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Large-scale and widely dispersed distributed energy resource (DER) can be gathered by a virtual power plant (VPP) in a given area, and its parameters can be combined into a single external operation profile. Each distributed energy source in the VPP has a complete backup of the critical information for the entire network because it is a node of blockchain. The distribution network can be accessed by DER freely and adaptable under the scientific management of the VPP, and it can offer the system high-reliability, high-quality, and high-security power services. An energy blockchain network model based on particle swarm optimization (PSO) to optimise the neural network is proposed in this paper as a solution to the issues with the current VPP models. This will enable distributed dispatching of the VPP and reasonable load distribution among units. According to the simulation results, this algorithm's error is minimal and its accuracy can reach 94.98 percent. This model can more accurately capture demand-side real-time information, which benefits VPP's stable scheduling with a welcoming environment and transparent information. It also enhances the system's data security and storage security. This system can successfully address the issues of subject-to-subject mistrust and high information interaction costs in the VPP. [ABSTRACT FROM AUTHOR]

Effective teaching behavior in physical education has an important impact on the quality of classroom teaching. To overcome the shortcomings in the existing university teaching quality assessment procedure, this paper designs an evaluation model for physical education departments based on long short-term memory networks (LSTM) with the improved particle swarm optimization (PSO). The model is constructed by analyzing the connotation and index system of teacher education teaching quality and constructing a fan-leaf structure model of teacher education teaching quality. Then, an improved PSO-LSTM model is proposed to train the teaching samples. The evaluation model applies the improved LSTM model and optimizes the network structure by dynamically adjusting the learning rate. The model is then optimized for the number of neurons and iterations of the network using the improved PSO. The results of the experiment indicate that the proposed model is effective in evaluating the quality of physical education. Moreover, the model analysis' accuracy has greatly improved. This helps teachers have a comprehensive understanding of classroom dynamics and improve their professional competence and classroom teaching quality. [ABSTRACT FROM AUTHOR]

Deformation prediction is an important basis for roadway information construction, especially for soft rock roadway at high altitude and large buried depth, whether the deformation of roadway surrounding rock can be effectively and accurately predicted is an important basis for judging the stability of roadway surrounding rock. However, at present, the research on the informatization construction of the roadway is not in-depth, and the intelligent prediction technology for the deformation of the surrounding rock of the roadway is still in its infancy, and the accuracy of deformation prediction is also low. Therefore, based on the research of domestic and foreign researchers, in order to solve the breakthrough of related technology, this paper puts forward the deformation prediction and control technology of high altitude and deep buried soft rock roadway based on a neural network model. This method is based on the traditional prediction model and is replaced by the neural network, so as to improve the problems of low accuracy and large prediction deviation in the related deformation prediction of the traditional prediction model. At the same time, aiming at the problem of poor local weight and network search ability, an improved method using particle swarm optimization algorithm is proposed, which effectively considers the influence of local and global factors on the combined weight. Finally, the improved deformation prediction model of high altitude and deep buried soft rock roadway based on particle swarm optimization LSTM model is applied to an engineering example and compared with the traditional model to explore its feasibility and effectiveness. The results show that the prediction model has higher prediction accuracy than the traditional prediction model, and the relative deviation of the prediction results is controlled within 2%. At the same time, compared with other models (BP neural network model), it has relatively higher accuracy and stability. The research results can provide a new idea for the deformation prediction of soft rock roadway with high altitude and deep burial. [ABSTRACT FROM AUTHOR]

This paper employs machine learning algorithms to detect tax evasion and analyzes tax data. With the development of commercial businesses, traditional algorithms are not appropriate for solving the tax evasion detection problem. Hence, other algorithms with acceptable speed, precision, analysis, and data decisions must be used. In the case of assets and tax assessment, the integration of machine learning models with meta-heuristic algorithms increases accuracy due to optimal parameters. In this paper, intelligent machine learning algorithms are used to solve tax evasion detection. This research uses an improved particle swarm optimization (IPSO) algorithm to improve the multilayer perceptron neural network by finding the optimal weight and improving support vector machine (SVM) classifiers with optimal parameters. The IPSO-MLP and IPSO-SVM models using the IPSO algorithm are used as new models for tax evasion detection. Our proposed system applies the dataset collected from the general administration of tax affairs of West Azerbaijan province of Iran with 1500 samples for the tax evasion detection problem. The evaluations show that the IPSO-MLP model has a higher accuracy rate than the IPSO-SVM model and logistic regression. Moreover, the IPSO-MLP model has higher accuracy than SVM, Naive Bayes, k-nearest neighbor, C5.0 decision tree, and AdaBoost. The accuracy of IPSO-MLP and IPSO-SVM models is 93.68% and 92.24%, respectively. [ABSTRACT FROM AUTHOR]

This paper proposes a method based on AP clustering and an improved particle swarm algorithm for radio frequency identification (RFID) indoor positioning, called the AP-PSO method. Firstly, an AP clustering algorithm is used to cluster the RSSI values of the experimental region tags with similarity, in order to achieve the division of tagged regions, reduce the search area of the later improved particle swarm algorithm, and reduce the search time. Secondly, the learning factor of the particle swarm algorithm is dynamically adjusted, in order to improve the search ability and convergence speed of the global optimal solution of particles. The experimental results show that the algorithm can effectively achieve RFID indoor positioning of the tags to be measured, with high positioning accuracy and with the algorithm spending less time. [ABSTRACT FROM AUTHOR]

Nowadays, colleges and universities are paying more and more attention to the physical condition of students. Many schools set up physical education courses to exercise students and improve their physical quality. They also conduct physical examinations every semester to test students' conditions. In order to ensure more accurate sports results, this paper uses optimization of the neural group particle group model method to forecast the physical culture test scores of the investigated students. In addition, to guarantee accuracy the particle swarm optimization neural network model method, we compare the GXD method and the LM method with our method. It has the advantage of high precision, optimal prediction effect, strong versatility, higher recall rate, stronger antinoise performance, and wider application range. The article compares the neural network model method for particle swarm optimization with the GXD way and the LM way to ensure precision the neural network model method for particle swarm optimization. [ABSTRACT FROM AUTHOR]

Condition monitoring plays a very important role in equipment fault diagnosis technology. However, existing monitoring methods often collect equipment fault signals from a single dimension, resulting in a major lack of fault information. To improve the problem, we built a gearbox preset fault test bench and constructed a dual-sensor acquisition system to realize the multiple dimensions of vibration signal acquisition in the horizontal and vertical directions of the gearbox. At the same time, given the poor adaptability of most current signal preprocessing methods, the improved nonlinear adaptive inertial weight particle swarm optimization algorithm (NAPSO) and variational modal decomposition (VMD) are combined to optimize the key parameters in VMD with the maximum correlation kurtosis convolution (MCKD) as the fitness function. Further, after extracting fault features from the intrinsic mode functions (IMFS) decomposed by VMD, the single-layer sparse autoencoder network (SAE) and the double-layer stacked sparse autoencoder network (SSAE) with different structures are used to realize an effective fusion of multidimensional information and deep feature extraction. Finally, the hybrid fault diagnosis of gearboxes is realized by using the random forest algorithm (RF) as the classifier. The experimental results show that the accuracy of the method proposed in this paper can reach 96.0%, and the accuracy can be improved by 3.0% and 4.0%, respectively, when compared with a single horizontal or vertical sensor signal input. [ABSTRACT FROM AUTHOR]

Resource depletion and ecological crisis have prompted human beings to reflect on the behavior patterns based on industrial civilization so as to seek ways of sustainable development of human society, economy, technology, and environment. The energy consumed in the construction process, commonly known as building energy consumption, accounts for more and more of the total social energy consumption, and with the continuous development of social economy and the improvement of living standards, this proportion will be larger and larger. The structure of the neural network directly determines its performance and work efficiency. The structure optimization of the neural network is not only a hot issue in this field but also an insurmountable key step in engineering applications. With the increase of network depth, the structural optimization difficulty index of the neural network increases, so solving this problem has important theoretical and practical significance for the design and application of the neural network. In this paper, the energy saving of buildings is optimized based on the optimization of structures such as particle swarm optimization (PSO) algorithm and restricted Boltzmann machine. The experimental results show that the BPNN optimized by the improved PSO algorithm is significantly better than the non-optimized BPNN and the BPNN optimized by the basic PSO algorithm. The comprehensive output rate of the optimized neural network can reach 64.5%. In general, the error rate of the optimized artificial neural network (ANN) will be 57.65% lower than the original one. [ABSTRACT FROM AUTHOR]

In this paper, static voltage stability assessments and their improvement using analytical and optimization techniques with different loading scenarios are described. In this study, the 400 kV, 230 kV, and 132 kV Ethiopian electric power networks are modeled using Power System Analysis Toolbox (PSAT), and the load flow analysis is carried out using MATLAB source code. Then, different voltage stability indices, such as the user-defined advanced voltage stability index (AVSI), modified voltage stability index (MVSI), and fast voltage stability index (FVSI), are used to identify the voltage unstable buses. Besides this, particle swarm optimization (PSO) is also used to find the optimal size and placement of the Static Synchronous Compensator (STATCOM) for the most severe buses identified by voltage stability indices. The result presents three special arguments: whether the most severe buses that are ranked at the top based on the voltage stability indices are optimal buses for compensation placement or not, the voltage stability improvement, and power loss reduction with the introduction of STATCOM. Accordingly, it is observed that the weakest bus that is identified through the stability indices may not be the optimal placement of the compensation device. It is also indicated that, with the introduction of STATCOM to the optimal selected bus, the network power loss is reduced from 46.65 MW to 33.32 MW and its voltage magnitude in all buses is also improved. All bus voltages are within the IEEE standard acceptable limit. [ABSTRACT FROM AUTHOR]

Universities are considered the main place for university students to study and live and also shoulder the important task of training talents for the future of our country. As a manager of university education and the relevant supervisor of education and teaching, it must constantly seek more efficient teaching mode and use the new communication platform in order to solve the current problems in the management of higher education. Since the media platform plays an important role in the social and political and economic life, it affects the way of education and the way of thinking in universities. Self-media technology promotes the communication between teachers and students in the teaching process. Educators can carry out a variety of teaching through the media, which innovates the educational methods of the University. This fully reflects the advantages of we media teaching in the media era. As a new means of information dissemination, the self-media platform not only has the traditional media communication functions but also gradually affects the way of thinking and lifestyles of university students to promote their correct world outlook, outlook on life, and formation of values. In this paper, self-media platform is used for university education and operation management process to conduct a systematic analysis and research. The Delphi method is used to screen out five major dimensions of hardware platform, content construction, communication mode, the quality of teachers and students in universities, and the environment of media network dissemination and interpret the positive effects of self-media platform in improving university teaching and operation management efficiency. Based on particle swarm optimization (PSO) algorithm, the importance distribution of the five dimensions is identified and an optimal path of university education operation and management is constructed to improve the overall efficiency of university education operation and management. [ABSTRACT FROM AUTHOR]

In order to actively respond to the "14th Five-Year Plan," the PGA algorithm is used to develop a new energy planning strategy in this paper. The project can make full use of my country's abundant renewable energy resources, encourage energy conservation and reduction of emissions, improve the energy structure's low-carbon level, support the development of smart green energy, and achieve ecological civilization construction. This solution can show users how much greenhouse gas emissions can be reduced through some environmental changes, as well as the basic issues of meeting the future energy needs. It can display the benefits, costs, and emissions data under different scenarios in the future and use the scenario demonstration method to show energy planning to make energy data more vivid. It allows people, technicians, and decision makers to understand what will happen to China's carbon emissions over time in the next 15 years. This paper innovatively combines a particle swarm optimization algorithm with a genetic algorithm and designs a PGA algorithm for path optimization. In terms of carbon emission reduction, comparative trials demonstrate that the PGA algorithm's path optimization is 58.06 percent greater than the genetic algorithm; In terms of cost, the PGA algorithm's path optimization is 15.72% less expensive than the genetic algorithm's. This article provides a reference path for selecting the best results for future energy planning schemes and provides a new strategy for the "14th Five-Year" energy plan. [ABSTRACT FROM AUTHOR]

PM2.5 concentration is an important indicator to measure air quality. Its value is affected by meteorological factors and air pollutants, so it has the characteristics of nonlinearity, irregularity, and uncertainty. To accurately predict PM2.5 concentration, this paper proposes a hybrid prediction system based on the Synchrosqueezing Wavelet Transform (SWT) method, Quantum Particle Swarm Optimization (QPSO) algorithm, and Long Short-Term Memory (LSTM) model. First, the original data are denoised by the SWT method and taken as the input of the prediction model. Then, the main parameters of the LSTM model are optimized by global search based on the QPSO algorithm, which solves the problems of slow convergence and local extremum of traditional parameter training algorithms. Finally, the PM2.5 daily concentration data of Chengdu, Shijiazhuang, Shenyang, and Wuhan are predicted by the proposed SWT-QPSO-LSTM model, and the prediction results are compared with those of single prediction models and hybrid prediction models. The experimental results show that the proposed model achieves higher prediction precision and lower prediction error than other models. [ABSTRACT FROM AUTHOR]

Big data entails massive cloud resources for data processing and analysis, which consumes more energy to run. The resources and tasks are increasing exponentially in the cloud environment for the processing of big data, which results in an increment in power consumption to run the cloud data center. So, there is always a scope for optimizing the energy utilization in cloud data centers. This paper presents a visionary architecture in a cloud environment for big data with a proposed energy-efficient strategy based on LSTM-DQN (long-short-term memory-deep Q network) using reinforcement learning (RL). The traditional techniques are not so efficient when the tasks are allocated dynamically, and the generic RL strategies are not able to store the data iterated in the last cycles of processing, so the LSTM is considered for this purpose. In the proposed model, integration of DPSO and DQN is used for better estimation and rectification of the curse of dimensionality. The proposed strategy is compared with different variants of PSO (particle swarm optimization) such as DPSO and QoS-PSO. The improvement in results through proposed model is recoded over the algorithm such as load aware (8.01%), DQN (13.36%), EA-DQN (34.16%), L-No-DEAF (15.62%), DPSO (62.68%), QoS-PSO (72.69%), FFO-EVSM (75.42%), and MIMT (76.39%) on the parameter of energy efficiency, tasks completion time, and energy consumption over the timeline. So, the proposed model is encouraging in the energy-efficient cloud environment for big data with the challenges that the technological world is facing and the emergence of deep learning as one propitious field. [ABSTRACT FROM AUTHOR]

In bygone days, the way to deal with traditional villages in developing new cities in China was often to overturn them and replace the original environment with the built-up urban environment, causing constructive destruction to the traditions and cultures. According to the new village transformation requirements, the planning and construction of villages must be shaped with their own cultural and humanistic characteristics of the habitat requirements. It does not favor demolishing large constructions by implementing a different environment. A traditional village is an essential spatial framework to retain the "mark" of the local characteristics of the new city. It can be an essential element in shaping the individual characteristics of the village by realizing the diversity of spatial forms and regional cultural connotations of the new village. Therefore, this paper proposes a new village transformation model based on traditional villages' social, economic, and spatial patterns, combined with a new particle swarm optimization (PSO) algorithm named multi-objective particle swarm optimization (MOPSO). The model transforms the village based on the required optimum resource allocation with the global solution. The experiment shows that the proposed model suits village renovation and modernization. The work is a step toward the advancements and betterment of rural development. [ABSTRACT FROM AUTHOR]

In order to better understand the purchase decision-making process of consumers, this paper makes an in-depth study on the precision marketing of e-commerce products on the basis of KNN algorithm. Through data mining, classic KNN algorithm, BPNN algorithm, and other methods, this paper takes the price and purchase intention of e-commerce agricultural products as an example. Based on the classic nearest neighbor algorithm, binomial function is combined with Euclidean distance formula when calculating the nearest neighbor through similarity. The particle swarm optimization algorithm is used to optimize the binomial function coefficient and the K value of the nearest neighbor algorithm, and the results of the best prediction model for the prediction application of e-commerce agricultural product price and purchase intention are established. Both pricing strategies and promotion strategies will weaken the compromise effect of consumers when they choose e-commerce agricultural products. After studying the calculation method of the KNN algorithm, it not only correctly predicts the price of e-commerce agricultural products but also makes a corresponding prediction and analysis of consumers' purchase intention of e-commerce agricultural products, with the highest accuracy of 94.2%. At the same time, in the future precision marketing process, e-commerce agricultural products enterprises use data technology to achieve precision marketing, which effectively changes the shortcomings of traditional marketing and improves the product marketing effect and economic benefits. [ABSTRACT FROM AUTHOR]

In the Internet of Things (IoT) ecosystem, localization is critical for tracking and monitoring targets via nodes. The distance vector-hop (DV-Hop) technique is a good choice for localizing neighborhood in IoT networks. The conventional DV-Hop algorithm is a distributed localization approach that does not consider the distribution of the nodes into full deliberation when calculating the hop count from the source to destined nodes. The transfer distance and node positions thus do not attain higher efficiency while ascertaining the distance between sources and destined nodes. The study aims to resolve the pitfalls in the traditional algorithm by making enhancements in controlling the original DV-Hop algorithm's hop count and transfer distance method by utilizing the particle swarm to estimate the node positions. Error rate in the distance between beacon nodes and unseen nodes is effectively reduced with the proposed technique that calculate error factors with corrections in a reversed fashion to revise hop counts. An escape factor is introduced to take control of updating particles' velocity in the system, and the inertia weight is defined by a piecewise function to enlarge search space. This mechanism increases the diversity of the particle populations and mitigates the tendency of estimations on node positions to be trapped into local optima under stationary state. Also, the improved DV-Hop algorithm described in the paper has a better convergence speed due to the presence of random inertia weight logarithmic method. Finally, the problem of premature convergence is also tackled as a variation factor is adopted in collaboration with a fitness function that affects the particles' movement range and assists in global convergence. The overall performance of improved DV-Hop is evaluated by statistical metrics and also compared with the traditional DV-Hop algorithm under simulated environment with the data collected from real-world scenarios. Industry 4.0 is fully dependent upon IoT and the count of hops is very important for deciding the routing from the source to destination for speedy transmission of data. The improved DV-hop algorithm can achieve better results and has reduced error rate by more than 30%. The DV-Hop algorithm plays an important role in IoT-enabled environment especially in Industry 4.0. [ABSTRACT FROM AUTHOR]

An efficient pattern synthesis approach is proposed for the synthesis of a time-modulated sparse linear array (TMSLA) in this paper. Due to the introduction of time modulation, the low/ultralow side lobe level can be obtained with a low amplitude dynamic range ratio. Besides, it helps reduce the difficulty of antenna feeding system effectively. Based on particle swarm optimization (PSO) and convex (CVX) optimization, this paper proposes a hybrid optimization method to suppress the grating lobes of the sparse arrays, peak side lobe level (PSLL), and peak sideband level (PSBL). Firstly, the paper utilizes the CVX optimization as a local optimization algorithm to optimize the elements' switch-on duration time, which reduces the side lobe of the array. Secondly, with the PSBL as the objective function, the paper adopts the PSO as a global optimization algorithm to optimize the elements' positions and switch-on time instant, which helps reduce the loss of sideband power caused by time modulation. With respect to the time modulation model, variable aperture sizes (VAS) and more flexible pulse-shifting (PS) schemes are used in this paper. Owing to the introduction of time modulation and CVX optimization, the proposed method is much more feasible and efficient than conventional approaches. Furthermore, it has better array pattern synthesis performance. Numerical examples of the TMSLA and comparisons with the reference are presented to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Research on intelligent transportation wireless sensor networks (ITWSNs) plays a very important role in an intelligent transportation system. ITWSNs deploy high-yield and low-energy-consumption traffic remote sensing sensor nodes with complex traffic parameter coordination on both sides of the road and use the self-organizing capabilities of each node to automatically establish the entire network. In the large-scale self-organization process, the importance of tasks undertaken by each node is different. It is not difficult to prove that the task allocation of traffic remote sensing sensors is an NP-hard problem, and an efficient task allocation strategy is necessary for the ITWSNs. This paper proposes an improved adaptive clone genetic algorithm (IACGA) to solve the problem of task allocation in ITWSNs. The algorithm uses a clonal expansion operator to speed up the convergence rate and uses an adaptive operator to improve the global search capability. To verify the performance of the IACGA for task allocation optimization in ITWSNs, the algorithm is compared with the elite genetic algorithm (EGA), the simulated annealing (SA), and the shuffled frog leaping algorithm (SFLA). The simulation results show that the execution performance of the IACGA is higher than EGA, SA, and SFLA. Moreover, the convergence speed of the IACGA is faster. In addition, the revenue of ITWSNs using IACGA is higher than those of EGA, SA, and SFLA. Therefore, the proposed algorithm can effectively improve the revenue of the entire ITWSN system. [ABSTRACT FROM AUTHOR]

A new method for locating hazardous gas source based on unmanned vehicles is presented in this paper. Based on the gas sensors and unmanned vehicles, the research on the gas source location algorithm, using the gas concentration of several detection sites as heuristic information, is carried out. When the available information is less, such that the gas diffusion model is unknown, the algorithm can locate the gas leakage source quickly. The proposed algorithm combines particle swarm optimization (PSO) and Nelder–Mead simplex method. Compared with the standard PSO, the proposed algorithm has fewer iterations and faster convergence speed. Finally, the feasibility of the algorithm is verified by digital simulation experiments. [ABSTRACT FROM AUTHOR]

The increasing integration of renewable energy sources into modern electric grids has led to a rise in uncertain factors that must be managed to maintain voltage security during reactive power optimization (RPO). Traditional deterministic RPO methods fail to account for these uncertainties, which can result in power grid security issues such as voltage violations. To address these challenges, this paper proposes a data-driven interval-based reactive power optimization method (IRPOM). The IRPOM represents the uncertainties associated with renewable power generation and load demands as intervals within the RPO problem formulation. The proposed method uses an improved particle swarm optimization algorithm to solve the RPO problem. In each iteration, the uncertain power flow is solved using the optimizing-scenarios method- (OSM-) based interval power flow (IPF) algorithm. This approach calculates the real power losses and checks whether state quantities, including voltage, power flow, and generator output, exceed their limits. Furthermore, a data-driven modeling approach is introduced to reduce the conservativeness of the IRPOM solutions. The effectiveness of the proposed method is demonstrated through detailed computational analysis on a modified IEEE 30-bus system. The results show that the proposed approach ensures economic efficiency while maintaining a low bus voltage threshold crossing probability close to zero. [ABSTRACT FROM AUTHOR]

An upgraded Black Widow Optimization algorithm for obtaining the ideal switching angles of a reduced structure multilevel inverter is proposed in this paper with minimum voltage THD. The conventional BWO algorithm has been modified and proposed to solve the THD minimization problem with faster convergence rate, lesser tuning variables, and lesser number of equations than the other existing nature inspired algorithms. The reduced structure MLI is designed with three asymmetrical DC voltage sources of ratio 1 : 2 : 4 to generate 15-level output voltage which requires reduced number of switching devices in comparison with the existing topologies of equivalent voltage level. The asymmetrical DC voltage sources to the MLI are derived from a single DC voltage source using a multisecondary winding DC-DC converter in order to provide the isolation between the load side and source side. The proposed modified BWO algorithm reduces the complexity of solving the nonlinear equations to achieve the best result by avoiding the local optima. To verify the effectiveness of the upgraded BWO algorithm, the optimization problem for three phase fifteen level MLI has been solved using the genetic algorithm, BFO algorithm, and PSO algorithm. The results for the above three applied algorithms are compared with the upgraded BWO and presented. The proposed algorithm has minimized the output voltage THD to 1.83% at 0.88 modulation index. The developed scheme has been corroborated with the help of MATLAB simulation and the results have been verified through a hardware model developed in laboratory. [ABSTRACT FROM AUTHOR]

In this paper, an intelligent integrated approach is proposed to control the reactive power and restore the voltage balance in a three-phase power system using particle swarm optimization (PSO), Gaussian process regression (GPR), and support vector machine (SVM). The PSO algorithm is used in offline mode to determine the optimal set of firing angles for the thyristor-controlled-reactor (TCR) compensator according to the smallest fitness value required for voltage balancing. The optimum firing angles are then used to train the GPR and SVM regression models. The GPR and SVM models are finally used as a real-time controller to retrieve the voltage balance in online mode. A simulation model and experimental setup of the electrical power system are built. The modeled system consists of a 500 km long transmission line. The line is divided into three-pi sections to guarantee a real system response. Several simulation and practical case studies have been conducted to test and validate the capability of the proposed integrated approach in solving the voltage unbalance problem. The results have revealed the supreme ability of the proposed integrated approach to restore the voltage balance quickly (within 20 ms) and for a wide range of voltage unbalance factors (VUFs) (3.90–8.42%). [ABSTRACT FROM AUTHOR]

This paper studies the multiple geosynchronous spacecraft refueling problem (MGSRP) with multiple servicing spacecraft (Ssc) and fuel depots (FDs). In the mission scenario, multiple Ssc and FDs are parked in the geosynchronous Earth orbit (GEO) initially. Ssc start from FDs and maneuver to visit and refuel multiple GEO targets with known demands. These capacitated Ssc are expected to rendezvous with fuel-deficient GEO targets and FDs for the purpose of delivering the fuel stored in FDs to GEO targets. The objective is to find a set of Pareto-optimal solutions with minimum fuel cost and mission duration. The MGSRP is a much more complex variant of multidepot vehicle routing problems mixing discrete and continuous variables. A two-nested optimization model is built. We propose a new multiobjective hybrid particle swarm optimization to solve the outer-loop problem, and the design variables are the refueling sequence, task assignment, time distribution, and locations of FDs. In the inner-loop problem, branch and bound method is used to find the optimal decision variable for a given outer-loop solution. Finally, numerical simulations are presented to illustrate the effectiveness and validity of the proposed approach. [ABSTRACT FROM AUTHOR]

The effectiveness of a photovoltaic (PV) system can be increased by using maximum power point tracking (MPPT). The literature has suggested a number of methods for tracking the maximum power point (MPP). However, this number of methods most often presents a high convergence speed in reaching the MPP, complexity under their implementation, power fluctuations, overshoots, and great difficulty in reaching the MPP under fast-changing atmospheric conditions, thus influencing the efficiency of PV systems. Intending to improve the performance of PV systems under rapid changes in the atmosphere, this paper proposes model reference adaptive control (MRAC) as a technique for tracking the MPP based on the employ of reference models such as optimal voltage and current at the MPP ( V MPP and I MPP ). The MATLAB/Simulink environment is used to produce the simulation results; the Kyocera Solar KC 130 GT module is used here as a photovoltaic power plant, connected to a boost converter, supplying a resistive load. The Lyapunov theory was used to demonstrate the stability of the system. The simulation outcomes obtained using the suggested method are compared with those obtained by techniques such as perturb and observe (P&O), incremental conductance (INC), variable step incremental conductance (VSINC), particle swarm optimization (PSO), and grey wolf optimization (GWO), thus showing a very large improvement under standard test and fast-changing atmospheric conditions of the technique proposed on the other techniques in terms of convergence speed and tracking efficiency. The simulation results prove that the suggested method has great tracking effectiveness (>99.88%), less time for convergence (<0.01 s), and simple implementation complexity under fast-changing atmospheric conditions without both transient and steady-state power oscillations, overshoots, and chattering effects, thus causing a great minimization of energy losses, and the proposed technique reaches exactly the MPP under fast-changing atmospheric conditions. [ABSTRACT FROM AUTHOR]

This paper studies the general SoC design AMBA bus and proposes an automatic generation method of software structure test data based on adaptive data optimization algorithm. By simplifying the basic particle expansion equation and eliminating the particle velocity term, an adaptive scheme based on inertia weight is proposed. In the research process, this article fully considers heterogeneous groups. Investment companies are divided into three types of decision-makers: reciprocal, intelligent, and leveraged, and their investment behavior is modeled. Given that factors such as technical level will affect the future of the project, and swarm simulation software is used to simulate and analyze the impact of the profitability of smart community microgrid construction projects on decision-making and to conduct dynamic research on market modeling risks. This article first outlines the construction of the renewable energy macro- and micro-market risk decision-making behavior model and clarifies the logic process of the market to promote the consumption of renewable energy. Then, it analyzes the causes of market risks based on dynamic models, first examines the relationship between key risks and risk factors, forms risks that affect returns (bilateral random risks and market efficiency risks), and then an analysis framework for the impact of renewable energy. This paper applies it to the analysis of data-based algorithms, thereby promoting the development of data-based algorithms. [ABSTRACT FROM AUTHOR]

In order to improve the communication efficiency of the CAN bus application layer communication protocol of the inverter network monitoring system, this paper proposes a TTCAN scheduling optimization algorithm for the robot inverter remote monitoring system based on PLC and cloud platform. According to the analysis of the communication requirements of the frequency converter monitoring system, this paper designs the application layer protocol of the nodes in the system and establishes the system scheduling matrix. The time triggered can bus protocol (TTCAN) combines the event triggered mechanism with the time triggered mechanism. The hybrid scheduling strategy is used to optimize the system matrix of TTCAN; that is, the hybrid particle swarm optimization algorithm is used for periodic messages. For nonperiodic messages, uniform scheduling strategy and dynamic scheduling algorithm are adopted. The simulation is carried out by MATLAB tools. The simulation results show that the sum of partial minimum transmission time is 1928 and there are multiple optimal individuals through the hybrid particle swarm optimization algorithm. Compared with the traditional genetic algorithm and single particle swarm optimization algorithm, the hybrid algorithm is better than the traditional genetic algorithm and particle swarm optimization algorithm in terms of iteration times and average fitness value. In conclusion, the optimized TTCAN protocol improves the real-time performance, reliability, and bandwidth utilization of the communication network. [ABSTRACT FROM AUTHOR]

Since the 1990s, emerging market financial crises have occurred frequently, causing huge damage to the real economy, and if we cannot find effective means of early warning and prevention of financial crises, the entire international economy and society will bear the high costs of crisis management. Difference nonparametric test and Spearman nonparametric correlation analysis were carried out with cash flow financial data, and 14 financial indicators with strong discriminant ability were selected from 28 financial indicators as the input variables of the model. Due to the limitations of traditional statistical methods, a BP neural network financial distress early warning model is established. Finally, a particle swarm optimization BP neural network financial early warning model is established for the shortcomings of the BP network. These 14 indicators can have strong information timeliness. The prediction accuracy rates of the two early warning models for the test samples are 80% and 85%, respectively. The empirical results show that the two models have good prediction effects. The prediction effect of the swarm optimization BP neural network model is better than that of the BP neural network model. Therefore, the particle swarm optimization BP neural network model proposed in this paper is suitable for solving the problem of discrimination and prediction of the financial distress of enterprises. The company's financial distress early warning has good application prospects and application value. Therefore, it has very important research significance for the early warning of the corporate financial crisis. [ABSTRACT FROM AUTHOR]

The application of artificial intelligence in the field of education is becoming more and more extensive and in-depth. The intelligent education system can not only solve the limitations of location, time, and resources in the traditional learning field but it can also provide learners with a convenient, real-time, and interactive learning environment and has become one of the important applications in the Internet field. Particle swarm optimization (PSO) is a swarm intelligence-enabled stochastic optimization scheme. It is derived from a model of bird population foraging behavior. Because of its benefits of ease of implementation, high accuracy, and quick convergence, this algorithm has gained the attention of academics, and it has demonstrated its supremacy in addressing real issues. This paper aims to study the optimization of PSO in the field of computational intelligence, improve the matching degree of learning resource recommendation and learning path optimization, and improve the learning efficiency of online learners. This paper suggests intelligent education as the center, takes the PSO algorithm as the main research object, and expounds the related concepts of intelligent education and PSO algorithm. It uses swarm intelligence algorithms for intelligent education personalized services. He focuses on PSO algorithm and its work in intelligent education recommendation and learning path planning. Experiments show that the average value of the difference between the two obtained by the NBPSO algorithm is 1.13E + 02 and the variance 1.88E + 02 is the smallest. Therefore, PSO aids in improving the quality and consistency of online course resource development. In conclusion, the research results of this paper further demonstrate the advantages of PSO algorithm in solving the problem of personalized service in intelligent education. It can promote the in-depth application of swarm intelligence optimization algorithms in intelligent online learning systems. This effectively enhances the intelligent service level of the online learning system and increases the efficiency of online learning. [ABSTRACT FROM AUTHOR]

The automobile Antilock Braking System (ABS) can prevent the wheel from locking by automatically adjusting the brake pressure at a high speed during emergency braking. While improving the braking effect of the car, it can also keep the steering ability of the car and ensure the safety of passengers. The automobile braking process has a strong nonlinearity and uncertainty, so exploring a simple and reliable control strategy is the focus of current research. Based on this, this paper proposes a variable-domain fuzzy proportional integral differential (PID) control strategy using a particle swarm optimization (PSO) algorithm to iteratively find the optimal theoretical domain. First, the PSO strategy is used to obtain the optimal regulation parameters. Then, the dynamic antiinterference ability of the control system is guaranteed by the variable theory domain fuzzy PID control, and the PID parameters and variable theory domain expansion factor are optimized by the PSO to increase the utilization degree of fuzzy rules initially set. Finally, compared with the traditional control strategy, the simulation and real vehicle test prove that the proposed system can significantly improve the control accuracy of abs. The proposed system has the advantages of small overshoot, short adjustment time, strong antiinterference ability, and practicability, which greatly improves the tracking performance of the ABS. [ABSTRACT FROM AUTHOR]

With the continuous development of e-commerce, logistics and express services have penetrated into every aspect of people's life. Research on the optimization of logistics network model is helpful to reduce the waste of routes, improve the utilization rate of transportation tools and hubs, and thus reduce the organizational cost of logistics. In this paper, the basic model of hub-and-spoke network (HSN) is constructed based on the principle of minimizing the connection distance and total cost between hubs. By discretizing the particles in the continuous motion space, the discrete particle swarm optimization (PSO) algorithm is designed and the Exchange () exchange function is used to improve its search strategy to update the individual optimal value and the global optimal value. Finally, the improved double-layer discrete PSO algorithm is obtained to solve the logistics network model. The results show that the optimized PSO algorithm has faster convergence speed and higher precision, and the application of the logistics network model is helpful to integrate logistics resources, reduce logistics costs, and improve logistics efficiency. [ABSTRACT FROM AUTHOR]

Each Chinese region has its own ancient opera, which is a treasure of folk culture and a living fossil for studying the historical origins of a local culture. It has significant academic value and historical significance at the national and local levels, whether from the perspective of promoting and disseminating national culture or from the perspective of protecting the world's intangible cultural heritage. Based on this practical significance, this paper conducts a study using the particle swarm algorithm to manage the marketing archives of drama intangible cultural heritage. The article employs the PSO algorithm to test the particle swarm optimization algorithm's convergence and conditions. The effectiveness of the algorithm is analysed in the Sphere function, Rosenbrock function, Griewanks function, and Rastrigin noncont function, and then the algorithm is compared, including the calculation speed comparison between the algorithm in this paper and the three optimal fitness functions. The experimental results show that the PSO algorithm has the highest four items in the statistics of the Schwefel function experimental results. About 45.0379 is the best value and 70.5878 is the maximum precision. The optimal average value is 6.1524, while the average value is 56.15245. In comparison to the QPSO and PSO algorithms, the algorithm in this paper has a faster convergence speed and better search accuracy. The topic of the intersection of the disciplines of drama, intangible cultural heritage marketing, and archive management using the particle swarm algorithm is well-developed. [ABSTRACT FROM AUTHOR]

For the development of nanotechnology, nanomaterials are in an important position. Due to their special structure and a series of unique properties, nano-metal oxide materials are widely used in nanoelectronics, catalysis, environmental monitoring, electrochemical sensors, and many other fields. Rice, as the most important food crop in the world, has a wide range of planting areas. Therefore, in this paper, the research on the construction of rice transgenic vectors is based on nano-metal oxide materials. In this paper, particle swarm optimization is used to optimize the weights of rice-growing convolutional neural networks. Secondly, it used rice as the test plant to study the effects of seven metal oxide nanomaterials on rice seed germination and root elongation. This sheds light on the toxic effects of metal oxide nanomaterials on rice growth and discusses the factors that affect the phytotoxicity of nanomaterials. The experimental results show that before optimization, the accuracy of the small batch sample test is 87.0456%, the accuracy of the large batch sample test is 93.5876%, and the accuracy of the test is increased by 6.542%. After optimization, the accuracy of the small batch sample test is 93.5876%, the accuracy of the large batch sample test is 99.0133%, and the accuracy of the test is increased by 5.4466%. The inhibition rates of nano-CuO and ZnO on rice root length were 97.28% and 66.75%. [ABSTRACT FROM AUTHOR]

Writing is an important part of testing language ability, and it is urgent to find some objective indicators to determine and evaluate the surface language structure, which will help language learners' better master the target language. Complexity and semantic coherence are considered to be an important factor in the teaching of second language writing. In practice, due to the complexity of English writing syntax, such as a large number of high-dimensional nonlinear optimization problems, a new intelligent evaluation method is needed to solve them. At present, particle swarm optimization (PSO) has been widely used in function optimization, neural network training, combinatorial optimization, and other fields. This paper studies the syntactic complexity and semantic coherence of academic English writing based on PSO. The number of phrases is related to writing achievement. When the number of experiments reaches 25, the significant values of syntactic complexity and semantic coherence of data mining algorithm, artificial intelligence algorithm, decision tree algorithm, and PSO algorithm are 0.008, 0.003, 0.002, and 0.013, respectively, which shows that PSO algorithm is the best among them. [ABSTRACT FROM AUTHOR]

With the gradual maturity of the development of scientific and technological modernization, China's economic level has risen rapidly, and people's living conditions have been further improved. Emerging technologies such as artificial intelligence, Internet of things, and big data have brought new opportunities for China's urban construction and landscape design. Facing the development of modern science and technology, the application of landscape design in rural construction has become the main research object. In this paper, particle swarm optimization algorithm is used to study the modernization of rural landscape design. Starting with intelligent technology, particle swarm optimization algorithm is used to improve the traditional model of rural landscape design. Firstly, the control range of rural landscape lighting system is divided into regions, the user needs are processed, and the intelligent system of landscape lamps is further adjusted. It improves the problems of energy conservation and environmental protection in rural landscape design. Finally, based on the particle swarm optimization neural network supervision algorithm, the rural planning land is allocated intelligently. The intelligent design of sprinkler irrigation equipment in landscape architecture can adjust the sprinkler area and range according to the growth period of crops and achieve the purpose of real-time monitoring. [ABSTRACT FROM AUTHOR]

In recent years, effective recognition and accurate assessment of psychological stress have been the focus of research. Because of the objectivity and authenticity of physiological signals, psychological stress recognition from physiological signals has become an important research content in the field of psychological stress recognition. As an important physiological signal, electrocardiogram has been proved to contain reliable physiological response to psychological stress. This paper designs a psychological stress analysis algorithm based on particle swarm optimization (PSO). The wavelet transform algorithm was used to filter and detect the ECG signal. RR interval was calculated from the detected R wave to obtain the ECG signal. An improved particle swarm optimization (PSO) algorithm was proposed, which introduced a particle swarm optimization model with contraction factor to eliminate the speed limit and realize the detection of psychological stress. Experimental results show that the recognition rate of the improved particle swarm optimization algorithm is significantly higher than that of the traditional method, which shows the effectiveness of the algorithm. On the one hand, the research of this paper has optimized the algorithm, which has theoretical significance; on the other hand, it can provide reference for the real psychological stress test, which has practical significance. [ABSTRACT FROM AUTHOR]

In order to improve the accuracy of network security situation prediction and the convergence speed of prediction algorithm, this paper proposes a combined prediction model (EMD-ELPSO-BiGRU) based on empirical mode decomposition (EMD) and improved particle swarm optimization (ELPSO) to optimize BiGRU neural network. Firstly, the network security situation data sequence is decomposed into a series of intrinsic mode function by EMD. Then, a particle swarm optimization algorithm (ELPSO) based on cooperative update of evolutionary state judgment and learning strategy is proposed to optimize the hyper-parameters of BiGRU neural network. Finally, a network security situation prediction model based on EMD-ELPSO-BiGRU is constructed to predict each intrinsic mode function, respectively, and the prediction results are superimposed to obtain the final network security situation prediction value. Simulation results show that ELPSO has better optimization performance, and EMD-ELPSO-BiGRU model has higher prediction accuracy and significantly improved convergence speed compared with other traditional prediction methods. [ABSTRACT FROM AUTHOR]

Art provides a starting point for children to understand works of art. In the initial stage of life, the development of vision and movement is particularly important, and painting helps coordinate the work of these systems. Art dispels the mystery of art and helps children connect with art classics, thus understanding the language and structure of art. In particle swarm optimization, the inertia weight is constant, but with the increase of iteration times, the inertia weight may become larger or smaller, so it tends to search globally or locally. This paper first analyzes what art therapy is from the basic concepts of art and art education and the stage characteristics of children's painting and then studies the application of art therapy based on particle swarm optimization in preschool children's art teaching. Finding the global optimal fitness function in the research process not only simplifies the computational complexity but also approximates the maximum coverage effect provided by the current network. The results show that applying particle swarm optimization algorithm to the research of mental health treatment of preschool children can establish an effective evaluation program in the practical research of children's art treatment. [ABSTRACT FROM AUTHOR]

The stochastic and nonlinear characteristics of electric arc furnaces (EAFs) lead to power quality challenges in the power system. In studying EAF behaviors, having optimized characteristics/models, selecting a suitable and optimum model that adapts to the actual characteristics of EAFs, and investigating simulation software's capability for implementing EAF models are essential. However, the literature shows a research gap in investigating EAF simulations in various software products based on different models. This paper studies several time-domain models, such as piece-wise linear, modified piece-wise linear, hyperbolic, exponential, and exponential-hyperbolic models, for EAF modeling and simulation. The optimal estimation of parameters for the introduced models is necessary to adapt actual EAF characteristics. Thus, one of the studies taken in this paper is optimizing the EAF model's characteristics. The proposed optimization problem is solved using the genetic algorithm (GA) and particle swarm optimization (PSO). Moreover, the optimized models are simulated in DIgSILENT and EMTP-RV to investigate different EAF models from the viewpoint of accuracy and efficiency. The optimization of different EAF models' characteristics and comparison of EMTP-RV and DIgSILENT in simulating EAF behavior are the contributions of this paper. The proposed method is validated based on the actual data of a realistic EAF-based steel company in Iran. The obtained results show that the modified piece-wise linear model has the most accuracy in identifying the EAF behavior. The test results based on DIgSILENT and EMTP-RV simulations imply that the EAF could be simulated with high accuracy using modified piece-wise linear and piece-wise linear models. In general, EMTP-RV has expressed more accuracy in simulating different EAF models, and the simulation execution speed of EMTP-RV is around 2.5 times faster than DIgSILENT. In contrast, DIgSILENT is more suitable to facilitate the power system studies of EAF according to its extensive study tools and library. [ABSTRACT FROM AUTHOR]

In this paper, we use a particle swarm optimization neural network algorithm to analyze the teaching data of physical education faculties and evaluate the quality of teaching in physical education faculties. By studying and analyzing the optimization problem of the weight parameters of convolutional neural network training, this paper designs a hybrid algorithm combining the improved PSO algorithm and the traditional gradient descent in the framework of the BP algorithm by using the gradient information of the loss function and the principle of group cooperative search through PSO algorithm. The hybrid algorithm takes the loss function as the objective function, based on the principle of the PSO algorithm, and optimizes the objective function by combining the gradient information of the loss function of the convolutional neural network. The convergence speed and global search ability of the algorithm are effectively improved while ensuring an acceptable increase in computation. The weight values of the three-level indicators of teacher teaching behavior, student learning behavior, and teaching environment relative to the teaching quality of physical education classroom are 0.106, 0.634, and 0.260, respectively, which shows that the dimension of student learning behavior has the highest weight value in the evaluation of physical education classroom teaching quality, followed by teaching environment and finally teacher teaching behavior. Teachers' teaching ability will affect the effect of teaching methods, and the stronger the teaching ability is, the better the selection and utilization of teaching methods can be optimized. [ABSTRACT FROM AUTHOR]

Portfolio optimization is one of the most complex problems in the financial field, and technical analysis is a popular tool to find an optimal solution that maximizes the yields. This paper establishes a portfolio optimization model consisting of a weighted unidirectional dual-layer LSTM model and an SMA-slope strategy. The weighted unidirectional dual-layer LSTM model is developed to predict the daily prices of gold/Bitcoin, which addresses the traditional problem of prediction lag. Based on the predicted prices and comparison of two representative investment strategies, simple moving average (SMA) and Bollinger bands (BB), this paper adopts a new investment strategy, SMA-slope strategy, which introduces the concept of k-slope to measure the daily ups and downs of gold/Bitcoin. As two typical financial products, gold and Bitcoin are opposite in terms of their characteristics, which may represent many existing financial products in investors' portfolios. With a principle of $1000, this paper conducts a five-year simulation of gold and Bitcoin trading from 11 September 2016 to 10 September 2021. To compensate for the SMA and BB that may miss buying and selling points, 4 different parameters' values in the k-slope are obtained through particle swarm optimization simulation. Also, the simulation results imply that the proposed portfolio optimization model contributes to helping investors make investment decisions with high profitability. [ABSTRACT FROM AUTHOR]

Human resources are the most dynamic factor among all productivity factors. The goal of human resource management is to achieve effective and optimal allocation of resources, while the goal of optimal allocation is to promote the realization of corporate goals. At present, the overall domestic human resource management is relatively backward, and most of it is nonstandardized management, and the level of informatization and technology is not high. Therefore, this paper proposes the image model and algorithm research of human resource optimization configuration based on FPGA and microsystem analysis. First, through the data analysis method, in-depth study of traditional resource allocation algorithms: genetic algorithm and particle swarm optimization algorithm, combined with machine learning algorithms, established a human resource optimization configuration model based on FPGA and microsystem analysis, and then optimized the configuration of human resources for the impact The standard deviation and variance measurement of 18 factors of the company, as well as the analysis of the matching degree between employees and positions, and required capabilities. Finally, it is concluded that the sample average is higher and the top three are employees' positive spirit A12, employees' executive ability A15, and the relationship between salary and performance A11. The sample averages are 4.615, 4.522, and 4.479, respectively; standard deviation of the three factors with the smallest values are good values A8, strong employee cooperation ability A17, and perfect reward and punishment system A16. This shows that the above considerations should be considered in the optimization of enterprise human resources. Moreover, the algorithm in this paper is easier to obtain the optimal solution, and it is easier to obtain the optimal allocation result of human resources. [ABSTRACT FROM AUTHOR]

This paper aims to map the task of reliable transmission of wireless sensor networks. At the same time, this paper transforms the mapping problem of wireless sensor networks into a problem of reducing the energy consumption of mapping under many constraints such as reliability and scheduling length and uses discrete particle swarm optimization algorithm to map. For optimization, the algorithm performs iterative calculations to obtain the best mapping node for each operation so that the inertia coefficient of the existing particle swarm optimization algorithm is improved and linearly minimized with the number of iterations. When resource-demanding tasks need to allocate dynamic resources to multiple nodes to complete collaboratively, adding the mapping principle of the nearest node in the discrete particle swarm optimization mapping reduces the energy consumption of communication between tasks. An in-depth analysis of the influencing factors of the ice and snow tourism market shows that the per capita disposable income of urban residents and the number of urban residents have a significant impact on the ice and snow tourism market demand. In addition, regression analysis and demand-based forecasting are important methods to analyze the scale and development trend of tourism. At the same time, it shows an important position in the purpose of urban tourism and regional market share so as to provide a basis for decision-making in tourism destination marketing. This paper mainly studies and analyzes the wireless sensor network and further introduces it into dynamic resource allocation and ice and snow tourism, which can promote the continuous development of dynamic resource allocation and ice and snow tourism. [ABSTRACT FROM AUTHOR]

The implementation of Enhanced Traffic Situational Awareness on the Airport Surface with Indications and Alerts (SURF IA) monitoring in airports improves the safety and efficiency of airport surface operation. Navigation accuracy category for position (NACP), navigation accuracy category for velocity (NACV), and other data are obtained from Automatic Dependent Surveillance-Broadcast (ADS-B) IN, and these data will be the only data source of SURF IA. In this paper, we use the Beidou System (DBS) and Global Positioning Symbol (GPS) dual system as the positioning data source of the ADS-B. In cases where all visible satellites meet the performance requirements of the SURF IA, particle swarm optimization (PSO) and a new particle swarm optimization (APSO) are used to screen the integrated navigation satellites to meet the requirements of SURF IA by using fewer satellites, and the satellite selection ability of the two is compared. The simulation results show that under the same conditions, by using fewer navigation satellites can meet the minimum monitoring performance requirements for the implementation of SURF IA operation. And the star selection ability of APSO is better. This improves the performance of airport surface intelligent monitoring. [ABSTRACT FROM AUTHOR]

As the continous innovation of new media technology, the media environment of the entire society has undergone profound changes. Digital technology has had a profound impact on the way news is disseminated. It has made a significant impact on the collecting, creation, and distribution of news, as well as the way viewers receive it. As a result, the news media's operation and management style is continually modified. However, in the process of news dissemination, the situations involved are complex and changeable, which leads to different digital technology applications. Aiming at different complex situations in news dissemination under the vision of new media art, this work designs a neural network to optimize the distribution for the required digital technology application schemes. The main work of this paper has the following two points. First, it systematically investigates the current research status of news communication based on digital technology and analyzes the research trends of digital technology and news communication in complex contexts under the vision of new media art. Second, a new neural network is proposed for the optimal application of digital technology for news propagation in different complex situations. This neural network uses an improved particle swarm optimization algorithm and an improved network training strategy to improve the BP network, which can effectively solve the shortcomings of the BP network. A large number of experiments have proved the effectiveness and correctness of this method. [ABSTRACT FROM AUTHOR]

With the widespread use of computers and the rapid development of Internet technology, computer application technology has become more and more important in people's work and life. The article mainly studies particle swarm optimization (PSO) and radial basis neural network function (RBF). Particle swarm optimization is an evolutionary swarm intelligence algorithm, such as nonderivative node transfer function or gradient information loss. Because its principle is simple and easy to implement, it can deal with some problems that cannot be solved by traditional methods. It is widely used in neural networks, and has achieved good results in many fields such as network training, performance optimization, and system mismanagement. RBF neural network is a feedforward neural network, which overcomes the shortcomings of traditional neural network learning process that the convergence is highly dependent on the initial value and can only be partially converged. This paper organically combines PSO algorithm and RBF neural network to study the detection and detection of computer network faults. The results show that although the prediction error of the improved network model on the experimental test set is still only 89.3% different from that of the SVM model, its convergence time is reduced to 0.90699885 s, which can effectively detect and identify computer network faults. [ABSTRACT FROM AUTHOR]

The public service system of national fitness is the condition and guarantee for the public to participate in various fitness activities. In order to effectively integrate the existing national fitness service resources and to improve the efficiency of the public service supply, this paper aims to study the informatization of building national fitness public services in smart city under the background of big data. Firstly, we build a public service information platform of national fitness in the smart city, consisting of different modules, and then describe each module in detail. Secondly, we consider different types of fitness projects and establish the database structure model of network resources. Thirdly, we obtain the multitree cascade system of the national fitness and the heterogeneous data model of the national fitness benefit index by using high-dimensional statistical analysis method. Combined with the national fitness behavior, data mining, and support vector machine (SVM), the objective function of data mining statistical decision-making is established. Finally, the well-known particle swarm optimization (PSO) method is used to optimize the target parameters so as to realize the national fitness, big data mining, and feature analysis. The simulation results show that the proposed model has superior performance, as it can realize heterogeneous data mining of the national fitness benefit index. In addition, we quantitatively analyze the promoting effect of the national fitness on the physical quality and health level of the public. [ABSTRACT FROM AUTHOR]

Metaheuristic algorithms are often applied to global function optimization problems. To overcome the poor real-time performance and low precision of the basic salp swarm algorithm, this paper introduces a novel hybrid algorithm inspired by the perturbation weight mechanism. The proposed perturbation weight salp swarm algorithm has the advantages of a broad search scope and a strong balance between exploration and exploitation and retains a relatively low computational complexity when dealing with numerous large-scale problems. A new coefficient factor is introduced to the basic salp swarm algorithm, and new update strategies for the leader position and the followers are introduced in the search phase. The new leader position updating strategy has a specific bounded scope and strong search performance, thus accelerating the iteration process. The new follower updating strategy maintains the diversity of feasible solutions while reducing the computational load. This paper describes the application of the proposed algorithm to low-dimension and variable-dimension functions. This paper also presents iteration curves, box-plot charts, and search-path graphics to verify the accuracy of the proposed algorithm. The experimental results demonstrate that the perturbation weight salp swarm algorithm offers a better search speed and search balance than the basic salp swarm algorithm in different environments. [ABSTRACT FROM AUTHOR]

Summary: With the commercialization of the electric vehicles, the large‐scale lithium‐ion cells as the power of electric vehicles are to be retired. The second‐use of retired cells is of great significance to improve the battery economy. A fast classification and regroup evaluation method of the retired lithium‐ion cells are proposed in this paper to improve the classification efficiency of retired lithium‐ion cells and adapt to the regroup under different conditions. The lithium‐ion cells after being balanced in parallel are charged in series with a constant current. A support vector regression (SVR) model with the parameters optimized by the particle swarm optimization (PSO) algorithm is built for the fast capacity estimation and the error will not exceed 0.3%. Different cells regrouped means different performance. In order to improve the consistency of retired cells and satisfy different using conditions, a Weighted‐K‐means algorithm is proposed in this paper to regroup the cells with the known capacity and internal resistance. The classification method is evaluated by the voltage consistency of cells using different working conditions, which indicates capacity occupied a large proportion can meet the requirement of energy condition meanwhile keep a good consistency. But the resistance will dominate in algorithm under conditions which have requirement for instantaneous power. [ABSTRACT FROM AUTHOR]

Summary: Around the world, automation is transforming work, business, and the economy as one of the Pillars of a smart grid. The vital role behind utilizing the automation system into distribution networks is to achieve grid self‐healing and improve the reliability level. Distributed Automation (DA) can be achieved via Smart secondary substations at each load point with installed automatic sectionalize switches that need a large investment. Therefore, this paper provides a long‐term plan based on a cost/benefit study to define the optimal automation level of Distribution networks constrained with system reliability indices limits. DA planning is categorized as a non‐linear Mixed Integer optimization problem with multi‐dimensions. Hence, it is not possible to apply numerical algorithms to directly search for all possible automation scenarios. To solve that problem; Hybrid Particle Swarm Optimization and Gravitational Search Algorithm (HPSO‐GSA) technique provides a binary‐coded procedure to handle all the controlled variables to provide the best solution within the permissible limits. The effectiveness of the proposed methodology is demonstrated by applying it to RBTS‐Bus 4 standard reliability test system considering uncertainty in Cost Damage Function (CDF), Investment Cost (IC), and Failure Rate (FR). Also, this paper study the effect of change in Depreciation ratio (DR), Load Growth (LG) rate besides; peak Load Change (LC). Finally, a reliability assessment using NEPLAN software is done for the optimal decision to enhance the technical analysis. Moreover, Distributed Generations (DG) has a significant impact on the reliability of distribution networks. Simulation results indicate that the system reliability cost is significantly reduced with adequate system reliability level, with comparative results to those in literary studies. [ABSTRACT FROM AUTHOR]

Sensor deployment is an important issue in wireless sensor network (WSN), which is a typical nonlinear system. Conditions of both area coverage and point coverage should be considered in research studies on sensor coverage. It is generally necessary to ensure high coverage ratio of area when controlling sensor locations, and covering specific point targets to ensure long lifetime is also important sometimes. In current studies, swarm intelligence algorithms such as particle swarm optimization (PSO) are widely used to solve the sensor deployment problem in WSN. In this paper, coverage rate and network life indicators are analyzed comprehensively with establishment of a more general K-coverage model. In related calculation examples with different coverage requirements including target coverage, area coverage, and boundary coverage, several improved algorithms based on PSO are applied to solve the problem in the paper. Simulation results show that the improved algorithms can achieve a good performance and deployment effect. [ABSTRACT FROM AUTHOR]