Chaotic Particle Swarm Optimization Research Articles

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.

Enhancing Sustainability and Efficiency in Offshore Oil and Gas Engineering through the Integration of Chaotic Local Search and Particle Swarm Optimization for Microenergy Systems Optimization

The offshore oil and gas industry is under increasing pressure to reduce carbon emissions while maintaining energy reliability. Offshore oil and gas platforms (OOGPs) face significant challenges in integrating low‐carbon operations with their energy systems. This study introduces an optimized scheduling approach for offshore microintegrated energy system (OMIES) that incorporates a hybrid energy storage system, including a floating power‐to‐gas associated gas storage (FP2G‐AGS) module, to address the intermittency of renewable energy sources. An economic optimization model is formulated, accounting for carbon emissions, operational costs, and the status of gas turbine generator sets. To solve the complex optimization problem, this study develops a hybrid chaotic local search and particle swarm optimization (CLPSO) algorithm. The CLPSO algorithm synergizes the global search ability of PSO with the local refinement of chaotic local search, enhancing the convergence to optimal solutions. Experimental results demonstrate that the proposed CLPSO algorithm effectively achieves optimal solutions within a range of 48.2–51.7. Case studies validate the model’s capability to promote new energy integration, reduce operational costs, and decrease CO2 emissions across various scenarios. This research significantly contributes to achieving low‐carbon operations on OOGPs and promotes the sustainable development of marine resources.

Power quality disturbance detection method based on optimized kernel extreme learning machine

In order to improve the accuracy of rapid detection of power quality, a power quality disturbance (PQD) classification method based on kernel-based extreme learning machine (KELM) is proposed, and chaos optimization is used to improve the global optimization performance of the particle swarm algorithm. This method first uses KELM to establish a classification model, and then uses an improved chaotic particle swarm optimization (CPSO) to optimize the parameters of KELM. Comparative analysis of example simulation results shows that the algorithm has higher classification accuracy and improves the reliability of power quality disturbance detection.

Chaotic Noise-Based Particle Swarm Optimization Algorithm for Solving System of Nonlinear Equations

Chaotic Noise-Based Particle Swarm Optimization Algorithm for Solving System of Nonlinear Equations

Reliability in Cloud Computing Applications with Chaotic Particle Swarm Optimization Algorithm

Reliability in Cloud Computing Applications with Chaotic Particle Swarm Optimization Algorithm

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.

Design, optimization, and data analysis of solar-tidal hybrid renewable energy system for Hurawalhi, Maldives

In the modern era, every country work towards sustainable development with the help of effective utilization of renewable energy system. The design and planning of multi-renewable energy system networks for Hurawalhi, Maldives, with an approximate 450.09 KW load, is proposed in this study. The first resource assessment for solar radiation, wind velocity, and the tidal range is done through linear regression and decision tree-based data analysis. Design of the system is done through the HOMER software, where the electricity production (KWh/Year) through the solar and tidal systems are 1,401,086 and 197,509, respectively. The energy generation through the proposed system is 1593.6 kWh/day (Baseline) and 2424.25 (Scaled). Cost optimization is done through the Chaotic Particle Swarm Optimization and Cuckoo Optimization techniques. Further survival measurement is done through the logrank and probit analysis.

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.