Search

The traditional decomposed ensemble prediction model decomposes the entire rainfall sequence into several sub-sequences, dividing them into training and testing periods for modeling. During sample construction, future information is erroneously mixed into the training data, making it challenging to apply in practical rainfall forecasting. This paper proposes a novel stepwise decomposed ensemble coupling model, realized through variational mode decomposition (VMD) and bidirectional long short-term memory neural network (BiLSTM) models. Model parameters are optimized using an improved particle swarm optimization (IPSO). The performance of the model was evaluated using rainfall data from the Southern Four Lakes basin. The results indicate that: (1) Compared to the PSO algorithm, the IPSO algorithm-coupled model shows a minimum decrease of 2.70% in MAE and at least 2.62% in RMSE across the four cities in the Southern Four Lakes basin; the IPSO algorithm results in a minimum decrease of 25.58% in MAE and at least 28.19% in RMSE for the VMD-BiLSTM model. (2) When compared to IPSO-BiLSTM, the VMD-IPSO-BiLSTM based on the stepwise decomposition technique exhibits a minimum decrease of 26.54% in MAE and at least 34.16% in RMSE. (3) The NSE for the testing period of the VMD-IPSO-BiLSTM model in each city surpasses 0.88, indicating higher prediction accuracy and providing new insights for optimizing rainfall forecasting. [ABSTRACT FROM AUTHOR]

With the rapid development of global trade, the turnover of shipping containers has increased rapidly. How to use port resources reasonably and efficiently has become one of the main challenges that ports need to deal with when planning for the future. In order to develop scientific and efficient berth plans to improve operational efficiency and service level, this paper proposes a hybrid prediction model based on Principal Component Analysis (PCA) and Extreme Learning Machine (ELM) optimized by Improved Particle Swarm Optimization (IPSO), namely, the PCA-IPSO-ELM model. After assessing the uncertain factors influencing the operation time of the container ship at berth, this work reduces the dimensionality of the investigational data by the PCA method. Aiming to solve easy premature convergence of the traditional particle swarm algorithm, this paper introduces an improved particle swarm optimization algorithm via dynamic adjustment of nonlinear parameters. This improved particle swarm algorithm is mainly used to optimize the weights and thresholds of the extreme learning machine. Thus, a PCA-IPSO-ELM model which aims to forecast the operation time of a container ship at berth, is constructed. Using the historical operation data of the Tianjin Port Container Shipping Company as the prediction sample, this PCA-IPSO-ELM model is compared and assessed with traditional models. The results show that compared with other models, the PCA-IPSO-ELM prediction model has the characteristics of high prediction accuracy, fast running rate and strong stability, and it has a higher coefficient of determination and a better fitting degree. [ABSTRACT FROM AUTHOR]

Hybrid heavy-duty trucks have attracted wide attention due to their excellent fuel economy and high mileage. For power-split hybrid heavy-duty trucks, the optimization of powertrain parameters is closely related to the control strategies of hybrid vehicles. In particular, the parameters of the powertrain system will directly affect the control of the vehicles' power performance and economy. However, currently, research on hybrid heavy-duty trucks employing power-split configurations is lacking. Furthermore, few studies consider both the optimization of powertrain parameters and the control strategy at the same time to carry out comprehensive optimization research. In order to address these issues, this paper focuses on the fuel economy of hybrid heavy-duty trucks with power-split configurations. Improved particle swarm optimization (IPSO) and dynamic programming (DP) algorithms are introduced to optimize powertrain parameters. With these methods being applied, hybrid heavy-duty trucks show a 2.15% improvement in fuel consumption compared to that of the previous optimization. Moreover, based on the optimal powertrain parameters, a DP-based rule-control strategy (DP-RCS) and optimal DP-RCS scheme are presented and used in this paper to conduct our research. Simulation results show that the optimal DP-RCS reduces fuel consumption per hundred kilometers by 11.35% compared to the rule-based control strategy (RCS), demonstrating that the combination of powertrain parameter optimization and DP-RCS effectively improves the fuel economy of hybrid heavy-duty trucks. [ABSTRACT FROM AUTHOR]

In modern distributed power photovoltaic (PV) grid-connected 1-φ inverter system below 10 kW, the PV mismatch that lowers the PV energy utilization efficiency limits this technology’s potential. An utilized switching in double-stage conversion introduces harmonics and degrades overall efficiency. Nonlinear adaptive controllers are required for strongly nonlinear photovoltaic (PV) converter-inverter-based switching in weather-affected systems to optimize performance. This paper proposes a maximum power point tracking (MPPT) algorithm based on two controllers, including a fuzzy logic controller (FLC) and a neuro-fuzzy controller (NFC), on the converter side and an additional FLC and NFC for DC-bus voltage and grid-current control on the inverter side. The sizing components of the system used in the modeling are comprehensively presented. The topology of FLCs and NFCs are achieved by using the improved particle swarm optimization (IPSO), thereby reducing complexity and achieving optimal control performance. The proposed FLCs and NFCs are implemented and evaluated on a 2.5 kW grid-connected PV system using Matlab/Simulink, while comparing with conventional FLC and FLC-PSO, including simple controllers of P&O and PI controllers. Grid current-voltage synchronization and THD reduction are achieved via a phase locked loop (PLL) circuit and an LCL filter. The simulation results show that the proposed controllers improve MPPT performance, in terms of PV energy utilization efficiency, by 8% to 40% on the converter side and achieve up to 96% inverter efficiency, reducing THD by 75% over the rest. [ABSTRACT FROM AUTHOR]

Precipitation images play an important role in meteorological forecasting and flood forecasting, but how to characterize precipitation images and conduct rainfall similarity analysis is challenging and meaningful work. This paper proposes a rainfall similarity research method based on deep learning by using precipitation images. The algorithm first extracts regional precipitation, precipitation distribution, and precipitation center of the precipitation images and defines the similarity measures, respectively. Additionally, an ensemble weighting method of Normalized Discounted Cumulative Gain-Improved Particle Swarm Optimization (NDCG-IPSO) is proposed to weigh and fuse the three extracted features as the similarity measure of the precipitation image. During the experiment on similarity search for daily precipitation images in the Jialing River basin, the NDCG@10 of the search results reached 0.964, surpassing other methods. This indicates that the method proposed in this paper can better characterize the spatiotemporal characteristics of the precipitation image, thereby discovering similar rainfall processes and providing new ideas for hydrological forecasting. [ABSTRACT FROM AUTHOR]

In the context of cloud computing, the task scheduling issue has an immediate effect on service quality. Task scheduling is the process of assigning work to available resources based on requirements. The objective of this NP-hard problem is to identify the ideal timetable for resource allocation so that more tasks can be done in less time. Several algorithms have been presented thus far to solve the problem of work scheduling. In this paper proposes an Improved Particle Swarm Optimization (IPSO) algorithm to address the aforementioned issue. In order to shorten the execution time of the original Particle Swarm Optimization (PSO) algorithm for task scheduling in the cloud computing environment, a multi-adaptive learning strategy is employed. In its initial population phase, the proposed Multi Adaptive Learning for Particle Swarm Optimization (MALPSO) defines two sorts of particles: ordinary particles and locally best particles. During this phase, the population's variety is reduced and the likelihood of reaching the local optimum rises. This study compares the proposed approach to various algorithms based on four criteria: makespan, load balancing, stability, and efficiency. Additionally, we examine the proposed technique using the CEC 2017 benchmark. Compared to what is currently known, the suggested method can solve the problem in less time and get the best answer for most of the criteria. [ABSTRACT FROM AUTHOR]

In this paper we outline newly-developed control algorithms, designed to achieve high-precision feedback for a motor control system using a magnetic encoder. The magnetic encoder, combing single-pole and multi-pole magnetic steels, was adopted to extend the resolution of the magnetic encoder. First, with a view to settling the issue of the jump points of the multi-pole angle value at the convergence of two adjacent magnetic poles, the angle segmentation method, which uses the window filter discrimination method, is employed to determine the actual angle value. The appropriate filter window width is selected via the improved particle swarm optimization (IPSO) algorithm, and an expanded resolution is achieved. Second, a compensation table is completed via a linear compensation algorithm based on virtual cutting to enhance the accuracy of the combined magnetic encoder. On this basis, a linear difference algorithm is used to achieve deviation correction of the angle. Finally, the jump points can be restrained effectively via the angle segmentation method. The resolution reaches 0.05°, and the accuracy is 0.045°. [ABSTRACT FROM AUTHOR]

Solar-powered water pumping systems (SPWPS) are increasingly used in remote agricultural areas where grid integration is not feasible. The main objective of SPWPS is to extract maximum power from the solar photovoltaic array (SPVA) and deliver it to the motor-pump drive. A simplified motor control scheme for the extraction of maximum power from SPVA with the induction motor drive for partial shading conditions is not presented so far. This paper proposes a vectorial sliding mode (VSM)-based field-oriented control for a single-stage induction motor drive. The VSM control is inherited with maximum power extraction algorithm for SPVA. The improved particle swarm optimization (IPSO) method is used to achieve maximum power extraction from the SPVA, even in partial shading conditions. The IPSO algorithm generates a peak power equivalent voltage reference, which is used as a torque reference for the VSM control (VSMC). The VSMC control is developed in complex valued form to eliminate the need for Clark and Park transformations and thus reducing the order of the system. VSMC incorporates sensorless features, eliminating the need for speed and voltage sensors at the motor terminals. The proposed system is investigated for change in insolation of SPVA that works for partial shading conditions also. The IPSO algorithm is compared with the incremental conductance and particle swarm optimization algorithms, showing the superiority of IPSO method in the peak power tracking accuracy. The IPSO-VSMC algorithm through hardware prototype achieves maximum power tracking accuracy between 87.1 and 93.71% for the most possible insolation cases with partial shading conditions. [ABSTRACT FROM AUTHOR]

Wind energy, as one of the renewable energies with the most potential for development, has been widely concerned by many countries. However, due to the great volatility and uncertainty of natural wind, wind power also fluctuates, seriously affecting the reliability of wind power system and bringing challenges to large-scale grid connection of wind power. Wind speed prediction is very important to ensure the safety and stability of wind power generation system. In this paper, a new wind speed prediction scheme is proposed. First, improved hybrid mode decomposition is used to decompose the wind speed data into the trend part and the fluctuation part, and the noise is decomposed twice. Then wavelet analysis is used to decompose the trend part and the fluctuation part for the third time. The decomposed data are classified. The long- and short-term memory neural network optimized by the improved particle swarm optimization algorithm is used to train the nonlinear sequence and noise sequence, and the autoregressive moving average model is used to train the linear sequence. Finally, the final prediction results were reconstructed. This paper uses this system to predict the wind speed data of China's Changma wind farm and Spain's Sotavento wind farm. By experimenting with the real data from two different wind farms and comparing with other predictive models, we found that (1) by improving the mode number selection in the variational mode decomposition, the characteristics of wind speed data can be better extracted. (2) According to the different characteristics of component data, the combination method is selected to predict modal components, which makes full use of the advantages of different algorithms and has good prediction effect. (3) The optimization algorithm is used to optimize the neural network, which solves the problem of parameter setting when establishing the prediction model. (4) The combination forecasting model proposed in this paper has clear structure and accurate prediction results. The research work in this paper will help to promote the development of wind energy prediction field, help wind farms formulate wind power regulation strategies, and further promote the construction of green energy structure. [ABSTRACT FROM AUTHOR]

Due to the characteristics of time-varying traffic and nonlinearity, the short-term traffic flow data are difficult to predict accurately. The purpose of this paper is to improve the short-term traffic flow prediction accuracy through the proposed improved wavelet neural network prediction model and provide basic data and decision support for the intelligent traffic management system. In view of the extremely strong nonlinear processing power, self-organization, self-adaptation and learning ability of wavelet neural network (WNN), this paper uses it as the basic prediction model and uses the particle swarm optimization algorithm for the slow convergence rate and local optimal problem of WNN prediction algorithm. With the advantages of fast convergence, high robustness and strong global search ability, an improved particle swarm optimization algorithm is proposed to optimize the wavelet neural network prediction model. The improved wavelet neural network is used to predict short-term traffic flow. The experimental results show that the proposed algorithm is more efficient than the WNN and PSO–WNN algorithms alone. The prediction results are more stable and more accurate. Compared with the traditional wavelet neural network, the error is reduced by 14.994%. [ABSTRACT FROM AUTHOR]

Three-dimensional path planning is instrumental in path decision making and obstacle avoidance for deep-sea mining vehicles (DSMV). However, conventional particle swarm algorithms have been prone to trapping in local optima and have slow convergence rates when applied to underwater robot path planning. In order to secure a safe and economical three-dimensional path for the DSMV from the mining area to the storage base in connection with innovative mining system, this paper proposes a multi-objective optimization algorithm based on improved particle swarm optimization (IPSO) path planning. Firstly, we construct an unstructured seabed mining area terrain model with hazardous obstacles. Consequently, by considering optimization objectives such as the path length, terrain undulation, comprehensive energy consumption, and crawler slippage rate, we convert the path planning problem into a multi-objective optimization problem, constructing a multi-objective optimization mathematical model. Following that, we propose an IPSO algorithm to tackle the multi-objective non-linear optimization problem, which enables global optimization for DSMV path planning. Finally, we conduct a comprehensive set of experiments using the MATLAB simulation platform and compare the proposed method with existing advanced methods. Experimental results indicate that the path planned by the IPSO exhibits superior performance in terms of path length, terrain undulation, energy consumption, and safety. [ABSTRACT FROM AUTHOR]

The Preisach model is a typical scalar mathematical model used to describe the hysteresis phenomena, and it attracts considerable attention. However, parameter identification for the Preisach model remains a challenging issue. In this paper, an improved particle swarm optimization (IPSO) method is proposed to identify Preisach model parameters. Firstly, the Preisach model is established by introducing a Gaussian−Gaussian distribution function to replace density function. Secondly, the IPSO algorithm is adopted to Fimplement the parameter identification. Finally, the model parameter identification results are compared with the hysteresis loop of the piezoelectric actuator. Compared with the traditional Particle Swarm Optimization (PSO) algorithm, the IPSO algorithm demonstrates faster convergence, less calculation time and higher calculation accuracy. This proposed method provides an efficient approach to model and identify the Preisach hysteresis of piezoelectric actuators. [ABSTRACT FROM AUTHOR]

In this paper, a novel method based on multi-dimensional Taylor network (MTN) and improved particle swarm optimization (IPSO), namely IPSO-MTN, is proposed for the identification and prediction of nonlinear time-delay system. Firstly, MTN is used to construct the identification and prediction model by its powerful nonlinear function approximation capability. Secondly, to optimize the weight coefficients of MTN, the improved particle swarm optimization algorithm is quoted. Finally, two numerical examples of identification and a practical example of prediction are given to certify the effectiveness of the proposed method. What's more, the traditional recursive least squares identification (RLS) and back propagation (BP) methods are introduced to compare. The experimental results show that the proposed method in this paper is effective in identification and prediction for the nonlinear time delay system and is superior to the recursive least squares method and back propagation method. [ABSTRACT FROM AUTHOR]

In digital holography (DH), the key parameters in the optical path structure, such as diffraction distance, optical path difference and deflection angle, affect greatly the image quality of holographic reconstruction. Thus, it is crucial to determine the appropriate values of parameters. In this paper, the influence of these parameters on the quality of reconstructed images is deeply analysed through the DH principle and numerical simulation. A method to optimize automatically the parameters in the optical path is proposed based on an improved particle swarm optimization algorithm. An experimental setup for reflective off-axis DH is constructed, and coin and optical resolution plate are taken as test samples to perform the experimental research. Experimental results demonstrate that the proposed method can not only effectively enhance the quality of reconstructed images in the light of five indicators but also reduce the complexity and difficulty of adjusting the optical path parameters in the experiments. [ABSTRACT FROM AUTHOR]

To improve the accuracy of short-term wind power prediction, a short-term wind power prediction model based on the LSTM model and multiple error correction is proposed. First, an affine wind power correction model based on assimilative migration is established to reduce the errors caused by false positives from the initial data. Then, a self-moving window LSTM prediction model based on the improved particle swarm optimization algorithm was established. By improving the particle swarm optimization algorithm, the optimal hidden neuron number and the optimal learning rate of the LSTM model were calculated to enhance the model's accuracy. Definitively, the idea of error feedback prediction is used to correct the initial prediction error, and the prediction error is fed back to the LSTM model to reduce the error caused by the calculation of the LSTM model. By starting from the initial data error, model accuracy error, and model prediction error, multiple error correction of wind power is realized to improve the model accuracy. The simulation results show that the method improves the model's prediction accuracy by using assimilative transfer and error feedback, contributing to the economic operation and sustainable development of the power system. Unlike traditional improvement ideas, the proposed improvement ideas do not involve the inherent characteristics of the original prediction methods. This method does not need to introduce other auxiliary methods and has good universality. [ABSTRACT FROM AUTHOR]

Featured Application: The proposed method in this paper has the potential to solve the task-AGV allocation problem in a complex assembly environment where both multiple-AGV cooperative handling tasks and single-AGV handling tasks exist. An AGV system can be used to transport different-size materials in an assembly line. The hybrid task allocation problem is involved in the assembly line, where both single-AGV tasks and multi-AGV tasks exist. However, there is little research on this problem. The goal of solving this problem is to obtain a task allocation scheme with minimum idle time and maximum system throughput. Since all necessary materials must be delivered to the assembly station before the operation can start, the delivery tasks are not independent of each other in a task group serving the operation. To solve the problem above, a hybrid task allocation method based on a task binding strategy and an improved particle swarm optimization (IPSO) is proposed. Firstly, a mathematical model considering the punctuality of material delivery and the cooperative relationship between tasks is established. Secondly, a task binding strategy and four heuristic rules are devised to improve the quality of randomly- and heuristic-generated individuals in the initial population for model optimization. Thirdly, an IPSO is developed to help the optimization algorithm jump out of local optimums. Finally, a simulation is performed to verify the effectiveness of the proposed methods. The simulation results show that a better scheme can be obtained by our hybrid task allocation method, compared to conventional Genetic Algorithms and PSO algorithms. [ABSTRACT FROM AUTHOR]

The absence of real-time airspeed sensors, which was more often ignored in previous studies, and low dynamic characteristics render stratospheric airship control challenging. This study creatively overcomes the aforementioned problems in an integrated path planning and following control scheme using forecasted wind field data. Herein, an efficient and practicable path planning algorithm is designed. Further, a smooth vector field guidance law is proposed for solving the problem of complex path following. Subsequently, an event-triggered neural network-based adaptive tracking controller is designed, considering the wind forecast error influence. Finally, these three parts are organically integrated to achieve autonomous flight. The stability of the closed-loop system and the exclusion of Zeno behavior are rigorously proved. The simulation results reveal that the convergence rate is 63.8% improved, essentially exhibiting better optimization, the tracking errors are eliminated within 80 s, and 99.4% control input updating times are saved. • This paper creatively addresses the problem of integrated path planning and following control for the stratospheric airship using forecasted wind field data. • A novel approach for the integration of path planning and following control is designed. [ABSTRACT FROM AUTHOR]

In order to meet security requirements of space on orbit service, an obstacle avoidance trajectory planning method using improved particle swarm optimization had been presented in this paper. On the basis of the actual overall structure of 7 degrees of freedom redundant space manipulator and the characteristics of obstacles, the envelope method was used to model the arm and obstacles, respectively. The limit conditions to avoid the collision between them were analyzed. Then, the fitness function under the symmetrical conditions of avoiding the collision and searching for the shortest trajectory was constructed. In addition, the obstacle avoidance trajectory planning was solved based on improved particle swarm optimization (IPSO). Finally, simulation experiments were carried out to prove its effectiveness and rationality, where there were symmetrical advantages in two aspects. It can be concluded that the presented method based on IPSO has strong robustness. [ABSTRACT FROM AUTHOR]

Due to its complexity and non-deterministic polynomial-time hard characteristic, multirobot task allocation problem remains a challenging issue in the field of cooperative robotics. Thanks to its easy implementation and promising convergence speed, the particle swarm optimization method has recently aroused increasing research interest in the area of multirobot task allocation problem. However, the efficiency of the standard particle swarm optimization is hindered by several deficiencies such as the inefficient capabilities in balancing exploration and exploitation, as well as the high likelihood of plunging into stagnation. Aiming at enhancing the performance of particle swarm optimization via remedying these two drawbacks, this paper proposes an improved particle swarm optimization method, which integrates standard particle swarm optimization 2011 with evolutionary game theory. To prevent particles being locked into stagnation, particles in the proposed particle swarm optimization first adopt the updating rules of standard particle swarm optimization 2011 to undertake their movements. Subsequently, attempting to well trade off the exploration and exploitation capabilities of particles, a novel self-adaptive strategy, which is determined by the evolutionary stable strategies of evolutionary game theory and the iteration number of particle swarm optimization, is presented to adaptively adjust the main control parameters of particles in the proposed particle swarm optimization. Since the convergence of particle swarm optimization remains paramount and dramatically affects the performance of particle swarm optimization, this paper also analytically investigates the convergence of the proposed particle swarm optimization and provides a convergence-guaranteed parameter selection principle for the proposed method. Finally, leveraging the development of the proposed particle swarm optimization, this paper completes the design of a new particle swarm optimization-based multirobot task allocation method. The performance of the new particle swarm optimization-based multirobot task allocation method is tested through three different allocation cases against four well-known evolutionary methods. Experimental results confirm that the proposed method generally outperforms its contenders in terms of the solution quality. Moreover, the proposed method performs slightly better than the majority of its peers as far as the computation time is concerned. [ABSTRACT FROM AUTHOR]

The biometric identification method is a current research hotspot in the pattern recognition field. Due to the advantages of electrocardiogram (ECG) signals, which are difficult to replicate and easy to obtain, ECG-based identity identification has become a new direction in biometric recognition research. In order to improve the accuracy of ECG signal identification, this paper proposes an ECG identification method based on a multi-scale wavelet transform combined with the unscented Kalman filter (WT-UKF) algorithm and the improved particle swarm optimization-support vector machine (IPSO-SVM). First, the WT-UKF algorithm can effectively eliminate the noise components and preserve the characteristics of ECG signals when denoising the ECG data. Then, the wavelet positioning method is used to detect the feature points of the denoised signals, and the obtained feature points are combined with multiple feature vectors to characterize the ECG signals, thus reducing the data dimension in identity identification. Finally, SVM is used for ECG signal identification, and the improved particle swarm optimization (IPSO) algorithm is used for parameter optimization in SVM. According to the analysis of simulation experiments, compared with the traditional WT denoising, the WT-UKF method proposed in this paper improves the accuracy of feature point detection and increases the final recognition rate by 1.5%. The highest recognition accuracy of a single individual in the entire ECG identification system achieves 100%, and the average recognition accuracy can reach 95.17%. [ABSTRACT FROM AUTHOR]

• Distinct DG Output Modes Validation: The simulation validates distinct DG output modes, impacting prediction. • Mode-Based State Estimation: The paper proposes an innovative mode-based state estimation using IPSO-DBSCAN clustering, overcoming k-means limitations. • Enhanced Data Recovery using DBSCAN: Enhanced data recovery employs DBSCAN with improved secondary clustering, guided by similarity and homogeneity, outperforming traditional methods. • BiGRU-PF State Estimation: BiGRU-PF enhances state estimation accuracy and robustness in distribution networks by detecting DG output modes, addressing k-means limitations, and improving data recovery. This paper proposes a new forecasting-aided state estimation (FASE) method for distribution systems that mitigates issues with uncertain distributed generation (DG) and lost measurements. We utilize an Improved Particle Swarm Optimization (IPSO)-optimized Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for examination of historical DG output data. Based on identified DG output modes, it facilitates precise state prediction and data reconstruction,. The proposed method employs a Bidirectional Gated Recurrent Unit (BiGRU) neural network for state prediction and a particle filter (PF) for final state filtering. The method verification is provided through Python simulations of the Distribution Transformer Unit (DTU)7k distribution network system, demonstrating improved accuracy and robustness against sudden load change and bad data in measurements. [ABSTRACT FROM AUTHOR]

In this paper, an improved version of the particle swarm optimization algorithm is proposed for the online tuning of power system stabilizers in a standard four-machine two-area power system to mitigate local and inter-area mode oscillations. Moreover, an innovative objective function is proposed for performing the optimization, which is a weight function of two functions. The first part of fitness is the function of the angular velocity deviation of the generators, and the other part is a function based on the percentage of undershoot and maximum overshoot, and also the damping time of the power system oscillations. The performance of the proposed stabilization method is compared with the genetic algorithm and bacteria foraging algorithm results. Simulations are made in three different power system operation conditions by changing the system load. The simulation results indicate the superiority of the proposed method over the genetic algorithm and bacteria foraging algorithm. In all the scenarios, power system oscillations are damped faster and with lower amplitude when the power system stabilizers coordinate with the proposed optimization method. [ABSTRACT FROM AUTHOR]

Increasingly complex air path concepts are investigated to achieve improving the power while reducing fuel consumption of diesel engines at high altitudes. One promising technology is the Twin-VGT (variable geometry turbocharger) for diesel engines. A control concept has to be developed to exploit boost potential by coordinated management of the two turbocharger stages at different altitudes. In this paper, a nonlinear model prediction control (NMPC) algorithm based on back propagation neural network (BPNN) was proposed to purpose multi-parameter control of turbocharging system at high altitudes. Optimal control sequences of NMPC were solved by improved particle swarm optimization (PSO), and boost pressure and intake flow achieved good dynamic tracking performance by collaborative control high-pressure VGT (HVGT) and low-pressure VGT (LVGT) under whole operative conditions at high altitudes. NMPC achieved better step response performance compared with PID controller at different altitudes. NMPC control error of intake flow and boost pressure are within 0.26% under steady and transient conditions, exhibiting higher control accuracy and responsiveness even under transient operating conditions at high altitudes. [ABSTRACT FROM AUTHOR]

To evaluate the real working behaviour of high arch dams, the in-situ monitoring data-based inversion of deformation parameters is essential. The frequently used hydraulic, seasonal, and time model cannot accurately interpret the viscoelastic deformation behaviour of concrete dams. The main purpose of this paper is to propose a hydraulic, exponential, seasonal, and time (HEST) statistical model to conduct the multi-point zonal intelligent inversion of viscoelastic parameters of high arch dams. The extreme learning machine (ELM) is used to establish the nonlinear relationship between viscoelastic parameters and finite element method-calculated total hydraulic displacement. The particle swarm optimization is improved by three strategies and is then used to search the optimal viscoelastic parameters from the ELM model, according to the HEST model-separated total hydraulic displacement. An engineering example of the Jinping-I arch dam has demonstrated the good performance of the proposed HEST model and inversion method. In the water level stable stage, the abnormal increase of dam radial displacement, which is measured by the plumb line monitoring system, can be reasonably interpreted by the air temperature annual drop effect and dam concrete's hysteretic deformation, and the latter can be reasonably represented by the step-type exponential function-based hysteretic hydraulic component, which occupies a proportion of 20–30%. The inverted instantaneous elastic moduli of concrete in the four clustered zones of the dam body are 44.8, 48.8, 30.8 and 41.0 GPa, respectively, and other viscoelastic parameters are linearly proportional to these. The inverted parameters in the central of the dam body are the highest, followed by the crest region, while smaller values are obtained for dam regions near the foundation. With the utilization of deformation spatial association, the zonal intelligent inversion can effectively avoid the interference of the location of used dam displacement monitoring points. [ABSTRACT FROM AUTHOR]

Due to outstanding learning capabilities, neural networks have attracted a great deal of research interests and various structures for them have been presented. Quantum neural network is one these structures used in quantum calculations to improve learning capabilities of neural networks. In this paper, using a quantum neural network, a chaos controller for rod-type plasma torch system is developed. This system is extensively nonlinear and its dynamics shows considerable sensitivity to its parameters that makes it a good challenge for investigation of controllers' performances. It is very common to utilize particle swarm optimization (PSO) algorithm to determine the parameters of neural networks. The novelty of this paper is changing the search space of the parameters in each particle based on that parameter's value. Also, weaker particles will be deleted and new particles will be substituted. These modifications have enhanced the proposed algorithm performance by adding the ability of global search and also searching in a reasonable space. To highlight the superiority of the proposed algorithm, the results of eight meta-heuristics optimization algorithms such as artificial bee colony, social spider algorithm and imperialist competitive algorithm are presented for comparison. Simulation results show that the improved PSO (IPSO) proposed in this paper is superior to other algorithms from the viewpoint of convergence speed and accuracy. Moreover, in chaos control of a rod-type plasma torch system, the proposed method outperforms a PID controller and also a neural network controller using perceptron. [ABSTRACT FROM AUTHOR]

The critical frequency foF2 of the ionosphere F2 layer is one of the most important parameters of the ionosphere. Based on the Elman neural network (ENN), this paper constructs a single station forecasting model to predict foF2 one hour ahead. In order to avoid the network falling into local minimum, the model is optimized by the improved particle swarm optimization (IPSO). The input parameters used in the model include local time, seasonal information, solar cycle information and magnetic activity information. Data of the Wuhan Station from 2008 to 2016 were used to train and test the model. The prediction results of foF2 show that the root mean square error (RMSE) of the Elman neural network model is 4.30% lower than that of the back-propagation neural network (BPNN) model. The RMSE is further reduced by 8.92% after using the IPSO to optimize the model. This indicates that the Elman neural network model optimized by the improved particle swarm optimization is superior to the BP neural network and Elman neural network in the forecast of foF2 one hour ahead at Wuhan station. [ABSTRACT FROM AUTHOR]

Traditional droop control strategy has lack of the decoupling ability, so the efficiency and stability of the microgrid based on the traditional droop control strategy are easily affected by the uncontrollable coupling of the distributed generation (DG) units. In this paper, an improved droop control strategy incorporating the coupling compensation and virtual impedance is proposed to improve the efficiency and stability of the microgrid. Its coupling compensation comprises the angular frequency deviation compensation and voltage deviation compensation, which decreases the influence of the uncontrollable coupling. The virtual impedance can change the characteristics of the line impedance of the DG units and reduce their coupling. Therefore, the combination of the coupling compensation and virtual impedance can greatly reduce the power circulating-current of the microgrid and improve its dynamic performance. Then, an improved particle swarm optimization algorithm incorporating the multi-swarm and multi-velocity is taken to optimize the parameters of the coupling compensation with high accuracy and high reliability. Simulation and experimental results validate the high performance of the improved droop control strategy. [ABSTRACT FROM AUTHOR]

This paper has discussed the study of the performance of dynamic voltage restorer (DVR) taking different voltage sag conditions in the supply voltage of the distribution system for linear and non-linear load. DVR is employed to mitigate the voltage sag. Proportional and integral (PI) controller is used for the control of DVR to mitigate the voltage sag. Synchronous reference frame theorybased control algorithm has been implemented for generating reference voltages. In this paper, an efficient improved particle swarm optimisation technique for optimizing the gain parameters of the PI controller has been proposed. The performance and suitability of DVR is validated through MATLAB simulation results and use of Sim Power Systems Blocksets. Integral squared error is implemented to check the performance and suitability of DVR. Extensive results of the proposed method are presented and compared with conventional Ziegler Nichols and genetic algorithm methods of tuning gain parameters of PI controllers. [ABSTRACT FROM AUTHOR]

This paper presents an efficient approach for solving economic dispatch (ED) problems with nonconvex cost functions using an improved particle swarm optimization (IPSO). Although the particle swarm optimization (PSO) approaches have several advantages suitable to heavily constrained nonconvex optimization problems, they still can have the drawbacks such as local optimal trapping due to premature convergence (i.e., exploration problem), insufficient capability to find nearby extreme points (i.e., exploitation problem), and lack of efficient mechanism to treat the constraints (i.e., constraint handling problem). This paper proposes an improved PSO framework employing chaotic sequences combined with the conventional linearly decreasing inertia weights and adopting a crossover operation scheme to increase both exploration and exploitation capability of the PSO. In addition, an effective constraint handling framework is employed for considering equality and inequality constraints. The proposed IPSO is applied to three different nonconvex ED problems with valve-point effects, prohibited operating zones with ramp rate limits as well as transmission network losses, and multi-fuels with valve-point effects. Additionally, it is applied to the large-scale power system of Korea. Also, the results are compared with those of the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In disaster response, 2D wireless sensor networks fail to be deployed since a large number of sensors cannot be placed in harsh ground environments. In this case, as the best air carrier for sensors, unmanned aerial vehicles (UAVs) are always used to construct a 3D flying ad hoc network (FANET) to realize emergency communication. In FANET, however, node localization and data transmission are great challenges due to the increase in network dimensions and the high mobility of UAVs. In this paper, a novel FANET clustering routing protocol based on nodes location is proposed. Firstly, an improved particle swarm optimization (IPSO) algorithm is designed by fusing variable neighborhood search, and IPSO is used to improve the locating precision of UAVs. Furthermore, the base station (BS) clusters UAVs evenly according to nodes location, and the cluster head is determined by weighting the intra-cluster distance and residual energy of nodes. Finally, the optimal next-hop routing is selected by IPSO according to the link reliability and the distance between nodes and the BS to ensure data are transmitted to the BS timely and reliable. The simulation results show that routing protocol based on IPSO realize more efficient performance compared with the existing schemes in terms of positioning indicators, energy consumption, delay, and clustering lifetime. [ABSTRACT FROM AUTHOR]

The traditional particle swarm optimization (PSO) path planning algorithm represents each particle as a path and evolves the particles to find an optimal path. However, there are problems in premature convergence, poor global search ability, and to the ease in which particles fall into the local optimum, which could lead to the failure of fast optimal path obtainment. In order to solve these problems, this paper proposes an improved PSO combined gray wolf optimization (IPSO-GWO) algorithm with chaos and a new adaptive inertial weight. The gray wolf optimizer can sort the particles during evolution to find the particles with optimal fitness value, and lead other particles to search for the position of the particle with the optimal fitness value, which gives the PSO algorithm higher global search capability. The chaos can be used to initialize the speed and position of the particles, which can reduce the prematurity and increase the diversity of the particles. The new adaptive inertial weight is designed to improve the global search capability and convergence speed. In addition, when the algorithm falls into a local optimum, the position of the particle with the historical best fitness can be found through the chaotic sequence, which can randomly replace a particle to make it jump out of the local optimum. The proposed IPSO-GWO algorithm is first tested by function optimization using ten benchmark functions and then applied for optimal robot path planning in a simulated environment. Simulation results show that the proposed IPSO-GWO is able to find an optimal path much faster than traditional PSO-GWO based methods. [ABSTRACT FROM AUTHOR]

Acoustic emission location is important for finding the structural crack and ensuring the structural safety. In this paper, an acoustic emission location method by using fiber Bragg grating (FBG) sensors and particle swarm optimization (PSO) algorithm were investigated. Four FBG sensors were used to form a sensing network to detect the acoustic emission signals. According to the signals, the quadrilateral array location equations were established. By analyzing the acoustic emission signal propagation characteristics, the solution of location equations was converted to an optimization problem. Thus, acoustic emission location can be achieved by using an improved PSO algorithm, which was realized by using the information fusion of multiple standards PSO, to solve the optimization problem. Finally, acoustic emission location system was established and verified on an aluminum alloy plate. The experimental results showed that the average location error was 0.010 m. This paper provided a reliable method for aluminum alloy structural acoustic emission location. [ABSTRACT FROM PUBLISHER]

To ensure effective hull assembly line production, it is vital to consider the problems on delivery date of order and sequencing complexity within mixed-model production environments. In this paper two criteria are considered for stratified optimization according to their importance: to minimize the satisfaction ratio of delivery date, and to minimize the complexity degree of the system arising from its state frequent changes. Finding an optimal solution for this complicated problem in reasonable computational time is cumbersome. Therefore, this paper presents an improved particle swarm optimization (IPSO) algorithm to solve the multi-objective sequencing problems. Instead of modeling the positions of particles in a continuous value manner in traditional PSO, IPSO uses an encoding and decoding scheme of task-oriented assignment for representing the discrete input sequences of products. Furthermore, dynamic mutation operator and chaos strategy are introduced to help the particles escape from local optima and the strategy for population decomposition is proposed to further improve the efficiency of the optimization. Numerical simulation suggests that the proposed IPSO scheduler can provide obvious improvement on solution quality and running time. Finally, a case study of the optimization of a panel block assembly line was given to illustrate the effectiveness of the method. [ABSTRACT FROM AUTHOR]

Optimal coordination of directional over-current relays (DOCRs) is a crucial task in ensuring the security and reliability of power system network. In this paper, a hybridization of an improved particle swarm optimization and linear programming (IPSO-LP) is proposed to solve DOCRs coordination problem. The considered decision variables in the optimization are plug setting current, time multiplier setting, type of relay, and type of curve. By considering these parameters in the optimization, the best relay operating time can be determined. Furthermore, the proposed technique also considered the continuous values of pick-up current setting (PSC) and time setting multiplier (TMS). Test on the IEEE 8 bus system has proven the effectiveness of the technique, where an improvement between 2.36% and 45.8% of total relay operating time can be observed as compared to other techniques in literature. In addition, the obtained settings of the DOCRs from the proposed technique have been verified using industrial software to make sure no setting that cause violation to the DOCRs operation. [ABSTRACT FROM AUTHOR]

The Internet of Things (IOT) management platform is used to manage and transmit data from a variety of terminal devices in the power system. In terms of detecting abnormal data, the existing IOT management platform has a low data processing efficiency and a high error rate. In addition, the optimal selection and determination of the structural parameters of a convolutional neural network (CNN) have a substantial effect on its prediction performance. On this basis, the paper proposes a decision algorithm for locating anomalous data in an IOT integrated management platform using a CNN and a global optimization decision of key structural parameters of a CNN using an improved particle swarm optimization (APSO) algorithm. Initially, an index model is developed to identify whether the data obtained from the IOT management platform is abnormal. Second, the structure of the CNN‐based anomaly detection approach is investigated. Next, an improved particle swarm optimization approach is designed to optimize the structural parameters of the CNN, and an APSO‐CNN with higher performance for anomalous data localization is constructed. Using the Adam optimizer, the accuracy, feasibility, and efficiency of the established method were assessed. The results demonstrate that the developed APSO‐CNN‐based decision method for anomaly data localization offers significant advantages in terms of precision and execution speed, with potentially intriguing application potential. The Internet of Things (IOT) management platform is used to handle and transmit data from many types of power system terminal devices. The current IOT management platform has a low data processing efficiency and a high mistake rate when it comes to finding anomalous data. [ABSTRACT FROM AUTHOR]

The wide application of renewable energy makes distributed power generation play an increasingly important role, which brings a certain influence to traditional power generation. In the overall environment of smart grid planning, reasonable optimization of load supply side will greatly reduce the cost of power generation, smooth the fluctuation of load, and improve the economy of energy utilization. The optimization of supply side load using time-of-use (TOU) could promote the optimal allocation of resources, which is conducive to the sustainable development of clean energy. This paper aimed at researching the operational mode of traditional power plant and wind power when considering the demand response TOU and considering the constraints of environmental cost, thermal power load, overall dispatching and other factors to optimize the operation mode. In this paper, a multi-objective optimization model with the objective function of minimum curtailment wind rate and maximum power generation profit was established. The effectiveness of the proposed model and the improved algorithm was verified by an example. This paper also provided some support for further research on supply-side renewable energy generation and thermal power optimization considering energy storage. [ABSTRACT FROM AUTHOR]

With the rapid development of network technology, college Chinese teaching needs to be reformed as so to adapt to the network environment. This paper puts forward a new strategy of college Chinese teaching based on SNS (social network services) network platform, constructs the model of college Chinese teaching resource allocation, and designs the college Chinese teaching network teaching platform using SNS network method. The routing nodes of SNS network platform are optimized, and the improved PSO (particle swarm optimization) is used to optimize the location of SNS network platform nodes. Based on the node distribution model of PSO, the node overlay connectivity graph of the new SNS network platform of college Chinese teaching is reconstructed to realize the network coverage optimization. The experimental results show that the proposed SNS network platform has better performance of resource scheduling and network connectivity. [ABSTRACT FROM AUTHOR]

The current fault diagnosis method has the problems of long time and low recognition rate of fault diagnosis for the gear fault diagnosis of automobile transmission. For these problems, a fault diagnosis method for gear of automobile transmission based on improved particle swarm optimization algorithm is proposed in this paper. The speed information of gear fault vibration signal is extracted. The extracted speed information is used for uniform angular resampling of gear fault vibration signal and converted to angular domain signal. The cyclostationary demodulation analysis is carried out to the angular domain signal, and the slice demodulation is performed at each order of the fault feature of the cyclic autocorrelation function. Compound fault diagnosis of gearbox is achieved based on slice demodulation spectrum of each slice signal. In order to improve the accuracy of fault diagnosis and reduce the time of fault diagnosis, the CGA algorithm is introduced. The CGA acceleration operator is introduced in every step of iterations of particle swarm optimization, so that the local search ability of particle swarm optimization can be improved. The local convergence speed and convergence accuracy of PSO can be greatly improved. Simulation results show that the proposed method can effectively shorten the time of fault diagnosis and improve the efficiency of fault diagnosis and recognition. [ABSTRACT FROM AUTHOR]

Traditional shallow machine learning algorithms cannot effectively explore the relationship between the fault data of oil-immersed transformers, resulting in low fault diagnosis accuracy. This paper proposes a transformer fault diagnosis method based on Multi-class AdaBoost Algorithms in response to this problem. First, the AdaBoost algorithm is combined with Support Vector Machines (SVM), The SVM is enhanced through the AdaBoost algorithm, and the transformer fault data is deeply explored. Then the dynamic weight is introduced into the Particle Swarm Optimization (PSO); through the real-time update of the particle inertia weight, the search accuracy and optimization speed of the particle swarm optimization algorithm is improved, and the improved particle swarm optimization algorithm (IPSO) is used to optimize the parameters of the SVM. Finally, by analyzing the relationship between the dissolved gas in the transformer oil and the fault type, the uncoded ratio method forms a new gas group cooperation. The improved ratio method is constructed as the input feature vector. Simulations based on 117 sets of IECTC10 standard data and 419 sets of transformer fault data collected in China show that the diagnosis method proposed in this paper has strong search ability and fast convergence speed and has a significant improvement in diagnostic accuracy compared with traditional methods.

Prediction-error value ordering (PEVO) is an efficient implementation of reversible data hiding (RDH), which is perfect for color images to exploit the inter-channel and intra-channel correlations synchronously. However, the existing PEVO method has a slight shortage in the mapping selection stage, the candidate mappings are selected under conditions inconsistent with actual embedding in advance, and this is not the optimal solution. Therefore, in this paper, a novel RDH method for color images based on PEVO and adaptive embedding is proposed to implement adaptive two-dimensional (2D) modification for PEVO. Firstly, an improved particle swarm optimization (IPSO) algorithm based on PEVO is designed to alleviate the high temporal complexity caused by the determination of parameters and implement adaptive 2D modification for PEVO. Next, to further optimize the mapping used in embedding, an improved adaptive 2D mapping generation strategy is proposed by introducing the position information of points. In addition, a dynamic payload partition strategy is proposed to improve the embedding performance. Finally, the experimental results show that the PSNR of the image Lena is as high as 62.94 dB and the average PSNR of the proposed method is 1.46 dB higher than that of the state-of-the-art methods for embedding capacity of 20,000 bits. • A novel RDH method for color images based on PEVO and adaptive embedding. • An improved particle swarm optimization (IPSO) algorithm based on prediction-error value ordering. • An improved adaptive 2D mapping generation strategy. • A dynamic payload partition strategy. • A novel priority evaluation formula for pixel block. [ABSTRACT FROM AUTHOR]

The accurate prediction of digging load serves as a fundamental cornerstone for advancing the development of intelligent and unmanned excavators. Given the complex nonlinear dynamics of digging load, this paper proposes a novel prediction model for excavator load power based on the chaos theory and support vector regression (SVR). The presence of chaos in the dynamic digging load system is detected through phase space reconstruction. SVR is utilized for nonparametric modeling and prediction, with the reconstructed phase space capturing the essential characteristics of excavator load and serving as inputs for SVR. To optimize the hyperparameters, an improved particle swarm optimization (IPSO) algorithm is presented. Excavation experiments conducted under two typical load conditions demonstrate the superiority of the proposed chaos-based IPSO-SVR model in terms of prediction accuracy. This research lays a solid foundation for practical load prediction in industrial excavator settings. [ABSTRACT FROM AUTHOR]

Estimation and analysis of energy consumption for machine tool is the basis of energy efficiency improvement. To improve the accuracy of ELM algorithm in CNC machine tool energy consumption prediction, a prediction method based on an improved particle swarm optimization (CAPSO) algorithm and an extreme learning machine (ELM) is proposed. The contribution of the algorithm includes the following three aspects. First, sobol sequence is used to initialize the PSO population to make distribution of initial population more even in solution space. Second, the center wanders and boundary neighborhood updates strategy are used to improve the population quality and convergence rate of PSO. Then, to avoid the optimal local solution, the adaptive inertia weight is introduced to achieve the stochastic perturbation of the population. The performance of the algorithm is tested by ten benchmark function, indicating that the CAPSO ensures the search accuracy and improves the algorithm's convergence rate. Finally, the CAPSO algorithm is used to optimize the weights and thresholds of an ELM, and the CAPSO-ELM cutting energy consumption prediction model is established. Case analysis and comparative experiments show that the stability, prediction accuracy and generalization ability of CAPSO-ELM model are better than those of other models. [ABSTRACT FROM AUTHOR]

Hybrid heavy-duty trucks have attracted wide attention due to their excellent fuel economy and high mileage. For power-split hybrid heavy-duty trucks, the optimization of powertrain parameters is closely related to the control strategies of hybrid vehicles. In particular, the parameters of the powertrain system will directly affect the control of the vehicles’ power performance and economy. However, currently, research on hybrid heavy-duty trucks employing power-split configurations is lacking. Furthermore, few studies consider both the optimization of powertrain parameters and the control strategy at the same time to carry out comprehensive optimization research. In order to address these issues, this paper focuses on the fuel economy of hybrid heavy-duty trucks with power-split configurations. Improved particle swarm optimization (IPSO) and dynamic programming (DP) algorithms are introduced to optimize powertrain parameters. With these methods being applied, hybrid heavy-duty trucks show a 2.15% improvement in fuel consumption compared to that of the previous optimization. Moreover, based on the optimal powertrain parameters, a DP-based rule-control strategy (DP-RCS) and optimal DP-RCS scheme are presented and used in this paper to conduct our research. Simulation results show that the optimal DP-RCS reduces fuel consumption per hundred kilometers by 11.35% compared to the rule-based control strategy (RCS), demonstrating that the combination of powertrain parameter optimization and DP-RCS effectively improves the fuel economy of hybrid heavy-duty trucks.

This paper presents an improved particle swarm optimization (PSO) algorithm for determining the global maximum power point tracking (GMPP) of photovoltaic (PV) array under partially shaded conditions (PSC). Under PSC, the power–voltage characteristics have a more complex shape with several local peaks and one global peak. Most of the conventional techniques that are applied in the maximum power-tracking control unit of PV stations do not provide reliable tracking of the GMPP under PSC, which leads to decrease the reliability and the performance of the PV power plant. The performances of the proposed PSO algorithm and the conventional perturb and observe algorithms are evaluated using simulations in MATLAB/Simulink. Eight different partial shading patterns have been selected to prove the robustness of the proposed algorithm. A (step-up) DC–DC boost converter is interfaced with the proposed model. The results indicate that the modified PSO algorithm can very fast track the GMPP within 150–280 ms for different shading conditions furthermore the quality of the tracked power is very high as compared with the previous studies in the literature. Also, the average tracking efficiency of the proposed PSO algorithm is higher than 99.8%, which provides good prospects to apply this algorithm in the control search unit for the GMPP in PV stations. [ABSTRACT FROM AUTHOR]

With the development of smart distribution networks, the future distribution network becomes an important platform for distributed power system in forms of consumption, acceptance and transmission. Reasonably planning the location and capacity of a distributed power supply system can improve the stability margin of the voltage distribution system, reduce the distribution network loss and increase the load rate of a distribution system effectively. However, without a suitable optimization method to solve distributed power supply system problem, it will not only increase system operation cost, but also seriously affect the safe operation of micro grids. Therefore, the problem of optimal configuration such as sizing of distributed power system has become an important research content of distribution network planning. This paper is focus on optimizing a multi-objective programming which relates to the distributed power planning. Considering various optimization objectives such as investment efficiency, voltage stability and the reduction of network loss, this paper builds a multi-objective distributed power optimization model and proposes a configuration method for optimizing the distributed power using improved particle swarm optimization algorithm based on the pareto planning concept. Example analysis shows that the proposed distributed power optimization configuration method has improved the investment benefit of the distributed power system, meanwhile the proposed method can provide a variety of reasonable alternatives from different aspects for decision makers. [ABSTRACT FROM AUTHOR]