Chaotic Particle Swarm Optimization Algorithm Research Articles

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.

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.

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.

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.

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.

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.

Fractional-Order Zener Model with Temperature-Order Equivalence for Viscoelastic Dampers

Viscoelastic (VE) dampers show good performance in dissipating energy, being widely used for reducing vibration in engineering structures caused by earthquakes and winds. Experimental studies have shown that ambient temperature has great influence on the mechanical behavior of VE dampers. Therefore, it is important to accurately model VE dampers considering the effect of temperature. In this paper, a new fractional-order Zener (AEF-Zener) model of VE dampers is proposed. Firstly, the important influence of fractional orders on the energy dissipation ability of materials is analyzed. Secondly, an equivalent AEF-Zener model is developed that incorporates the ambient temperature and fractional-order equivalence principle. Finally, the chaotic fractional-order particle swarm optimization (CFOPSO) algorithm is used to determine the model’s parameters. The accuracy of the AEF-Zener model is verified by comparing model simulations with experimental results. This study is helpful for designing and analyzing vibration reduction techniques for civil structures with VE dampers under the influence of temperature.

Parameter Identification and Dynamic Characteristic Research of a Fractional Viscoelastic Model for Sub-Frame Bushing

This research focused on the rubber bushings of the rear sub-frame in an electric vehicle. A dynamic model was developed to represent the bushing, incorporating an elastic element, a frictional element, and a viscoelastic element arranged in series using a fractional-order Maxwell and a Kelvin–Voigt model. To identify the parameters of the bushing model, an improved adaptive chaotic particle swarm optimization algorithm was employed, in conjunction with dynamic stiffness test data obtained at an amplitude of 0.2 mm. The test data obtained at different amplitudes (0.2 mm, 0.3 mm, 0.5 mm, and 1 mm) were fitted to the model, resulting in fitting errors of 1.13%, 4.07%, 4.42%, and 28.82%, respectively, when compared to the corresponding test data in order to enhance the accuracy of the model fitting; the Sobol sensitivity analysis method was utilized to analyze the parameter sensitivity of the model. Following the analysis, the parameters α, β, and k2, which exhibited high sensitivity, were re-identified. This re-identification process led to a reduction in the fitting error at the 1 mm amplitude to 7.45%. The improved accuracy of the model plays a crucial role in enhancing the simulation accuracy of design of experiments (DOE) analysis and verifying the vehicle’s performance under various conditions, taking into account the influence of the bushing.

A multiple fault diagnosis approach for transformer based on double antenna RFID sensor and PSA-optimized LSTM

This work aims to realize multiple mechanical fault diagnosis for transformers in the incubation period. A double antenna radio frequency identification (RFID) sensor is used to obtain vibration signals generated by a transformer. The measured raw vibration signals have prominent nonlinear characteristics; besides, they are mixed with vast noises, such as electromagnetic interference, measurement disturbance and so on, making it challenging to identify distinguishable features from the measured data. The long short-term memory (LSTM), which exhibits satisfactory performance in dealing with large-scale nonlinear time-series signals, is adopted to extract features from high-dimensional raw signals. Because the parameters of the LSTM cell and support vector machine significantly determine the accuracy of diagnosis, these parameters are determined using the chaotic quantum particle swarm optimization algorithm. Moreover, the sparse periodical attention (PSA) mechanism is used to enhance the LSTM model’s performance by focusing on global feature learning and determining the input data length. The experimental results verify that the exploited RFID sensor realizes reliable data wireless transmission within 17.5 m. Moreover, the PSA-optimized LSTM approach achieves satisfactory trade-off between diagnosis accuracy and computation complexity while dealing with multiple mechanical fault diagnosis of a transformer in early stages.

A Novel Prediction Model for Seawall Deformation Based on CPSO-WNN-LSTM

Admittedly, deformation prediction plays a vital role in ensuring the safety of seawall during its operation period. However, there still is a lack of systematic study of the seawall deformation prediction model currently. Moreover, the absence of the major influencing factor selection is generally widespread in the existing model. To overcome this problem, the Chaotic Particle Swarm Optimization (CPSO) algorithm is introduced to optimize the wavelet neural network (WNN) model, and the CPSO-WNN model is utilized to determine the major influencing factors of seawall deformation. Afterward, on the basis of major influencing factor determination results, the CPSO algorithm is applied to optimize the parameters of Long Short-Term Memory (LSTM). Subsequently, the monitoring datasets are divided into training samples and test samples to construct the prediction model and validate the effectiveness, respectively. Ultimately, the CPSO-WNN-LSTM model is employed to fit and predict the long-term settlement monitoring data series of an actual seawall located in China. The prediction performances of LSTM and BPNN prediction models were introduced to be comparisons to verify the merits of the proposed model. The analysis results indicate that the proposed model takes advantage of practicality, high efficiency, stable capability, and high precision in seawall deformation prediction.

Effective Workflow Scheduling in Cloud using Constriction Factor based Inertia Weight Particle Swarm Optimization

Cloud computing allows rapid provision of resources based on the need. This enables users to execute the independent tasks and dependent tasks called workflows on the cloud system. Workflow scheduling is a crucial problem that is NP Hard and is still a challenging problem. Particle Swarm Optimization (PSO) is one of the commonly used metaheuristic algorithms for solving task scheduling problems, but it has issues with premature convergence and lack of diversity. In recent years, chaotic maps have been employed in PSO to enhance its performance. This study proposes a Constriction factor-based inertia weight in PSO for workflow scheduling (CFPSO). The proposed algorithm utilizes a constriction factor for updating the inertia weight, which enhances the exploration ability of the algorithm thereby avoid local optima. The algorithm considers a fitness function with an aim to minimize makespan, service cost, and maximize load balance. The proposed algorithm is evaluated using a set of benchmark workflows, and the obtained results are compared with the standard PSO algorithm, Grey Wolf Optimizer (GWO) algorithm and Chaotic PSO algorithm. The extensive experimentation performed show that the proposed algorithm outperforms the other algorithms in terms of makespan, service cost, and load balance. The proposed CFPSO shows reduction of 20% of makespan, 2% of the service cost and 18% load balance rate compared to the conventional algorithms on Montage workflow with 1000 tasks. The use of constriction factor enhances the performance of the algorithm and makes it suitable for solving complex problems with multiple objectives. The proposed algorithm can be used in real-world applications to optimize workflow scheduling in cloud computing environments.

Overcoming Nonlinear Dynamics in Diabetic Retinopathy Classification: A Robust AI-Based Model with Chaotic Swarm Intelligence Optimization and Recurrent Long Short-Term Memory

Diabetic retinopathy (DR), which is seen in approximately one-third of diabetes patients worldwide, leads to irreversible vision loss and even blindness if not diagnosed and treated in time. It is vital to limit the progression of DR disease in order to prevent the loss of vision in diabetic patients. It is therefore essential that DR disease is diagnosed at an early phase. Thanks to retinal screening at least twice a year, DR disease can be diagnosed in its early phases. However, due to the variations and complexity of DR, it is really difficult to determine the phase of DR disease in current clinical diagnoses. This paper presents a robust artificial intelligence (AI)-based model that can overcome nonlinear dynamics with low computational complexity and high classification accuracy using fundus images to determine the phase of DR disease. The proposed model consists of four stages, excluding the preprocessing stage. In the preprocessing stage, fractal analysis is performed to reveal the presence of chaos in the dataset consisting of 12,500 color fundus images. In the first stage, two-dimensional stationary wavelet transform (2D-SWT) is applied to the dataset consisting of color fundus images in order to prevent information loss in the images and to reveal their characteristic features. In the second stage, 96 features are extracted by applying statistical- and entropy-based feature functions to approximate, horizontal, vertical, and diagonal matrices of 2D-SWT. In the third stage, the features that keep the classifier performance high are selected by a chaotic-based wrapper approach consisting of the k-nearest neighbor (kNN) and chaotic particle swarm optimization algorithms (CPSO) to cope with both chaoticity and computational complexity in the fundus images. At the last stage, an AI-based classification model is created with the recurrent neural network-long short-term memory (RNN-LSTM) architecture by selecting the lowest number of feature sets that can keep the classification performance high. The performance of the DR disease classification model was tested on 2500 color fundus image data, which included five classes: no DR, mild non-proliferative DR (NPDR), moderate NPDR, severe NPDR, and proliferative DR (PDR). The robustness of the DR disease classification model was confirmed by the 10-fold cross-validation. In addition, the classification performance of the proposed model is compared with the support vector machine (SVM), which is one of the machine learning techniques. The results obtained show that the proposed model can overcome nonlinear dynamics in color fundus images with low computational complexity and is very effective and successful in precisely diagnosing all phases of DR disease.

Cooperative control of multiple intersections combining agent and chaotic particle swarm optimization

The regional intersection can be regarded as an agent model, that should be controlled and optimized. The traditional particle swarm optimization algorithm has been enhanced through optimization techniques and the inclusion of chaos algorithms and agent interactive and cooperative control technology, resulting in the development of a chaotic particle swarm optimization algorithm. Multi-agent is applied to the collaborative control of intersections to realize the collaborative decision-making of multi-intersections. The state and action of intersection signal control agent are defined to reduce the dimension of state-action space and speed up the convergence rate. The proposed method takes vehicle delay, vehicle queue length and regional access ratio as optimization objectives, implements regional traffic signal control strategy and improves overall traffic efficiency. By constructing a simulation environment based on Paramics software, the effectiveness of the regional traffic signal control method based on chaotic particle swarm optimization algorithm is verified, and the convergence efficiency and signal timing optimization have interactive effects.

Multi-index dominant grouping of rock mass discontinuities based on the combined weighting method: A case study for the Huayang tunnel

The properties of discontinuities are determined by their occurrence, trace length, aperture, roughness, and the degree of particle fill; therefore, a multi-index dominant grouping method for rock mass discontinuities based on the combined weighting method was proposed. Firstly, the combined weighting method was used to calculate the weighted value of each index participating in the similarity measurement, and a reasonable elimination of outlier discontinuities is achieved based on the principle of possibility clustering. The adaptive chaotic annealing particle swarm optimization algorithm was then used to search the global optimal initial clustering centers of the fuzzy C-means clustering algorithm. The objective function was established by minimizing the weighted sum of the distance between the discontinuities to be grouped, and the clustering centers to complete the multi-index dominant grouping of rock mass discontinuities. 230 artificial discontinuities with outliers were grouped and compared with other methods, which indicated that this method has higher grouping accuracy. The proposed method was applied to group the measured discontinuities of the Huayang tunnel in Chongqing City, China. Based on the grouping results, a discrete element model for tunnel excavation was established, and the construction was guided according to the calculated results from the model. The engineering applicability of the method was further verified.