain

This study proposes a novel component sizing method for electric delivery trucks (EDTs) employing dual motor coupling powertrain (DMCP) to enhance both the energy efficiency and operating profitability. A control-oriented model for the EDT is first established, encompassing the three-mode DMCP dynamics. Variations in component size and mass have been modeled, with consideration of their effects on the load capacity. To maximize the average profit per kilometer over the truck’s lifespan, four objective functions are defined to accommodate to the diverse types of cargo being transported. We formulate the optimization problem in a bi-level form, and propose a solution method that combines particle swarm optimization (PSO) handling parameter filtering with iterative dynamic programming (IDP) to minimize energy consumption. Three real-world delivery tests show that component sizing leads to an increase in the average profit per kilometer by 2.62%–8.10%. Upon evaluating the impact of powertrain and battery mass/volume on cargo capacity, the battery pricing ceases to impact the sizing of components. However, the electricity price and freight significantly influence the optimal size of components. Moreover, a sensitivity analysis focusing on market price factors underscores the importance of component sizing for maximizing profit, particularly in scenarios where freight costs fluctuate in commercial settings.

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.

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.

Developing two robust hybrid models for predicting tunnel deformation in squeezing prone grounds

The ground deformation under squeezing conditions has a considerable impact on the tunnel excavation process. In this study, we utilize a combination of particle swarm optimization (PSO) and the Entropy Weight Method (EWM) optimization algorithms to optimize the hyperparameters of Extreme Gradient Boosting (XGBoost) and Random Forest (RF) algorithms. This optimization process aims to accurately predict tunnel deformation in squeezing prone grounds, utilizing a dataset derived from 263 tunnel case histories. In the initial step, the database underwent processing utilizing the DBSCAN method to eliminate outlier data. Subsequently, XGBoost and RF algorithms were applied within the Python environment. The optimal hyperparameters for each model were then determined through two methods: PSO and EWM. The overburden depth (H), tunnel diameter (D), support stiffness (K), and rock mass quality index (Q) serve as inputs for the tunnel deformation estimation models. The performance evaluation of the developed models, based on 10-fold cross-validation, using three statistical indices (Normalized Root Mean Square Error/NRMSE, Coefficient of Determination/R2, and Variance Accounted For/VAF), shows that the proposed XGBoost-PSO model accurately predicts tunnel deformation with acceptable performance (R2 = 0.922, NRMSE = 0.344, and VAF = 91.675). Moreover, the results regarding the importance of the input parameters suggest that the rock mass quality index (Q) holds the most significant influence, while the tunnel diameter (D) exhibits the least impact on tunnel deformation in squeezing prone grounds. This study can be a reference for predicting tunnel deformation in the squeezing prone grounds, as well as estimating the severity of the squeezing phenomenon based on deformation. It aids in estimating project costs, selecting an appropriate support system, and determining the necessary methods to mitigate the risk of squeezing.

Enhancing Camera Calibration for Traffic Surveillance with an Integrated Approach of Genetic Algorithm and Particle Swarm Optimization.

Recent advancements in sensor technologies, coupled with signal processing and machine learning, have enabled real-time traffic control systems to effectively adapt to changing traffic conditions. Cameras, as sensors, offer a cost-effective means to determine the number, location, type, and speed of vehicles, aiding decision-making at traffic intersections. However, the effective use of cameras for traffic surveillance requires proper calibration. This paper proposes a new optimization-based method for camera calibration. In this approach, initial calibration parameters are established using the Direct Linear Transformation (DLT) method. Then, optimization algorithms are applied to further refine the calibration parameters for the correction of nonlinear lens distortions. A significant enhancement in the optimization process is achieved through the integration of the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) into a combined Integrated GA and PSO (IGAPSO) technique. The effectiveness of this method is demonstrated through the calibration of eleven roadside cameras at three different intersections. The experimental results show that when compared to the baseline DLT method, the vehicle localization error is reduced by 22.30% with GA, 22.31% with PSO, and 25.51% with IGAPSO.

Real-time video surveillance on highways using combination of extended Kalman Filter and deep reinforcement learning

Highways, as one of the main arteries of transit and transportation in today's world, play a fundamental role in accelerating transportation, and for this reason, continuous monitoring of them is of great importance. Among these, monitoring compliance with transportation laws by vehicles is of utmost importance; for automation, efficient and vehicle-specific models can be used. In this article, a new method for video surveillance of highways is presented using an extended Kalman filter (EKF) and reinforcement learning models. There are three primary stages to the suggested approach. During the first stage, the extended Kalman filter (EKF) is used to identify and track multiple targets. Next, in the second stage, a convolutional neural network (CNN) processes each detected moving item to determine the kind of vehicle. During this stage, the CNN model's ideal configuration is ascertained using a new optimization approach that combines Particle Swarm Optimization (PSO) and reinforcement learning. After identifying the type of vehicle, in the third phase, the proposed method uses a separate CNN model for each target vehicle to assess its compliance with transportation safety principles. It should be mentioned that each vehicle's associated CNN model is configured during this phase using the suggested optimization methodology. Investigations have been conducted into the effectiveness of the suggested method in identifying violations of road safety laws as well as how well it performed in the two phases of vehicle type identification. According to the findings, the suggested approach can identify the kind of vehicle with 98.72% accuracy, which is at least 3.41% better than the approaches that were compared. On the other hand, this model can detect the violation of road safety laws for each vehicle with an average accuracy of 91.5%, which shows at least a 3.49% improvement compared to the other methods.

An efficient ECG signals denoising technique based on the combination of particle swarm optimisation and wavelet transform

During the recording time, electrocardiogram (ECG) signals are subject to multiple artefact noises, such as muscle activity, white Gaussian noise (WGN), baseline wander, and power line interference (PLI). Therefore, pre-processing of ECG signals is essential to eliminate these artefacts and to obtain efficient ECG features. Many approaches have been proposed for removing ECG noises, including ECG signal denoising using wavelet transform (WT). However, the effectiveness and performance of the WT technique are strongly related to the configuration of its control parameters, which are typically fine-tuned through a laborious and time-consuming series of experiments. This paper introduces a technique that combines particle swarm optimisation (PSO) with WT for ECG signal denoising. The key contribution of this research is the use of PSO to determine the optimum settings for all WT parameters for ECG signal denoising (type of wavelet basis function Φ, thresholding function β, level of decomposition L, rule for threshold selection λ, and rescaling method ρ). The efficiency of the proposed method is evaluated using the percentage root mean square difference (PRD) and the signal-to-noise ratio (SNR), employing various ECG signals available online from the MIT-BIH Arrhythmia database. Experimental results show that the proposed PSO-WT technique yields better results than state-of-the-art techniques in terms of SNR, particularly for PLI measured at 60 Hz and still acceptable at 50 Hz. For example, a denoised ECG signal resulting from the proposed technique at an SNR input of 10 dB corresponds to an SNRoutput of 27.47 dB at 60 Hz, improving the quality of the denoised ECG signal and making it more appropriate for clinical diagnosis. Furthermore, the proposed method also shows promising efficiency in the presence of WGN, making it highly relevant for IoT applications and RF transmission.

An Autonomous Sleep-Stage Detection Technique in Disruptive Technology Environment.

Autonomous sleep tracking at home has become inevitable in today's fast-paced world. A crucial aspect of addressing sleep-related issues involves accurately classifying sleep stages. This paper introduces a novel approach PSO-XGBoost, combining particle swarm optimisation (PSO) with extreme gradient boosting (XGBoost) to enhance the XGBoost model's performance. Our model achieves improved overall accuracy and faster convergence by leveraging PSO to fine-tune hyperparameters. Our proposed model utilises features extracted from EEG signals, spanning time, frequency, and time-frequency domains. We employed the Pz-oz signal dataset from the sleep-EDF expanded repository for experimentation. Our model achieves impressive metrics through stratified-K-fold validation on ten selected subjects: 95.4% accuracy, 95.4% F1-score, 95.4% precision, and 94.3% recall. The experiment results demonstrate the effectiveness of our technique, showcasing an average accuracy of 95%, outperforming traditional machine learning classifications. The findings revealed that the feature-shifting approach supplements the classification outcome by 3 to 4 per cent. Moreover, our findings suggest that prefrontal EEG derivations are ideal options and could open up exciting possibilities for using wearable EEG devices in sleep monitoring. The ease of obtaining EEG signals with dry electrodes on the forehead enhances the feasibility of this application. Furthermore, the proposed method demonstrates computational efficiency and holds significant value for real-time sleep classification applications.

An improved optimization method combining particle swarm optimization and dimension reduction kriging surrogate model for high-dimensional optimization problems

An improved optimization method is proposed which combines the particle swarm optimization (PSO) algorithm with the dimension reduction kriging surrogate model (DK), namely PSO + DK. In this method, a surrogate model is dynamically constructed and updated between the lower dimensional principal components and the response, instead of between the high-dimensional design variables and the response as in traditional methods. The key point of DK is that the principal components analysis (PCA) is integrated with the active learning kriging surrogate model (AK). Since PCA can reduce the dimension effectively, DK makes surrogate model construction possible for complex high-dimensional optimization problems. Numerical examples and four classical engineering problems are presented to validate the effectiveness of the proposed method. The results show that the proposed method can decrease the computational cost significantly while guaranteeing the precision compared with PSO and PSO + AK for high-dimensional optimization problems.

Parametric analysis of wastewater electrolysis for green hydrogen production: A combined RSM, genetic algorithm, and particle swarm optimization approach

The production of green hydrogen from wastewater presents a significant opportunity to address environmental challenges associated with wastewater treatment and fulfill the increasing demand for renewable and sustainable energy sources. This study aimed to determine the optimal operating parameters for maximizing hydrogen production through wastewater electrolysis. To design the experimental runs, a Box-Behnken design (BBD) with an L17 array was implemented, while Response Surface Methodology (RSM) in conjunction with Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) was employed to develop predictive models and optimize the operating parameters. The operating parameters considered were the catalyst amount (g), electrode voltage (V), and electrolysis time (min), and the response variable was the oxyhydrogen (HHO) gas generation rate (L/min). The comparative analysis shows that the optimized parameters obtained through RSM surpassed those obtained through GA and PSO, with a catalyst amount of 99.42 g, an electrode voltage of 22.9 V, and an electrolysis time of 23.17 min. Consequently, the HHO generation rate reached a maximum value of 12.42 L/min. Furthermore, the experimental validation indicated a close agreement between the model's predicted results and the actual experimental outcomes. This study shows the effectiveness of combining RSM, GA, and PSO for accurate prediction and optimization of operating parameters in wastewater electrolysis for green hydrogen production. The identified optimal conditions contribute to enhanced efficiency and increased hydrogen yield, thereby promoting the advancement of sustainable energy systems.

Research on Pricing and Replenishment Strategies of Supermarkets Based on PSO-LSTM- Transformer Models

The supermarket's role in ensuring market supply and enhancing livelihoods prompted the implementation of a new PSO-LSTM-Transformer hybrid model in this study. This amalgamation combines Particle Swarm Optimization (PSO), Long Short-Term Memory (LSTM), and Transformer to forecast commodity prices and inventory needs. By combining these methods, the research aimed to satisfy market requirements while maximizing grocery store profits and improving market operations sustainability.Particle Swarm Optimization (PSO) enhances prediction accuracy by effectively exploring the vast solution space for optimal solutions. Long Short-Term Memory (LSTM), which is known for capturing long-term data dependencies, enhances the comprehension and forecasting of market trends. Furthermore, the Transformer model enhances the forecasting process by capturing intricate patterns and relationships in market data through its attention mechanism.The research concludes that the PSO-LSTM-Transformer model effectively predicts commodity pricing and replenishment needs, thereby aiding supermarkets in making informed decisions to balance market demands and optimize revenue. The findings of the study contribute to the promotion of forecasting methods within the supermarket sector, facilitating efficient management of the market and sustainable economic growth.

基于改进A*算法与粒子群算法的果蔬采摘机器人路径规划

Aiming at the suboptimal local path, slow convergence speed, and many inflection points in the path planning of fruit and vegetable picking robots in complex environments, a global planning method combining particle swarm optimization (PSO) algorithm and A* algorithm was proposed. Firstly, Manhattan distance was taken as a heuristic function of global programming based on the A* algorithm. Secondly, the step size of PSO was adjusted to optimize the path, shorten the path length, and reduce the number of inflection points. Finally, the planned path of the fruit and vegetable picking robot was smoothed so that it could steadily move along a smoother driving path in real scenarios. The experimental results show that compared with the traditional PSO algorithm, the hybrid algorithm based on the improved A* algorithm and PSO algorithm achieves a smoother path and fewer folding points. In comparison with the PSO algorithm, moreover, this algorithm can guarantee the path generation efficiency and the global optimum. In the end, the effectiveness of the proposed method was verified by shortening the path length and reducing the accumulative number of inflection points.