Chaotic Particle Swarm Optimization Research Articles

Economic Scheduling Model of an Active Distribution Network Based on Chaotic Particle Swarm Optimization

Economic Scheduling Model of an Active Distribution Network Based on Chaotic Particle Swarm Optimization

Synchronous Control of High-Speed Train Lift Wing Angle of Attack Drive System Based on Chaotic Particle Swarm Optimization and Linear Auto-Disturbance Resistant Controller

In this study, a control scheme is proposed based on Chaotic Particle Swarm Optimization (CPSO) to enhance the Linear Auto-Disturbance Rejection Controller (LADRC). The focus is on addressing the challenge of high-precision variations in angle-of-attack through dual-motor cooperative control within the lifting wing of a high-speed train. The scheme initiates with the design of a dual-loop structure for LADRC, integrating position and current control. The position loop is further refined. Subsequently, the CPSO algorithm is employed to optimize the parameters of the LADRC controller. Ultimately, the loop is closed by feeding back the position error in the cross-coupled structure to the current loop, thereby achieving high-precision control. The performance of the proposed structure is validated through both Matlab/Simulink simulations and an experimental platform. The experimental results demonstrate that CPSO-LADRC, in comparison to traditional LADRC and Proportion-Integration-Differentiation (PID) control, exhibits an increase in the maximum response time by 3.76 s and 3.3 s, respectively, a reduction in overshoot by 1.12% and 0.8%, as well as a decrease in the maximum synchronization error by 0.45 cm and 1 cm, respectively. These findings validate the effectiveness of the proposed synchronous loop controller method in simplifying computational complexity, enhancing system responsiveness, robustness, and synchronization performance. Additionally, our approach facilitates precise angle-of-attack transformation for the lifting wings of high-speed trains effectively.

Study of convergence speed of chaotic particle swarm optimization algorithm

AbstractThis study introduces the Chaotic Particle Swarm Optimization as an innovative variant of the traditional particle swarm optimization algorithm, addressing the issue of particle swarm optimization getting trapped in local minima with a low convergence characteristic during later iterations. Chaotic particle swarm optimization incorporates principles from chaos theory to enhance the swarm's exploration and exploitation capabilities. By introducing controlled chaotic behavior, particles exhibit more diverse and unpredictable movements in the search space, leading to improved global convergence and escape from local minima. The proposed method has been implemented and evaluated on benchmark problems to assess its effectiveness. The integration of chaos theory with particle swarm optimization offers promising opportunities for developing robust and efficient optimization techniques suitable for complex and dynamic problem domains in various real-world applications.

Validation study of Yunnan ethnic culture industry stock model under epidemic situation based on chaotic particle swarm optimization neural networks

The new crown pneumonia epidemic is raging, in the context of global integration, the scope of the impact of this sudden event spread around the world, the stock market has not been spared, the financial risk has increased dramatically compared with the past, the emergence of the epidemic has led to the spread of investor panic, March 2020, the U.S. S&P 500 index appeared in the four plunge, and led to the market trading meltdown, the world’s financial markets have had an extremely serious impact. The study of the impact of Xin Guan Pneumonia on the company’s stock returns is not only conducive to enriching the theoretical study of public health emergencies, but also conducive to improving the coping strategy, stabilizing the general economic market, and enhancing the public’s awareness of risk response. This paper compares the effect of the four intelligent algorithms of chaotic particle swarm algorithm, chaotic bee colony algorithm, chaotic fruit fly algorithm and chaotic ant colony algorithm combined with neural network on the prediction of the stock price trend of Yunnan national culture, and the study shows that the speed of convergence of the chaotic particle swarm optimization neural network and the speed of descent is better than that of the two models of chaotic fruit fly and chaotic bee colony, and the coefficients of decision of the chaotic particle swarm optimization neural network are higher than that of the other three models, and the errors are lower than the other three models. Indexes are lower than the other three models and have high accuracy in stock prediction of Yunnan ethnic culture, this finding emphasizes the potential of PSO-BP model to provide robust stock market prediction, which is important for both investors and policy makers in dealing with volatile market conditions.

Optimal Configuration of Multi-Energy Storage in an Electric–Thermal–Hydrogen Integrated Energy System Considering Extreme Disaster Scenarios

Extreme disasters have become increasingly common in recent years and pose significant dangers to the integrated energy system’s secure and dependable energy supply. As a vital part of an integrated energy system, the energy storage system can help with emergency rescue and recovery during major disasters. In addition, it can improve energy utilization rates and regulate fluctuations in renewable energy under normal conditions. In this study, the sizing scheme of multi-energy storage equipment in the electric–thermal–hydrogen integrated energy system is optimized; economic optimization in the regular operating scenario and resilience enhancement in extreme disaster scenarios are also considered. A refined model of multi-energy storage is constructed, and a two-layer capacity configuration optimization model is proposed. This model is further enhanced by the integration of a Markov two-state fault transmission model, which simulates equipment defects and improves system resilience. The optimization process is solved using the tabu chaotic quantum particle swarm optimization (TCQPSO) algorithm to provide reliable and accurate optimization results. The results indicate that addressing severe disaster situations in a capacity configuration fully leverages the reserve energy function of energy storage and enhances system resilience while maintaining economic efficiency; furthermore, adjusting the load loss penalty coefficients offers a more targeted approach to the balancing of the system economy and resilience. Thus, new algorithmic choices and planning strategies for future research on enhancing the resilience of integrated energy systems under extreme disaster scenarios are provided.

Enhancing personalized learning with explainable AI: A chaotic particle swarm optimization based decision support system

In the realm of Educational Technology, personalized learning is pivotal, yet predicting students' learning abilities based on learning styles and ICT remains challenging. We propose a decision support system using Machine Learning (ML), swarm intelligence, and explainable artificial (XAI) techniques to assess students' performance. Our model employs Chaotic Particle Swarm Optimization (C-PSO) with Henon execution, outperforming Genetic Algorithm (GA), Ant Colony Optimization (ACO), Firefly Algorithm (FA), Bee Colony Optimization (BCO), Artificial Fish Swarm Algorithm (AFSA), Mayfly Optimization Algorithm (MFOA), Mother Optimization Algorithm (MOA), Fuzzy Self-Tuning PSO (FST-PSO). Evaluating efficiency, effectiveness, and solution quality reveals C-PSO's superiority. The study identifies the significant impact of ICT on self-progress and employs Spearman Rank correlation for statistical validation. Findings suggest C-PSO as an effective tool for optimizing educational data analysis and decision-making. Further exploration in real-world educational settings and comparative analyses with alternative optimization techniques are recommended for future research.

Optimal Configuration of Wind–Solar–Thermal-Storage Power Energy Based on Dynamic Inertia Weight Chaotic Particle Swarm

The proposed approach involves a method of joint optimization configuration for wind–solar–thermal-storage (WSTS) power energy bases utilizing a dynamic inertia weight chaotic particle swarm optimization (DIWCPSO) algorithm. The power generated from the combination of wind and solar energy is analyzed quantitatively by using the average complementarity index (ACI) to determine the optimal ratio of wind and solar installations. We constructed a multi-objective optimization configuration model for the WSTS power generation systems, considering the equivalent annual income and the optimal energy consumption level as objective functions of the system. We solved the model using the chaotic particle swarm optimization algorithm with linearly decreasing dynamic inertia weight. To validate the effectiveness of the proposed approach, we conducted a simulation using the 2030 power energy base planning data of a particular region in Inner Mongolia. The results demonstrate that the proposed method significantly improves the annual income, enhances the consumption of wind–solar energy, and boosts the power transmission capacity of the system.

Investigation on wireless communication reliability planning in cloud computing mMTC scenarios

With the development of cloud computing technology, cloud computing has been applied in various fields, but there is a lack of research in the Massive Machine Type Communication (mMTC) scenario. This article analyzed wireless communication reliability solutions in cloud computing mMTC scenarios to address this issue. Using the method of cloud computing, this article analyzed the reliability of wireless communication schemes and verified the effectiveness of the algorithm through credibility judgment and algorithm convergence characteristics under the guidance of wireless communication schemes. By analyzing the application of cloud computing in wireless communication solutions, this article analyzed the effectiveness of wireless communication solutions in the experimental section by comparing the error rate, success rate, and convergence of various algorithms. Through experimental analysis, it was found that the implementation success rate of chaotic particle swarm optimization algorithm was 5% higher than that of adaptive particle swarm optimization. The results indicated that cloud computing was well applied in wireless communication networks, thus providing reference significance for the application of cloud computing in other communication fields.

Speed control of sensorless PMSM drive based on EKF optimized by variable scale chaotic particle swarm optimization

To investigate the parameter characteristics of permanent magnet synchronous motor (PMSM) speed sensorless vector control system and capture the noise matrices quickly and accurately in the speed estimation process of the extended Kalman filter for PMSM, The recursive least square method with forgetting factor is proposed to determine the actual parameters of the system, and then a new variable-scale chaotic particle swarm optimization (VCPSO) algorithm is put forward to accurately obtain the system noise matrix and the measurement noise matrix. The simulation results show that noise matrix optimization of extended Kalman filter by employing VCPSO algorithm under actual motor parameters is better than those employing standard PSO or chaotic PSO algorithms with faster speed and higher accuracy.

Fault Diagnosis of Wastewater Treatment Processes Based on CPSO-DKPCA

The wastewater treatment process (WWTP) is one of the most common links in chemical plants. However, the testing for diagnosing faults in wastewater treatment plants is expensive and time-consuming. Due to strong nonlinearity and variable autocorrelation, traditional WWTP diagnostic methods based on principal component analysis (PCA) can lead to low fault detection rates (FDR) or difficulty in determining the root cause of faults. In this paper, an improved dynamic kernel principal component analysis (DKPCA) and Granger causality (GC) analysis model that uses chaotic particle swarm optimization (CPSO) to detect WWTP and locate the root causes of faults is proposed. First, a kernel function is introduced to map a nonlinear matrix to a linear space. Then, the training data are extended through a time lag constant to solve the problem of nonlinear and variable autocorrelation in WWTP. Moreover, a novel fault candidate variables selection method, together with GC, is introduced to locate the root variables of the fault. The CPSO algorithm is employed to optimize DKPCA's kernel function parameters, enhancing the accuracy of fault monitoring and diagnosis models. Compared with traditional methods, the proposed method has a better fault detection rate, achieving 95.83% and 93.33% fault detection rates in simulated and real WWTP, respectively.

Ultra-short-term wind power probabilistic forecasting based on an evolutionary non-crossing multi-output quantile regression deep neural network

Ultra-short-term wind power probabilistic forecasting is of significance for stable power grid operation; however, it is still challenging due to the inherent nonlinearity and uncertainty. Most state-of-the-art methods have focused on achieving quantile prediction using a combination of linear quantile regression and nonlinear complex deep neural networks. Yet, these methods struggle with several dilemmas. Quantile regression deep neural networks require a complete training once for each quantile. The multi-training mode and complex structure of quantile regression deep neural network can lead to extremely high computational complexity. Most of the training of quantile regression deep neural networks are guided by the loss of each quantile, and the weights are adjusted by gradient descent in which the gradient explosion and quantile crossover may be encountered. Therefore, this paper proposes a non-crossing multi-output quantile regression deep neural network optimized by chaotic particle swarm optimization. It designs a multi-output deep neural network to output all quantile estimations simultaneously through one training, effectively solving the structural complexity problem of traditional quantile regression deep neural networks. Since quantile regression produces a non-differentiable loss function which significantly hinders model training, the proposed neural network is trained by chaotic particle swarm optimization. It not only achieves the effect of optimizing all quantile losses simultaneously, but also can significantly alleviate the dilemma of training in traditional neural network weight optimization. In addition, several non-crossing constraints are designed for avoiding quantile crossover. The proposed model is trained and tested on two real-world wind power case studies. The experiment results show that the proposed model shows superiority in performance criteria, training speed, and avoiding quantile crossover.

Variable Universe Fuzzy PID Control for Active Suspension System With Combination of Chaotic Particle Swarm Optimization and Road Recognition

Variable Universe Fuzzy PID Control for Active Suspension System With Combination of Chaotic Particle Swarm Optimization and Road Recognition

Multiple PAHs’ Detection under CDOM Interference Based on Excitation-Emission Matrix and Interval Selection

Fluorescence technology is an effective tool for detecting polycyclic aromatic hydrocarbons (PAHs) in water. However, the accuracy of fluorescence detection is reduced by the spectral overlap of different PAHs and coexisting colored dissolved organic matter (CDOM). In this study, a single-excitation interval selection method based on an excitation-emission matrix is proposed to quantify four PAHs: fluorene, pyrene, phenanthrene, and benzo(a)pyrene under CDOM interference. The optimal excitation wavelength for each PAH was obtained by stability analysis, based on which the optimal emission interval was obtained by chaotic particle swarm optimization. The partial least squares (PLS) prediction models of four PAHs under interference were established. On comparing with other modeling methods, the results show that the models with interval selection have better prediction accuracy (mean relative error < 10%) under CDOM interference. The recovery rate and limit of detection of the method were also evaluated. This study provides a new and helpful strategy for fluorescence detection of interfering PAHs in water.

Comparative analysis of meta heuristics algorithm for differential amplifier design

The design of analog circuits is very essential for the development of system design based on electronics as the world is analog in nature. Furthermore, performance of emerging products depends on analog circuits for reduction of power dissipation and improvement of speed. Though in the system on chip (SoC) analog circuit consumes less, it is more complex to design it due to the analog circuit nature complexity. The job of the evolutionary algorithm (EA) in the process of optimization of an analog circuit based on complementary metal oxide semiconductor (CMOS) is to get the appropriate values that can be used for the design parameters of the given circuit in such a way such that all the targeted specification of the given circuit can be obtained. The chaotic serial particle swarm optimization (CSPSO) and cuckoo search (CS) algorithms are lower than to those found through the particle swarm optimization (PSO) and differential evolution (DE) algorithms. The CS algorithm obtained all the desired specifications with less power dissipation and the least total transistor area compared to those attained by the DE and PSO algorithms.

Financial management early warning model of enterprise circular economy based on chaotic particle swarm optimization algorithm

ABSTRACT Financial management is an important part of enterprise management, aiming to ensure the reasonable utilization of financial resources and help enterprises achieve business goals. This study utilizes chaotic particle swarm optimization algorithm to optimize the backpropagation neural network, and based on this, constructs a financial management early warning model for enterprise circular economy. Performance testing experiments show that the error reaches the target accuracy of 0.042881, with an R-value of 0.92287, indicating high prediction accuracy of the model; In the comparison experiment with the backpropagation model and the genetic feedback model, this study constructed a model with faster convergence speed and an overall accuracy of 92%, significantly higher than the other two models, further verifying the superiority of the chaotic particle swarm feedback neural network model. This indicates that the research model has certain application value in the financial management of circular economy in enterprises.

Prediction and control strategy based on optimized active disturbance rejection control for AHC system

In this paper, a novel control and prediction algorithm is proposed to solve the problem of decoupling control and response delay in the AHC system of electric winch, which effectively improves the compensation accuracy and response speed of the AHC system. The innovation of the control strategy is mainly as follows: 1) Long short-term memory (LSTM) neural network is used for predicting the ship heave motion. The Bayesian algorithm is used to optimize the hyperparameters to improve the prediction accuracy.2) In the design of the AHC controller, the main speed feedforward is used to directly govern the system's output. The position loop adopts the active disturbance rejection control (ADRC) to improve the anti-disturbance and position-tracking ability of the AHC system.3) For the numerous parameters of ADRC, the chaotic particle Swarm optimization (CPSO) algorithm is used to tune parameters. The simulation results show that the algorithm is effective in improving the robustness and compensation efficiency of the system. Finally, the experiment is carried out on the AHC experimental platform of the full-size winch. The compensation results before and after prediction are compared and analyzed.

The Optimization of Digital Currency Electronic Payment in RMB Based on Big Data and Fuzzy Theory

Supply chain management is a key component of the Electronic-China Yuan (e-CNY) infrastructure and is crucial to developing and using e-CNY. This paper first provides an overview of the features of e-CNY and the idea of incorporating supply chain management into the creation of e-CNY software to better meet user payment requirements. This will help to reinforce the CNY's dominant position in the global monetary system. Meanwhile, this paper uses the fuzzy theory evaluation method to assess the software supply chain management informatization level. More broadly, this paper discusses the development level of digital enterprise supply chain management informatization to understand the current situation of e-CNY software supply chain management. The network's energy usage will increase when e-CNY software is developed. This research suggests a routing protocol based on the combination of chaotic particle swarm optimization (CPSO) and ant colony algorithm (ACA). It employs a new CPSO algorithm to optimize the cluster head selection.

A modified particle swarm optimization algorithm for a vehicle scheduling problem with soft time windows

This article constructed a vehicle scheduling problem (VSP) with soft time windows for a certain ore company. VSP is a typical NP-hard problem whose optimal solution can not be obtained in polynomial time, and the basic particle swarm optimization(PSO) algorithm has the obvious shortcoming of premature convergence and stagnation by falling into local optima. Thus, a modified particle swarm optimization (MPSO) was proposed in this paper for the numerical calculation to overcome the characteristics of the optimization problem such as: multiple constraints and NP-hard. The algorithm introduced the “elite reverse” strategy into population initialization, proposed an improved adaptive strategy by combining the subtraction function and “ladder strategy” to adjust inertia weight, and added a “jump out” mechanism to escape local optimal. Thus, the proposed algorithm can realize an accurate and rapid solution of the algorithm’s global optimization. Finally, this article made typical benchmark functions experiment and vehicle scheduling simulation to verify the algorithm performance. The experimental results of typical benchmark functions proved that the search accuracy and performance of the MPSO algorithm are superior to other algorithms: the basic PSO, the improved particle swarm optimization (IPSO), and the chaotic PSO (CPSO). Besides, the MPSO algorithm can improve an ore company’s profit by 48.5–71.8% compared with the basic PSO in the vehicle scheduling simulation.

Research on the Service Condition Monitoring Method of Rolling Bearings Based on Isomorphic Data Fusion

In order to solve the problem that it is difficult for a single sensor to accurately characterize the running state of rotating bearings under complex working conditions, this paper proposes a data-level fusion method based on multi-source isomorphic sensors to monitor spindle bearings. First, new vibration signals in the X,Y,Z direction were obtained through the process of decomposing, de-noise, and reconstructing. Second, the PCA algorithm was used to select the time-domain and frequency-domain features of the vibration signals, construct the feature matrix, and perform dimensionality reduction in the feature matrix. Finally, the entropy weight method was introduced to obtain the initial weights of the three directions as the inputs of the adaptive function. The chaotic particle swarm optimization algorithm proposed in this paper helps particles jump out of the local optimum. Chaotic mapping is used to initialize the velocity and position of the particles, which calculates globally optimal weights in three directions. In order to extract bearing signal features more accurately and efficiently, a DenseNet and Transformer (DAT) feature extraction model is proposed to deal with the complex changes and noise interference of bearing signals. Through the open data set of Jiangnan University and the data collected by our own experimental platform, the maximum accuracy of the DAT model was verified to be 100%.

Transformer fault diagnosis based on Chaotic Particle Swarm Optimization RBF Neural Network

This article uses logistic chaotic mapping to improve the particle swarm algorithm parameters and construct the chaotic particle swarm optimization (CPSO) algorithm. Then, the CPSO algorithm is used to optimize the width, weight, and center values of the Radial Basis Function Neural Networks (RBFNN) to improve the RBFNN model used to diagnose transformer fault types. Results show that the CPSO-RBFNN model has a small mean square error and high accuracy in diagnosing transformer faults.