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This paper presents a comprehensive study and analysis of the intelligent grouping of papers in adult higher education using a random matrix approach. Using the results of random matrix theory on the eigenvalues of the sample covariance matrix, the energy of each subspace is estimated, and the estimated energy is then used to construct a subspace weighting matrix. The statistical properties of the sample covariance matrix eigenvectors are analyzed using the first-order perturbation approximation, and then, asymptotic results from random matrix theory on the projection of the sample covariance matrix signal subspace to the real signal parametrization are used to obtain the weighting matrix based on the random matrix eigenvectors. Dynamic adjustment according to the fitness of individuals in the population is performed to ensure population diversity, while the combination of the small habitat technique can avoid the algorithm from falling into early convergence. The algorithm introduces chaos theory to optimize the population initialization process and uses the dynamic traversal randomness of chaos to select individuals in the population so that the initial population is close to the desired target solution. The design of the fitness function in the genetic algorithm generally maps the objective function of the problem to the fitness function. A good fitness function can directly reflect the quality of the individuals in the group. Based on the in-depth study of the basic attributes of the test questions and the principles of test paper evaluation, the mathematical model and objective function of intelligent paper grouping are determined by the difficulty, knowledge points, and cognitive level of the test questions as the main constraints, and NCAGA is applied to the intelligent paper grouping method, which better completes the intelligent paper grouping session for the computer system architecture course. In the process of designing the intelligent grouping algorithm, for the situations of premature convergence and convergence to locally optimal solutions that easily occur in the traditional genetic algorithm, this paper adopts the approach of adaptive adjustment of crossover probability and variation probability to improve the algorithm and achieves satisfactory results. Based on extensive business research, this paper completes the requirement analysis of the online practice system based on the intelligent grouping of papers and presents the functional design and database design of the key functional modules in the system in detail. Finally, this paper conducts functional tests on the system and analyses the test results. [ABSTRACT FROM AUTHOR]

With the increasing number of college students, the difficulty and workload of ideological and political education are also increasing. For the calculation and analysis of big data, it is most suitable to use data mining algorithm for calculation and research. In order to help college students' ideological and political education to develop and innovate, this paper establishes a new model of college students' ideological and political education combined with particle swarm optimization algorithm to help carry out college students' ideological and political education. The particle swarm optimization algorithm is tested. Therefore, this paper uses data mining algorithm to calculate and analyze the research in this paper. The algorithm is tested and analyzed. Through the test of data mining algorithm, the calculation accuracy of the algorithm is proved, and the feasibility of this paper is proved. This paper studies the ideological and political education by using data mining algorithm. The computational efficiency of the optimized data mining algorithm is twice that of the traditional algorithm, and the optimized algorithm has also been significantly improved. In general, the data mining algorithm optimized in this paper is reasonable, which can improve the calculation accuracy and efficiency of the algorithm. [ABSTRACT FROM AUTHOR]

Finally, the last paper presents a design-task-oriented model assignment framework that involves model value selection, multiobjective model establishment, and multiobjective optimization algorithm. We hope that the papers presented in this Special Issue will be useful and stimulating for further developments and applications of multicriteria and multiobjective models and that we again have been able to highlight the extensive range of contexts over which these methods can be used. This is the Fourth Special Issue (SI) dealing with "Building Mathematical Models for Multicriteria and Multiobjective Applications.". [Extracted from the article]

In recent years, an increasing number of population-based metaheuristic algorithms have been proposed to solve the flexible job-shop scheduling problem (FJSP) according to its practicality and complexity. Most of these algorithms are single-population-based and hence are very susceptible to becoming trapped in local optimal values. Thus, multipopulation methods are widely used to improve these algorithms, resulting in multipopulation algorithms. These multipopulation algorithms have been widely studied in the context of single-layer complex networks recently. However, coupled networks used to control two (or more) algorithms simultaneously to get a better algorithm are always ignored in literature. Therefore, in this paper, using coupled scale-free networks (with different scaling exponents) to control the genetic algorithm (GA) and the invasive weed optimization (IWO) simultaneously, a multipopulation GA/IWO with coupled scale-free networks (MPGAIWO-SF) is proposed to solve FJSP. Then, we study how some parameters (e.g., subpopulation size, subpopulation number, and scaling exponent) of MPGAIWO-SF affect its performance, which is also ignored in literature. The simulation results illustrate that (1) the performance of MPGAIWO-SF is significantly improved compared with that of GA and IWO; (2) as the subpopulation size increases, the performance of MPGAIWO-SF first becomes better and then remains almost unchanged; (3) as the subpopulation number increases, the performance of MPGAIWO-SF first becomes better and then decreases rapidly; and (4) the performance of MPGAIWO-SF becomes worse slightly as the scaling exponent decreases. Finally, solving more FJSP instances, the MPGAIWO-SF with optimized parameters is compared with other related algorithms to verify its effectiveness. [ABSTRACT FROM AUTHOR]

To cope with the challenge of successful edge offloading brought by the mobility of mobile devices in intelligent factories, this paper studies the optimization problem of the edge offloading strategy of mobile devices based on mobility. Considering the decision task flow executed by priority, the unique offloading mode of a single task, the communication range of the edge server, and the delay constraint of the offloading of a single task, appropriate computing resources are selected according to the real-time location of the mobile device to offload the computing task. Based on the edge computing architecture of an intelligent factory, this paper puts forward five different computation offloading methods. From a global perspective, the energy consumption and delay of tasks offloading in local, edge, cloud center, local-edge collaboration, and local-edge-cloud collaboration are considered. In this paper, the algorithm based on the genetic algorithm and particle swarm optimization is used to design and obtain the decision task flow offloading strategy with the lowest energy consumption and delay. Simulation results show that the proposed algorithm can reduce the computation offloading energy consumption and delay of mobile devices. [ABSTRACT FROM AUTHOR]

Research in nature-inspired intelligence methods and applications has increased exponentially in the past decade. In the paper "Multiobjective brain storm optimization community detection method based on novelty search" by Pan et al., a multiobjective brain storm optimization based on novelty search (MOBSO-NS) was proposed to solve the premature convergence caused by the loss of diversity in complex network community detection. Inspired by a natural phenomenon from biology, physics, or sociology, population-based nature-inspired algorithms aim to achieve satisfactory results for many difficult optimization problems effectively. [Extracted from the article]

The convergence performance of existing fractional particle swarm optimization algorithm directly depends on a single fractional-order operator. When its value increases, the convergence speed of particles gets slower. When its value decreases, the probability of the particle swarm trapping into the local optimum increases. In order to solve this problem, an improved fractional particle swarm optimization (IFPSO) algorithm is proposed in this paper. New variables are introduced in this paper to redefine the formula. The fractional-order operator is added to the velocity update formula and the position update formula. The IFPSO algorithm has linearly decreasing inertia weights. Then the proposed IFPSO algorithm is used to optimize the parameters of support vector machine (SVM) and the clustering center of the K-means classifier is selected. Experimental results show that the IFPSO algorithm can effectively avoid falling into the local optimal solution. It has a faster convergence rate and better stability than the original algorithm, which proves the effectiveness of the algorithm. Examples verify that the IFPSO algorithm can improve the classification accuracy of SVM in practical application. The IFPSO algorithm effectively solves the problem that K-means algorithm is overly dependent on the initial center and may have empty classes. [ABSTRACT FROM AUTHOR]

In order to solve the problems of large demand for power business and small number of customer service, an intelligent customer service questioning and answering a system for power business scenario based on AI technology is designed. The approach first uses the particle swarm optimization algorithm to automatically classify the question attributes and then uses the fuzzy c-means clustering algorithm to match the answers with the highest similarity to the questions and return to the customers. The system collects the questions raised by customers through the acquisition module, uploads the question work order to the knowledge base through the information assistance module, and stores the preprocessed questions to the knowledge base. After completing the problem attribute classification through the particle swarm clustering algorithm classification model in the batch analysis and calculation module, the question answers are matched through fuzzy c-means clustering. At the same time, the similarity of different keywords is calculated to find a series of related questions. After the obtained data are analyzed in real time through the self-service customer service module, the answer is extracted and fed back to the customer, and the question answer is presented to the customer in the system interface. The experimental results show that the designed system has low worst-case time complexity, which is up to 0.35 only. The reason is that the system in this paper can use priority information to deal with the problems raised by customers, which is different from the past work where dealing with customers request via priority information is not used. The system can give the corresponding answers according to the customer's options. It has convenient operation, high integrated control ability, and good information management performance. Compared with the traditional approach which could waste a lot of resource and data, the proposed approach can reduce the differences between problem data, eliminate invalid data, and simplify the data classification process. The application of the system can effectively accelerate the information transmission efficiency of the power company and can be used for power exchange platform automation in the future. [ABSTRACT FROM AUTHOR]

The mission planning of active debris removal (ADR) of revolver mode on geosynchronous orbit (GEO) is studied in this paper. It is assumed that there are one service satellite, one space depot, and some pieces of space debris in the ADR mission. The service satellite firstly rendezvouses with the debris and then releases the thruster deorbit kits (TDKs), which are carried with the satellite, to push the debris to the graveyard orbit. Space depot will provide replenishment for the service satellite. The purpose of this mission planning is to optimize the ADR sequence of the service satellite, which represents the chronological order, in which the service satellite approaches different debris. In this paper, the mission cost will be stated firstly, and then a mathematical optimization model is proposed. ADR sequence and orbital transfer time are used as designed variables, whereas the fuel consumption in the whole mission is regarded as objective for optimizing, and a specific number of TDKs is also a new constraint. Then, two-level optimization is proposed to solve the mission planning problem, which is low-level for finding optimal transfer orbit using accelerated particle swarm optimization (APSO) algorithm and up-level for finding best mission sequence using immune genetic (IGA) algorithm. Numerical simulations are carried out to demonstrate the effectiveness of the model and the optimization method. Results show that TDK number influences the fuel consumption through impacting the replenishing frequency and TDK redundancy. To reduce fuel consumption, the TDK number should be optimized and designed with suitable replenishing frequency and minimum TDK redundancy. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) has been used in smart cities, agriculture, weather forecasting, smart grids, and waste management. The IoT has huge potential but needs refinement. The paper focuses on lowering IoT sensor power consumption to improve network life. This work selects the best IoT cluster header (CH) to maximize energy consumption. The suggested technique uses particle swarm optimization (PSO) with artificial neural networks (ANNs). The optimal CH in an IoT network cluster was identified by taking into account the number of active nodes, the load, the residual energy, and the cost function. This work compares the suggested method with artificial bee colony, genetic, and adaptive gravity search algorithms. The hybrid solution beats conventional methods. [ABSTRACT FROM AUTHOR]

Concrete thermal parameters in a natural pouring environment are essential inputs for simulating the temperature field of a concrete dam. This paper proposes a two-stage thermal parameters inversion method for a concrete dam based on optimal temperature measuring point selection to improve the accuracy of parameters. Firstly, a selection method of optimal measuring point for thermal parameters inversion is presented and the temperature response sensitivity of measuring points when the parameters disturb is taken as the critical evaluation index. And then, an inversion model is established based on support vector regression (SVR) and particle swarm optimization (PSO). Finally, the proposed method is applied to the thermal parameter inversion of a concrete dam. The results show that the proposed method is effective for improving the inversion accuracy and obtaining accurate parameters. The average error of the inversion results based on the SVR-PSO model is 28.54% lower than that of the genetic algorithm optimization using a back propagation neural network (BPNN-GA). Besides that, the average error of the inversion results based on the optimal measurement points is 35.57% lower than that of the nonoptimized ones. [ABSTRACT FROM AUTHOR]

Data anomaly detection plays a vital role in protecting network security and developing network technology. Aiming at the detection problems of large data volume, complex information, and difficult identification, this paper constructs a modified hybrid anomaly detection (MHAD) method based on the K-means clustering algorithm, particle swarm optimization, and genetic algorithm. First, by designing coding rules and fitness functions, the multiattribute data is effectively clustered, and the inheritance of good attributes is guaranteed. Second, by applying selection, crossover, and mutation operators to particle position and velocity updates, local optima problems are avoided and population diversity is ensured. Finally, the Fisher score expression for data attribute extraction is constructed, which reduces the required sample size and improves the detection efficiency. The experimental results show that the MHAD method has better performance than the K-means clustering algorithm, the support vector machine, decision trees, and other methods in the four indicators of recall, precision, prediction accuracy, and F-measure. The main advantages of the proposed method are that it achieves a balance between global and local search and ensures a high detection rate and a low false positive rate. [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]

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 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]

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]

The southern Shaanxi Province, China, has abundant iron ore resources due to local ideal metallogenic conditions. The development and utilization of iron ore resources and other related industries serve as important pillars of the regional economy. In this paper, principals and methodology of system dynamics are adopted to develop a system model of interactions of development and utilization of mineral resources, social economy, and ecological environment, using the iron ore reserves in the southern Shaanxi region as a resource base, the regional GDP as a benchmark of regional economic development, and the environmental treatment expenditure as the measurement of the regional environmental protection efforts. The model is performed based on an optimized neural network by the particle swarm optimization algorithm. Through setting variables and parameters for the system and model simulation as well as comparative research, this paper analyzes the interconnectivity and interaction of the three subsystems and explores associated relationships among mineral resources, social economy, and environmental carrying capacity in the southern Shaanxi region. In addition, the present study also proposes advice on the development and utilization of iron ore resources, ecological protection, and high-quality development of the economy in the southern Shaanxi region based on the research results. [ABSTRACT FROM AUTHOR]

Based on the principle of particle swarm algorithm and support vector machine, this article aims to improve the classification performance of college English teaching effect and explores the best support vector machine parameter optimization algorithm to promote college English teaching for the theory and application research of data analysis. First, the advantages and disadvantages of common support vector machine parameter selection methods such as grid search algorithm, gradient descent method, and swarm intelligence algorithm are studied. Secondly, this article has a detailed analysis and comparison of various other algorithms. Finally, the study analyzed the advantages and disadvantages of the quantum particle swarm algorithm, introduced the dual-center idea into the quantum particle swarm algorithm, and proposed an improved quantum particle swarm algorithm. Through simulation experiments, it is proved that the improved quantum particle swarm algorithm is more superior in optimizing the parameters of support vector machine. In general, this paper uses the PSO algorithm to simultaneously solve the SVM feature selection and parameter optimization problems and has achieved good results. Within the scope of the literature that the author has, there is still a lack of work in this area. Compared with the existing algorithms, the algorithm proposed in this paper has stronger feature selection ability and higher efficiency. [ABSTRACT FROM AUTHOR]

In this paper, based on the existing research, the rural talent supply situation is analyzed. Rural talent relative to the existing rural labor force has a higher human capital of the labor force. We also discuss the part of moving workers who can be considered as talents. Financial dimension is mostly through literature collection and statistical data finding and computational calculation, according to the results of communicate indicators, to reflect its performance level. The performance analysis of organizational management dimension and institutional development dimension mainly focuses on qualitative analysis and comparative analysis, and then we summarize and evaluate the performance status of Shanghai basic endowment insurance system for urban and rural residents. Therefore, all countries in the world are constantly exploring and trying to improve the performance management model of their social security institutions. Through the evaluation and analysis of the above four dimensions, it can be found that the overall performance level of urban and rural residential insurance in Shanghai is relatively high, but there are still some problems in the operation process. [ABSTRACT FROM AUTHOR]

With the rapid development of artificial intelligence (AI) concept technology, it promotes the innovation of educational concept. Mostly for the education information analysis in the class of mathematics in the university, it should be based on a big data-driven system to promote the quality of teaching in the classroom. In the method of teaching math in university, teachers should take full advantage of the benefits of a big data-driven system powered by AI, grow a good teaching model for students, promote education through big data, progressively teach students according to their aptitude, develop in a tailored direction, increase teaching quality and effectiveness, and finally create more great talents for our country. For the sake of improving the resource sharing and the management level of the curriculum which teaches advanced knowledge about mathematics teaching, based on a particle swarm optimization algorithm, an advanced math teaching system is proposed in this paper. The fusion model that can be used in the teaching process of math in university is constructed, the adaptive scheduling of the curriculum which teaches advanced knowledge about mathematics auxiliary teaching resources is realized by an optimization algorithm used for fusion particle swarm, the autocorrelation feature of the curriculum which teaches advanced knowledge about mathematics auxiliary teaching resources is extracted, and the adaptive optimization of the curriculum which teaches advanced knowledge about mathematics auxiliary teaching resource fusion is realized by fuzzy correlation feature matching and statistical analysis. In the process of particle swarm optimization, the combination of statistical features is studied and managed, the resource scheduling and information fusion are realized, and the management capability of the curriculum which teaches advanced knowledge about mathematics auxiliary teaching is promoted, and the experimental results demonstrate that the designed system has a high integration of teaching information resources and strong information scheduling ability and improved the management level of the curriculum which teaches advanced knowledge about mathematics complementary teaching. [ABSTRACT FROM AUTHOR]

Due to the large fluctuation of import and export volume and many influencing factors, it is difficult for the general prediction algorithm to obtain more accurate prediction results. To solve this problem, a trade forecasting method based on the Particle Swarm Optimization (PSO) optimization hybrid Relevance Vector Machine (RVM) model is proposed. The method firstly finds out the indexes that affect the import and export trade and extracts the principal factors of the indexes as the input data of the model through principal component analysis. Then, based on the single-kernel RVM model training with multiple different kernel functions, the multikernel weighting method was used to construct the hybrid kernel RVM model according to the prediction error of the single-kernel RVM model. Finally, the parameters of the hybrid kernel model were optimized by PSO to improve the prediction accuracy. Close trade will strengthen the dependence between countries, and under this premise, it becomes particularly important to study the frequency of trade between countries. In this paper, the countries along the Belt and Road are classified by the method of high-order directed graph clustering, and the trade transactions between the countries are represented by a small network subgraph. Then, the 65 trading countries are classified by cutting graph and K-means clustering method, and the classification results are obtained by running relevant codes in MATLAB. The results are represented by the lattice graph. [ABSTRACT FROM AUTHOR]

In cloud computing, a shared, configurable pool of computing resources is made accessible online and allocated to users according to their needs. It is essential for a cloud provider to schedule jobs in the cloud to keep up service quality and boost system efficiency. In this paper, we present a scheduling technique based on modified particle swarm optimization to combat the issues of excessively long scheduling time and high computation costs associated with scheduling jobs in a cloud environment. The modified PSO is used to allocate the jobs to virtual machines in order to minimize the objective function consisting of cost and makespan. The algorithm relies on biological changes that occur in organisms to regulate premature convergence and improve local search capability. The technique is analyzed and simulated using CloudSim, and the simulation results demonstrate that the proposed approach decreases makespan and cost effectively as compared to standard PSO. [ABSTRACT FROM AUTHOR]

The effectiveness of evolutionary algorithms (EAs) such as differential search algorithm (DSA), Real-Coded genetic algorithm with simulated binary crossover (RGA-SBX), particle swarm optimization (PSO), and chaotic gravitational search algorithm (CGSA) on the optimal design of cross-coupled nonlinear PID controllers is compared in this paper. A cross-coupled multivariable PID controller structure for the binary distillation column was developed with two inputs and two outputs. EA simulations are run to lower IAE using two stopping criteria, namely, maximum number of functional evaluations (Fevalmax) and Fevalmax plus PID parameter and IAE tolerance. Over 20 separate trials were used to compare the performances of several EAs using statistical measures such as best, mean, standard deviation of outcomes, and average calculation time. This article presents the design of a cross-coupled nonlinear PID controller using single-objective evolutionary algorithms. Using evolutionary algorithms (EAs) with a multicrossover strategy, the results achieved by various EAs are compared to previously reported results. The results of a multivariable cross-coupled system clearly show that a single-objective nonlinear PID controller performs better. Simulations further show that all four techniques evaluated are suitable for PID controller tweaking off-line. However, only the single-objective evolutionary algorithms are acceptable for online PID tuning due to their higher consistency and shorter computation time. [ABSTRACT FROM AUTHOR]

In this paper, trajectory planning and navigation control problems have been addressed for a mobile robot. To achieve the objective of the research, an adaptive PSO (Particle Swarm Optimization) motion algorithm is developed using a penalty-based methodology. To deliver the best or collision-free position to the robot, fitness values of the all-random-positioned particles are compared at the same time during the target search action. By comparing the fitness values, the robot occupies the best position in the search space till it reaches the target. The new work integrated with conventional PSO is varying a velocity event that plays a vital role during the position acquisition (continuous change in position during the obstacle negotiation with the communication through random-positioned particles). The obstacle-negotiating angle and positional velocity of the robot are considered as input parameters of the controller whereas the robot's best position according to the target position is considered as the output of the controller. Simulation results are presented through the MATLAB environment. To validate simulation results, real-time experiments have been conducted in a similar workspace. The results of the adaptive PSO technique are also compared with the results of the existing navigational techniques. Improvements in results between the proposed navigation technique and existing navigation techniques are found to be 4.66% and 11.30%, respectively. [ABSTRACT FROM AUTHOR]

The reasonable selection of cutting parameters in the machining process is of great significance to improve productivity, reduce production costs, and improve the quality of parts. However, due to the complexity of cutting parameter model optimization, most factories currently use experience or refer to relevant manuals to select the value of cutting parameters in production. In order to avoid and minimize abnormalities, they usually select more experienced and conservative values, and often do not select reasonable cutting parameters, which is not conducive to improving productivity, reducing production costs, and improving the quality of parts. Therefore, the research on cutting parameter optimization has important theoretical value and application value. In this paper, in order to find the optimal cutting parameters, the cutting model is solved by the improved monarch butterfly optimization (IMBO) algorithm, and the optimized cutting parameters are obtained. By establishing the mathematical model of cutting, the constraint conditions of actual machining are introduced into the model. In order to solve the model, some ideas of particle swarm optimization (PSO) and differential evolution (DE) are added to the traditional monarch butterfly optimization (MBO) algorithm. The MBO algorithm is improved to deal with multiobjective optimization problems. The IMBO algorithm is used to optimize the cutting model. The experiment shows that the optimized cutting parameters can significantly reduce production cost and maintain high production efficiency. Compared with NSGA-II algorithm and other swarm intelligence optimization algorithms, it shows that the IMBO algorithm has certain advantages in multiobjective optimization. [ABSTRACT FROM AUTHOR]

In the process of steel bar straightening processing, the straightening rollers are often damaged by fatigue due to uneven force, which causes frequent replacement of the straightening rollers. Therefore, the structural parameters optimization of the steel bar straightening machine is very important to improve the machining accuracy of the straightening machine and prolong the service life of straightening roller. In this paper, an optimization method based on the particle swarm optimization (PSO) algorithm was proposed and used to optimize the structural parameters of the steel bar straightening machine. First, the main parameters that affect the accuracy of the straightening machine were comprehensively analysed, and the optimization range of each parameter was determined. Second, in order to minimize the fluctuation of the contact stress, the objective function was established by fitting the roller spacing, roller diameter, roller number, and so on. Then, the PSO algorithm was used to find the optimal solution of structural parameters. Finally, the proposed structural optimization method was verified in practice and compared with the single variable algorithm. As a result, the straightness of the steel bar is increased by 2.88%, and the total straightening force is reduced by 16.25%, compared with the single variable algorithm. In conclusion, it was demonstrated that the proposed optimization method based on the PSO algorithm is better than the single variable algorithm in optimizing the structural parameters of the steel bar straightening machine. [ABSTRACT FROM AUTHOR]

Electricity has become one of the most essential components of establishing a quality standard of living in any country. Consequently, considerable work has been focused on designing a sophisticated load frequency control (LFC) system. However, in light of limited resources and real-world challenges, computationally based control algorithms that are more effective and less expensive remain critically needed. Thus, this paper employs a modified honey badger algorithm (HBA) in conjunction with the concepts of Lévy flight and inertia weight to optimize the parameters of a new cascaded two-degree-of-freedom fractional-PID structure coupled with a proportional derivative (2DOF + FOPIDN)-PD controller to solve LFC problems in an interconnected power system (IPS) comprising conventional and renewable energy sources (RES). The proposed control technique is applied to a two-area IPS under diverse load conditions and in the presence of nonlinear elements and electronic devices. The proposed method is evaluated with respect to a range of performance metrics, such as settling time, undershoots, and error criteria values. The collective performance of the established control scheme indicated that the suggested control approach provides excellent reliability under various load condition scenarios, sensitivity tests, and perturbations, proving the system's efficacy and dependability. [ABSTRACT FROM AUTHOR]

This paper presents a U-model-based adaptive sliding mode control (SMC) using a deep deterministic policy gradient (DDPG) for uncertain nonlinear systems. The configuration of the proposed methodology consisted of a U-model framework and an SMC with a variable boundary layer. The U-model framework forms the outer feedback loop that adjusts the overall performance of the nonlinear system, while SMC serves as a robust dynamic inverter that cancels the nonlinearity of the original plant. Besides, to alleviate the chattering problem while maintaining the intrinsic advantages of SMC, a DDPG network is designed to adaptively tune the boundary and switching gain. From the control perspective, this controller combines the interpretability of the U-model and the robustness of the SMC. From the deep reinforcement learning (DRL) point of view, the DDPG calculates nearly optimal parameters for SMC based on current states and maximizes its favourable features while minimizing the unfavourable ones. The simulation results of the single-pendulum system are compared with those of a U-model-based SMC optimized by the particle swarm optimization (PSO) algorithm. The comparison, as well as model visualization, demonstrates the superiority of the proposed methodology. [ABSTRACT FROM AUTHOR]

Task scheduling is an essential component of any distributed system because it routes tasks to appropriate resources for execution, such as grids, clouds, and peer-to-peer networks. Common scheduling algorithms include downsides, such as high temporal complexity, non-simultaneous processing of input tasks, and longer program execution times. Exploration-based scheduling algorithms prioritize tasks using a variety of methods, resulting in long execution times on heterogeneous distributed computing systems. As a result, task prioritization becomes a bottleneck in such systems. It is appropriate to prioritize tasks with the shortest execution time using faster algorithms. The genetic algorithm (GA) is one of the evolutionary approaches used to solve complex problems quickly. This paper proposes a parallel GA with a MapReduce architecture for scheduling jobs on cloud computing with various priority queues. The fundamental aim of this study is to employ a MapReduce architecture to minimize the total execution time of the task scheduling process in the cloud computing environment. The proposed method accomplishes task scheduling in two stages: first, the GA was used in conjunction with heuristic techniques to assign tasks to processors, and then the GA was used in conjunction with the MapReduce framework to assign jobs to processors. In our experiments, we consider heterogeneous resources that differ in their ability to execute various tasks, as well as running a job on different resources with varying execution durations. The results show that the proposed method outperforms other algorithms such as particle swarm optimization, whale optimization algorithm, moth-flame optimization, and intelligent water drops. [ABSTRACT FROM AUTHOR]

Dragonfly algorithm (DA) is a recently proposed optimization algorithm based on swarm intelligence, which has been successfully applied in function optimization, feature selection, parameter adjustment, etc. However, it fails to take individual optimal position into consideration but only relies on population optimal position and 5 behaviours to update individual position, leading to low accuracy, slow convergence, and local optima. To overcome these drawbacks, Tent Chaotic Map and Population Classification Evolution Strategy-Based Dragonfly Algorithm (TPDA) is proposed. Tent chaotic map is used to initialize the population, making individuals distributed more uniformly in search space to improve population diversity and search efficiency. Population is classified according to individual fitness value, and different position update methods are adopted for different types of individuals to guide the search process and improve the ability of TPDA to jump out of local optima, thus realizing a balance between exploration and exploitation. The efficiency of TPDA has been validated by tests on 18 basic unconstrained benchmark functions. A comparative performance analysis between TPDA, Particle Swarm Optimization (PSO), DA, and Adaptive Learning Factor and Differential Evolution-Based Dragonfly Algorithm (ADDA) has been carried out. Experimental and statistical results demonstrate that TPDA gives significantly better performances compared with PSO, DA, and ADDA on the average and standard deviation in all 18 functions. The global optimization capability of TPDA on high-dimensional functions and the comparison of the time complexity of TPDA and other swarm intelligence algorithms is also verified in the paper. The results indicate that TPDA is able to perform better on optimizing functions without consuming more computational time. [ABSTRACT FROM AUTHOR]

The shape optimization of developable surfaces is a key and difficult technology in CAD/CAM. This paper studies the shape optimization of generalized developable Bézier-like surfaces using the improved salp swarm algorithm. First, aiming at the problems of slow convergence speed and low calculation accuracy, quickly falling into the optimal local solution for the salp swarm algorithm, and by adjusting the control parameters, the added Morlet wavelet mutation strategy to the individual position updates operation. An improved salp swarm algorithm is proposed, called FMSSA for short. Numerical results show that FMSSA performed the best among 18 (78%) comparisons with other improved SSAs in the CEC2005 test set. In addition, FMSSA serves the best among 16 (69%) comparisons with other algorithms. Therefore, the convergence speed and computational accuracy of FMSSA are improved, and the computational results are significantly better than other intelligent algorithms. Second, according to the duality principle of point and plane, the shape optimization problem of generalized developable Bézier-like surface is transformed into the minimization problem of arc length, energy, and curvature change rate of the dual curve, and three shape optimization models of the developable surface are established. Finally, shape optimization of expandable surfaces is mathematically an optimization problem that the swarm intelligence algorithm can efficiently handle, so FMSSA is used to solve these three optimization models. Numerical experiments show that FMSSA obtains optimal generalized developable Bézier-like surfaces in solving these three optimization models, demonstrating the superiority of the proposed FMSSA in effectively solving the shape optimization models in terms of precision and robustness. [ABSTRACT FROM AUTHOR]

People are the most dynamic factor of productivity, and human resource allocation is both the starting point and the end point of human resource management. In modern enterprises, human resource optimization is the scientific and rational allocation of human resources within the enterprise through certain means and methods. The basic concept of particle swarm optimization (PSO) originates from the study of bird predation. It is an evolutionary computation technique based on the swarm intelligence method, which is similar to genetic algorithms and is a population-based optimization tool. This paper is inspired by the ant colony algorithm and introduces the ant colony pheromone and variation algorithm model into the PSO algorithm for further optimization. The application of this improved particle swarm optimization algorithm to the optimal allocation of human resources recommendations is demonstrated by a real case study. [ABSTRACT FROM AUTHOR]

Accurate identification of permanent magnet flux linkage can effectively improve the control performance of model predictive current control system of permanent magnet synchronous motor (PMSM). This paper reviews the existing identification methods of permanent magnet flux linkage of PMSM first and then proposes an identification method of permanent magnet flux linkage based on an improved unscented particle filter, the problem of particle diversity reduction of the traditional unscented particle filtering algorithm is solved, and the identification performance of permanent magnet flux is improved. Simulation research and experimental verification of the proposed method are carried out, and the simulation and experimental results confirm the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Over the last few years, different contaminants have posed a danger to the quality of the water. Hence modelling and forecasting water quality are very important in the management of water contamination. The paper proposes an ensemble machine learning-based model for assessing water quality. The results of the proposed model are compared with several machine learning models, including k-nearest neighbour, Naïve Bayes, support vector machine, and decision tree. The considered dataset contains seven statistically important parameters: pH, conductivity, dissolved oxygen, Biochemical Oxygen Demand, nitrate, total coliform, and fecal coliform. The water quality index is calculated for assessing water quality. To utilize an ensemble approach, a voting classifier has been designed with hard voting. The highest prediction accuracy of 99.5% of the water quality index is presented by the voting classifier as compared to the prediction accuracy of 99.2%, 90%, 79%, and 99% presented through k-nearest neighbour, Naïve Bayes, support vector machine, and decision tree, respectively. This was further enhanced to 99.74% using particle swarm based optimization. [ABSTRACT FROM AUTHOR]

Plant diseases pose a major challenge in the agricultural sector, which affects plant development and crop productivity. Sugarcane farming is a highly organized part of farming. Owing to the desirable condition for sugarcane cultivation, India stands among the second largest producers of sugarcane over the globe. At the same time, sugarcane gets easily affected by multifarious diseases which significantly influence crop productivity. The recently developed computer vision (CV) and deep learning (DL) models with an effective design can be employed for the detection and classification of diseases in sugarcane plant. The disease detection in sugarcane plant is not accurate in the existing techniques. This paper presents a quantum behaved particle swarm optimization based deep transfer learning (QBPSO-DTL) model for sugarcane leaf disease detection and classification which produces high accuracy. The proposed QBPSO-DTL method is designed and trained for the prediction of diseased leaf images. The proposed QBPSO-DTL technique encompasses the design of optimal region growing segmentation to determine the affected regions in the leaf image. In addition, the SqueezeNet model is employed as a feature extractor and the deep stacked autoencoder (DSAE) model is applied as a classification model. Finally, the hyperparameter tuning of the DSAE model is carried out by using the QBPSO algorithm. For demonstrating the enhanced outcomes of the QBPSO-DTL approach, a wide range of experiments were implemented and the results ensured the improvements of the QBPSO-DTL model. [ABSTRACT FROM AUTHOR]

Since the limitation of carbon emissions, China's photovoltaic (PV) industry has developed vigorously, while some traditional heavy industries have been violently hit. Therefore, the industrial production data exhibits significant nonlinear and complexity characteristics, which may affect prediction accuracy, thus hindering the corresponding department's decision-making. Consequently, a novel structure-adaptive fractional Bernoulli grey model is presented in this paper to surmount this toughie, and the core innovations can be summarized as follows. Initially, a novel time function term is utilized to depict the accumulative time effect, which can smoothly represent the dynamic variations and significantly strengthen the robustness of the new model. Besides, the fractional-order accumulation technique, which could effectively improve the predicting accuracy, is employed in the proposed model. Furthermore, the adaptability and generalizability of the proposed model can be enhanced by the self-adaptive parameters optimized by the Particle Swarm Optimization. For illustration and verification purposes, experiments on forecasting the annual output of Photovoltaic modules in China and the annual output of steel in Beijing are compared with a range of benchmarks, including the classic GM (1, 1), conventional econometric technology, and machine learning methods. And the results confirmed that the proposed model is superior to all benchmark models, which indicates that the novel model is indeed suitable for industrial production forecasting. [ABSTRACT FROM AUTHOR]

Aiming at the problem of alignment deviation between shaft and hole caused by accumulated error in the assembly process of brake disc shaft hole, a trajectory planning method for compensating accumulated error is proposed. First, the path before the error is planned, and then the second compensation path is planned between the error position and the actual target point, so as to realize the accurate assembly of the brake disc. In this paper, the IRB 1410 robot is taken as the research object, and its kinematic model is established by using the improved D-H parameter method. The joint space quintic B-spline interpolation method was used to carry out trajectory planning, and the improved particle swarm optimization algorithm was introduced to solve the optimization. The optimal time and smooth trajectory were expected to be obtained. Then, using MATLAB software to simulate, compared with the nonoptimized trajectory, not only is the trajectory running time reduced from 8.6 s to 6.7 s but also the maximum joint change angle of each joint is reduced, which proves that the algorithm has optimization effect on trajectory running time and stability. Finally, the accuracy verification experiment of the algorithm is carried out, and the error between simulation and experiment is less than 6%, which shows the effectiveness of the method. This research provides a theoretical basis for improving the responsiveness and stability of brake disc assembly. [ABSTRACT FROM AUTHOR]

Pixel-based classification and area measurement play a vital role in satellite image processing. The accuracy in classification and area measurement is required for remote monitoring various regions such as water area, cultivation regions, reservoir water spread, and disease spread in cultivation area. Traditionally, Google Earth Pro-based area measurement has error in area measurement due to curvature nature and irregular land surface. Moreover, exact point identification on land surface on Earth pro is difficult due to frequent changes on the land surface. The problem in the land area measurement is mostly affected due to the man-made changes in that particular land area. In this paper, we solve land area measurement error problem through the automated single and multithresholding pixels of different iterations on the land surface by the BSO algorithm. The land cover region pixel intensity changes on the curvature region of land surface with respect to spatial and temporal variations which are identified through BSO optimization-based image segmentation for exact area measurement. For the experimentation of accurate measurement, land area images such as urban, semiurban, hill, and coastal region from LANDSAT and SENTINEL images for period 2016 to 2019 are taken for the land area measurement study. BSO enhances and segments the land regions such as road, building, water body, vegetation, bare land, hill, and coastal region of about 32% more than particle swarm optimization (PSO) algorithm. Furthermore, the urban land area measurement accuracy increases to about 97% than the irregular land surface area. [ABSTRACT FROM AUTHOR]

Due to the complexity of photosynthesis of greenhouse crops, there are inevitable errors between the established photosynthetic rate model and the actual value, which makes it difficult to guarantee the optimality and even feasibility of the optimal regulation of greenhouse light environment based on the original photosynthetic rate model. To solve these problems, this paper proposes an optimal regulation method of greenhouse light environment considering the error of photosynthetic rate model. The main contributions of this method are as follows: firstly, the photosynthetic rate model is established by using the error compensation extreme learning machine method to reduce the error between the model output and the actual value. Then, the model output is introduced into the optimal regulation problem of greenhouse light environment to form the optimal regulation model of greenhouse light environment after error compensation, and the constrained adaptive particle swarm optimization algorithm is used to solve the optimal regulation model, which ensures the rapid convergence of optimization results, makes the optimization results closer to the actual optimal values, and improves the reliability of optimal regulation of greenhouse light environment. Finally, the effectiveness of the proposed optimal regulation method is verified by experiments. The simulation results also show that the proposed optimal regulation method can give a reasonable light intensity setting value in time under different temperature, humidity, and CO2 conditions inside greenhouse, meet the needs of photosynthesis of actual greenhouse crops, and make the growth of greenhouse crops efficient and stable. [ABSTRACT FROM AUTHOR]

Yarn education is a crucial step in producing high-quality textile end products. By offering quick insights into yarn quality, online yarn testing can reduce latency in critical process control, resulting in the manufacture of higher-quality yarns. This paper proposes the development and application of information resources for the education and teaching of yarn history based on fifth-generation (5G) network technologies. Firstly, the yarn database is preprocessed using greyscale transformation and image filtering methods. Secondly, yarn segmentation is implemented using a Multiresolution Markov Random Field (MRMRF) model. Fourier transform and two-scale attention models are used to extract statistical and relative characteristics, respectively. For classifying the yarn photos, we propose using Improved Support Vector Machine (ISVM) classification. The 5G network has been set up, and data is transferred using the Transmission Control Protocol. To enhance the performance, we propose an Improved Particle Swarm Optimization (IPSO) algorithm. The performance of the suggested methodology is analyzed and compared with existing methodologies using the MATLAB simulation tool. The suggested algorithm's classification performance suggests that the 5G framework could provide an accurate, rapid, reliable, and cost-effective solution to industrial automation. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of the sparrow search algorithm (SSA), such as falling into local optimum and slow convergence speed, an improved sparrow search algorithm based on multimixed strategy (MISSA) is proposed in this paper. In the initial stage, the iterative chaotic mapping is used to initialize the population in order to improve the diversity of population. In the foraging stage, the golden sine algorithm and nonlinear convergence factor strategy are introduced to optimize the discoverer-follower model, which make search process more comprehensive and extensive for the discoverer. The elite opposition-based learning strategy is used to update the optimal solution and the population obtained in each iteration to improve the self-learning ability of the algorithm. To verify the rationality of the multimixed strategy selection and efficiency of the proposed algorithm, MISSA is compared with three derived single-strategy improved algorithms, other improved SSAs, and five typical swarm intelligence algorithms using ten basic benchmark functions and CEC 2014 function. The optimization results, diversity analysis, and Wilcoxon rank-sum test results certify that the proposed MISSA has better optimization accuracy, convergence speed, and robustness than other compared methods. Moreover, the practicability and feasibility of MISSA are verified by solving the traveling salesman problem (TSP). [ABSTRACT FROM AUTHOR]

The economic development in China has brought about the urban traffic problems such as traffic congestion, long traffic waiting time, and inappropriate vehicle transfer. Therefore, under the technical background of China's Internet of Things platform, the fusion technology based on big data is studied and an algorithm for Chinese urban intelligent traffic safety scheduling is designed. In this paper, first of all, the urban traffic safety big data is clustered. Secondly, the gray distribution model of the big data is established by extracting the association rule features. Thirdly, the elements in Chinese urban traffic safety big data are fused, including the text, location, picture, audio, and video. On the condition of meeting highly time-sensitive needs of urban traffic intelligence, the video information after data fusion is applied to detect traffic flow parameters, so that an evaluation strategy for urban traffic safety state under the urban traffic speed dispersion is established. According to the fuzzy value of urban traffic drivers' satisfaction with the waiting time, the effect of traffic dispatching is measured, the convergence formula of urban traffic in the morning and evening peaks is constructed, and the optimal solution of the objective function of dispatching strategy is calculated by the particle swarm optimization algorithm. In this way, more efficient urban traffic safety scheduling in China is realized. As can be learned from the experimental results, the proposed algorithm can reasonably judge the urban traffic safety situation and reduce the time of waiting for urban vehicles with reasonable data fusion results, so it is proved to improve the urban traffic safety. [ABSTRACT FROM AUTHOR]

Customers nowadays rely heavily on online reviews when making buying decisions. Various internet websites, including Amazon, Yelp, Google Plus, BookMyShow, Facebook, Twitter, and others, allow users to generate massive amounts of data. The information is gathered through feedback/reviews, comments, and tweets. Companies can leverage this information to improve the quality of their products. Spam reviews are created pretentiously by some businesses and people to promote or degrade the popularity of any product, organization, or person due to their reliance on these online reviews. Thus, identifying spam or nonspam review by the naked eye is nearly impossible. Classifying the reviews manually is also highly speculative. Hence, to overcome this issue, a fitness-based Grey Wolf Optimizer (FGWOK) clustering method is proposed in this paper to identify spam reviews. The fitness-based grey wolf optimization (GWO) is used to obtain the optimal cluster heads in the proposed method. In the fitness-based GWO, the position of the grey wolves is updated in two phases. In the first phase, all the search agents update their position using the contemporary GWO, and in the second phase, the fitness is evaluated to update the position. To prove that the proposed strategy is effective, three spam data sets, namely synthetic spam reviews, movie reviews, and Yelp hotel and restaurant reviews, have been used in our work. The reported results are compared with the existing state-of-art metaheuristic clustering methods like a genetic algorithm (GA), differential evolution (DE), particle swarm optimization (PSO), cuckoo search (CS), k-Means, and artificial bee colony clustering (ABCK) method. The experimental and statistical analysis results show that the proposed FGWOK algorithm outperforms current methodologies. [ABSTRACT FROM AUTHOR]

Teaching-Learning-Based Optimization (TLBO) algorithm is an evolutionary powerful algorithm that has better global searching capability. However, in the later period of evolution of the TLBO algorithm, the diversity of learners will be degraded with the increasing iteration of evolution and the smaller scope of solutions, which lead to a trap in local optima and premature convergence. This paper presents an improved version of the TLBO algorithm based on Laplace distribution and Experience exchange strategy (LETLBO). It uses Laplace distribution to expand exploration space. A new experience exchange strategy is applied to make good use of experience information to identify more promising solutions to make the algorithm converge faster. The experimental performances verify that the LETLBO algorithm enhances the solution accuracy and quality compared to original TLBO and various versions of TLBO and is very competitive with respect to other very popular and powerful evolutionary algorithms. Finally, the LETLBO algorithm is also applied to parameter estimation of chaotic systems, and the promising results show the applicability of the LETLBO algorithm for problem-solving. [ABSTRACT FROM AUTHOR]

Optimizing the allocation of higher education resources and improving the utilization efficiency of educational resources are of great significance to further promoting the balanced development of higher education quality. In order to rationalize and maximize the efficiency of the allocation of innovation and entrepreneurship education resources in colleges and universities and build an evaluation index system for the input and output of educational resources, this paper proposes to use the improved cytogenetic algorithm educational resources for resource allocation and utilization. The adaptive constraint processing technology is combined with the cytogenetic algorithm to avoid the algorithm falling into local optimum. Our method has achieved good results on high-dimensional functions. The utilization efficiency and allocation efficiency of innovation and entrepreneurship education resources in colleges and universities have increased by 21.33% and 18.92%, respectively, and tend to be in a balanced state, which can optimize the allocation of educational resources. [ABSTRACT FROM AUTHOR]

At this stage, the varieties and specifications of aluminum profiles continue to increase, and the scope of application continues to expand. The extrusion forming process is the key technology to realize the production of aluminum profiles, and its technological level determines the quality of the profile products and the service life of the mold. However, in actual production, the quality of the die is difficult to guarantee, and it takes multiple die trials and die repairs to produce qualified products. The numerical simulation technology is used to simulate the actual extrusion process, which can track and describe the flow behavior of the metal in real time, reveal the real flow law of the metal, predict the possible defects of the profile during the extrusion process, and adjust the mold structure and process parameters in time. It can not only improve the quality of extruded profiles, but also reduce production costs and shorten the mold production cycle. Based on this, this paper established an extrusion die optimization design model integrating finite element simulation technology and artificial intelligence algorithm. The prediction models were established with the mean square deviation of the section velocity, the maximum extrusion force and the maximum temperature of the section, respectively, and the optimal design of the structure was realized by using the particle swarm algorithm. The experimental results show that the model in this paper can realize the function well, which verifies the validity of the model. [ABSTRACT FROM AUTHOR]

Economic load dispatch should be given special care even when the primary responsibility of any demand response model is to provide a consistent supply to the load. Demand can be satisfied by the utility grid as well as self-sustaining user sources. If a user generates excess power after meeting demand, the user can pool it and transfer it to the grid or neighboring consumers. This is referred to as the prosumer model, in which the user serves as both a producer and a consumer. Furthermore, some of the surplus power may be stored in energy storage devices. A sophisticated mathematical model is required to estimate how much power should be generated, pooled, pulled from the grid, gathered from close users, supplied to nearby customers, and so on. This paper tries to present a smart economic load dispatch model for a demand response system that combines a multithreaded swarm model with a reward-based reinforcement system to assure optimal source selection and power flow management. To identify the optimum cost-effective power sharing model among a user, the grid, and neighboring users, the system uses particle swarm optimization (PSO) and artificial bee colony (ABC) optimization. Both models have benefits and drawbacks, and not all models work well with all data input. Using two models at the same time consumes a significant amount of time and computational power. As a consequence, for each data input, an upper bound confidante (UBC) model is used in parallel to select the best economical swarm model based on a semisupervised reinforcement model. A weighted Boruvka's algorithm based on transmission line cost and transmission loss is being used to construct an optimum economic power sharing model, which is backed by swarm models. The efficiency of each model is evaluated using the same data for both models, and error analysis is performed. It was discovered that each model performs differently for various data, and creating a reinforced multithreaded model helps to increase accuracy, reduce computing time, and improve efficiency. [ABSTRACT FROM AUTHOR]

Biomedical image analysis based on metaheuristic algorithms is one of the most important research areas encountered in recent years. Due to the low contrast differences between the diseased areas and the image background in high-contrast biomedical images, effective methods are required to diagnose diseases with high accuracy. To overcome the difficulties encountered in this field, metaheuristic approaches may offer effective solutions due to their advantages such as the ability of converging to the global optimum, higher convergence rate, and having few control parameters. In this work, Jellyfish Search (JS), Marine Predators (MPA), Tunicate Swarm (TSA), Mayfly Optimization (MA), Chimp Optimization (ChOA), Slime Mould Optimization (SMA), Archimedes Optimization (AOA), and Equilibrium Optimizer (EO) algorithms, which are the most recently proposed metaheuristic algorithms in the literature, have been improved as clustering based in order to achieve vessel segmentation with high precision. Also, a detailed performance comparison of these algorithms has been realized for the rate of convergences, error values reached, CPU time, standard deviation, sensitivity, specificity, accuracy, F-score, and Wilcoxon rank sum-test. In order to present the compatibility of the results obtained with the literature, the performances of these novel algorithms have also been compared to that of Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Differential Evolution (DE) algorithms. The simulation results represent that each algorithm produces similar convergence and error performance. Also, it can be emphasized from the statistical analyses that the stability and robustness of each metaheuristic approach are quite adequate in separating the vessel pixels and the background pixels of a retinal image. In general, this paper proves that although having fewer number of control parameters, the JS, MPA, TSA, MA, ChOA, SMA, AOA, and EO algorithms produce similar but a bit better results in terms of image segmentation when compared to PSO, GWO, and DE algorithms. [ABSTRACT FROM AUTHOR]

Aiming at the problem of insufficient peak shaving capacity of the power system after large-scale renewable energy such as wind power and photovoltaics is connected to the grid, a new energy-pumped storage combined system low-carbon economic operation strategy is proposed. First, the energy storage characteristics of pumped storage are studied; wind power and photovoltaic output are shifted in time and space; and a new energy-pumped storage combined system model with photovoltaics, wind power, and pumped storage as the main body is established. Second, the carbon trading mechanism was introduced, and the reward and punishment ladder carbon trading cost model was established. Then, via the new energy-pumped storage combined system, the upper optimization target with the smallest investment cost and the lower optimization target with the largest system benefit are established, and a two-layer optimization model solution method based on linearly decreasing inertia weight particle swarm optimization and sequential quadratic programming algorithm is proposed. Finally, via the operating data of a pumped storage power station on an island in northern China, the new energy-pumped storage combined system low-carbon economic operation strategy was simulated and verified. The simulation results show that the low-carbon economic operation strategy of the new energy-pumped storage combined system proposed in this paper can effectively suppress load fluctuations, reduce the impact of wind and wind anti-peak regulation characteristics on the power grid, increase the utilization rate of renewable energy, and improve system economy. [ABSTRACT FROM AUTHOR]

To promote the effect of variational mode decomposition (VMD) and further enhance the recognition performances of bearing fault signals, genetic algorithm (GA) is applied to optimize the combination of VMD parameters in this paper, and GA-VMD algorithm is put forward to improve the decomposition accuracy of VMD. In addition, combined with the center frequency, a feature extraction method based on GA-VMD and center frequency is proposed to ameliorate the difficulty of bearing fault feature extraction. Firstly, the bearing signal is decomposed into a series of intrinsic mode components (IMFs) by GA-VMD. Then, the Center Frequency of IMFs is extracted, and the recognition rate is calculated by k-nearest neighbor (KNN) algorithm. Simulation signal experiments state clearly that, compared with manual parameter setting-VMD algorithm and parameter optimization VMD algorithm based on particle swarm optimization (PSO), the decomposition result of GA-VMD has a smaller root mean square error and higher decomposition accuracy, which verifies the effectiveness of GA-VMD. The experimental results demonstrate that, by comparison with the feature extraction method based on envelope entropy, the feature extraction method based on center frequency has better inter class separability and higher mean recognition rate (the highest recognition rate of single feature is 94.5%, and in the case of multiple features, the recognition rate reaches 100% when four features are extracted) and can realize the accurate identification of different bearing fault signals. [ABSTRACT FROM AUTHOR]