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In order to improve the autonomy and intelligence of group animation behavior, this paper proposes a particle swarm optimization algorithm for automatic control of group animation behavior. After the user inputs the group animation behavior construction target, the individual path is planned according to the fixed behavior rules, and the particle swarm optimization algorithm and the improved artificial fish swarm optimization algorithm are used to select the optimal action for the individual in the group animation according to the top-down control process. The experimental results show that after the PSO converges to the global optimal solution, the evolution times of the PSO converge to the global optimal solution again when the surrounding environment changes 10 times. The population diversity of particle swarm optimization algorithm is obviously better than those of the other two algorithms in the process of automatically controlling the movement of individuals to the circle and to the stand in the crowd animation. Compared with the original algorithm AFSA, the improved artificial fish swarm algorithm proposed in this paper is easier to obtain the global optimal value, and the optimization speed is also improved. In conclusion, the example analysis verifies that the algorithm can effectively realize the automatic control of group animation behavior and has high convergence speed and high autonomy. [ABSTRACT FROM AUTHOR]

In the current complex cyber environment, malicious attacks take various forms and constantly change. To overcome the problems of low detection efficiency and high false alarm rate in traditional intrusion detection techniques, a new method of network intrusion detection combining the variable scale chaos bat (MSCB) algorithm and BP neural network (BPNN) called MSCB-BPNN is proposed in this paper. In the proposed approach, a new MSCB algorithm is proposed to improve the BPNN by optimizing its thresholds and weights so as to prevent it from falling into local optimum. To verify the practical classification performance of the proposed method for intrusion detection, some experiments are carried out. In these experiments, the proposed approach is compared with BPNN, SA-BPNN, and GA-BPNN on the benchmark intrusion detection datasets KDD cup 99 and UNSW-NB15. The experimental results show that the accuracy of the proposed method can reach 99.4% and 99.8% accuracy on the KDD cup 99 training and test sets, respectively, while 89.0% and 93.9% accuracy on the UNSW-NB15 training and test sets, respectively, which are higher than BPNN, SA-BPNN, and GA-BPNN. Furthermore, the precision and recall ratio of MSCB-BPNN are also superior to other approaches. Thus, the proposed model has significant advantages over BPNN, SA-BPNN, and GA-BPNN methods. [ABSTRACT FROM AUTHOR]

In order to improve the effect of agricultural machinery task scheduling, this paper starts from the perspective of multiobjective optimization to achieve task scheduling based on multiobjective particle swarm optimization algorithm. The position of each particle is a combination of resource options for each construction activity, and the displacement range and speed range of the particles are determined. Moreover, this paper uses the method of introducing an external storage library to store the current noninferior solutions and uses the adaptive grid method and the roulette selection method to select the global optimal solution of the particles. In addition, this paper proposes a task scheduling algorithm suitable for modern agricultural machinery based on the actual needs of current agricultural machinery task scheduling. The experimental results show that the agricultural machinery task scheduling algorithm based on multiobjective optimization proposed in this paper has a good agricultural machinery task scheduling effect and meets the basic purpose of optimizing the algorithm in this paper. [ABSTRACT FROM AUTHOR]

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]

Physical exercise for college students is an important means to build a healthy standard of college students and an important way to a healthy campus. In addition to creating good physical fitness, physical exercise has significant effects on improving psychological stress and alleviating psychological problems and mental illnesses among college students. It is important to predict and analyze the physical exercise behavior of college students and explore the positive value of physical exercise for college education. In order to overcome the problem of low accuracy of traditional algorithms in prediction, this paper uses the improved gray wolf algorithm (IGWO) and support vector machine (SVM) for predictive analysis of college students' physical exercise behavior. A nonlinear decreasing convergence factor strategy and an inertia weight strategy are introduced to improve the gray wolf optimization algorithm, which is used to determine the SVM parameters for the purpose of improving the model accuracy. Then, the college students' physical exercise data are input into the model for validation. By constructing a campus behavior data set of college students and conducting experiments, the algorithm achieves 90.45% behavior prediction accuracy, which is better than that of typical prediction models. Finally, individual growth monitoring of college students is targeted to warn students with abnormal behaviors. At the same time, the higher-order information such as physical exercise behavior habits of college students is explored to provide meaningful reference for constructing personalized training. [ABSTRACT FROM AUTHOR]

In recent years, the incidence of diabetes has been increasing year by year. Since most of the fundus lesions are located near blood vessels, the image information is complex, and the end vessels are difficult to identify. So, a new segmentation method of diabetic retinal vessel images based on particle swarm optimization and salp swarm algorithm is proposed. This paper uses a Gaussian filter to enhance the main blood vessels, and a top-bot hat transform is used to strengthen the end vessels. The preprocessing process is completed by combining and reconstructing the two images through a normalization operation. The improved particle swarm optimization and salp swarm algorithms perform multi-threshold segmentation on the preprocessed vessel images. The best fit value, Structural Similarity Index Measure, Peak Signal to Noise Rati, feature similarity index measure, sensitivity, accuracy, regional consistency, Dice coefficient, Jaccard similarity, and Shannon entropy are selected for comprehensive evaluation and analysis. The results showed that this paper's improved particle swarm-salp swarm algorithm for segmenting diabetic retinal blood vessel images is more efficient, and the threshold is better. The vascular segmentation method in this paper is applied in medical image processing, which improves the accuracy of medical image processing and reduces the computational effort. [ABSTRACT FROM AUTHOR]

Aiming at the resource optimization problem in the cooperative detection task, the objective function is constructed based on the channel capacity, and the artificial bee colony (ABC) algorithm is improved to realize the joint optimization of the UAV swarm trajectory and radiation power. Firstly, a multiple input multiple output (MIMO) cooperative detection model is constructed. Then, based on the perspective of information theory, the channel capacity of the cooperative detection model is derived and used as the objective function for optimizing the detection resources of UAV swarm. Then, the factors affecting the objective function are sorted out and analyzed one by one, and the constraints are clarified. Aiming at the shortcomings of ABC algorithm, its search strategy and parameter optimization method are improved. A joint optimization process of UAV swarm trajectory and radiated power based on improved ABC algorithm is constructed. Finally, through simulation verification and algorithm comparison, it shows that the algorithm in this paper can improve the perception ability of cooperative detection of UAV swarm. [ABSTRACT FROM AUTHOR]

Knowledge map is a new method of knowledge management with the information revolution. This paper is aimed at forming a systematic and standardized huge redundant knowledge structure, which can be used to mine the knowledge structure and the relationship between knowledge and visualize it in a graphical way, in order to obtain more representative information and improve the classification accuracy of text classification model. In this paper, a knowledge map construction method based on the Text CNN algorithm is proposed for the subject of Nautical English. It is of practical significance and academic value to make use of knowledge map to study Chinese Maritime English, which is helpful to the development of Chinese Maritime English and provides guidance. In order to maintain the diversity of particle swarm optimization, the Text CNN algorithm is combined with the construction of Maritime English subject knowledge map, and the network parameters and structure are optimized. Using knowledge map to study China Maritime English has important practical significance and academic value and has certain guiding significance for the development of China Maritime English. [ABSTRACT FROM AUTHOR]

Chinese traditional culture is a typical "moral culture," and traditional morality is the core and essence of culture. However, in recent years, the phenomenon of following the trend of rural construction has been particularly serious, and many villages have lost their original features. To solve the above problems, the genetic algorithm can be used to further explore the traditional culture of urbanization construction. A genetic algorithm is a natural evolutionary process that imitates natural selection and genetic operation in nature to obtain optimal solution, in which genetic operation mainly includes the processes of gene replication, crossover, and mutation. This paper studies the traditional culture of urbanization construction based on the genetic algorithm under the concept of environmental protection. Among the accuracy of urban construction land expansion, in 2018, the accuracy of ant colony algorithm, data mining algorithm, and particle swarm optimization algorithm is 58%, 51.8%, and 56.7%, respectively. The accuracy of this genetic algorithm is as high as 58.8%. It can be seen that the genetic algorithm in this paper has the highest accuracy in the expansion of urban construction land. Therefore, in the process of large-scale urbanization based on the genetic algorithm, we should pay attention to not being separated from traditional culture, not letting farmers lose their regional culture, local culture, and grassroots culture, and protecting the cultural-ecological environment on which these cultures depend. [ABSTRACT FROM AUTHOR]

This paper is concerned with the design of a dual-loop control system for permanent magnet synchronous motor (PMSM). An improved linear extended state observer (LESO) with excellent estimation capability is employed to develop an improved linear active disturbance rejection control (LADRC) suitable for PMSM speed regulation, achieving outstanding disturbance suppression in PMSM speed control. The use of an internal model control scheme to initialize the parameters of the proportional-integral- (PI-) based current controller simplifies the search space of the control system parameter optimization. An improved particle swarm optimization (PSO) algorithm is applied to optimize the controller parameters, thereby enhancing the overall system performance. Finally, through a series of simulations and experiments, we validate that our proposed controller exhibits superior performance compared to some other control methods. [ABSTRACT FROM AUTHOR]

In order to explore a quantitative and multiobjective optimization method of land use spatial allocation, this paper proposes a multiobjective algorithm for urban land spatial layout optimization. In this paper, the optimal multiobjective particle swarm optimization (MSO) algorithm is used to construct the optimal land use allocation model, and the variation characteristics of the optimized land use allocation scheme in quantity structure and spatial layout are analyzed. The results show that the total running time of the MSO model and the ordinary genetic algorithm spatial optimal allocation model is 8.57 h and 3.31 h, respectively, and the running efficiency of the mosolua model is 61.38% higher than that of the ordinary genetic algorithm spatial optimal allocation model. The configuration was optimized by using the model of land use spatial pattern from the plaque compactness, adjacency, aggregation degree, environmental compatibility, and the overall degree of resource-saving and environmental friendliness than the ordinary genetic algorithm model of optimal configuration results, and the model of overall fitness model compared with the ordinary genetic algorithm improved by 12.57%. [ABSTRACT FROM AUTHOR]

The paper proposed a new algorithm to solve the Multiskill Resource-Constrained Project Scheduling Problem (MS-RCPSP), a combinational optimization problem proved in NP-Hard classification, so it cannot get an optimal solution in polynomial time. The NP-Hard problems can be solved using metaheuristic methods to evolve the population over many generations, thereby finding approximate solutions. However, most metaheuristics have a weakness that can be dropping into local extreme after a number of evolution generations. The new algorithm proposed in this paper will resolve that by detecting local extremes and escaping that by moving the population to new space. That is executed using the Migration technique combined with the Particle Swarm Optimization (PSO) method. The new algorithm is called M-PSO. The experiments were conducted with the iMOPSE benchmark dataset and showed that the M-PSO was more practical than the early algorithms. [ABSTRACT FROM AUTHOR]

In this paper, a cognitive electronic jamming decision-making method based on improved Q -learning is proposed to improve the efficiency of radar jamming decision-making. First, the method adopts the simulated annealing (SA) algorithm's Metropolis criterion to enhance the exploration strategy, balancing the contradictory relationship between exploration and utilization in the algorithm to avoid falling into local optima. At the same time, the idea of stochastic gradient descent with warm restarts (SGDR) is introduced to improve the learning rate of the algorithm, which reduces the oscillation and improves convergence speed at the later stage of the algorithm iteration. Then, a cognitive electronic jamming decision-making model is constructed, and the improved Q -learning algorithm's specific steps are given. The simulation experiment takes a multifunctional radar as an example to analyze the influence of exploration strategy and learning rate on decision-making performance. The results reveal that compared with the traditional Q -learning algorithm, the improved Q -learning algorithm proposed in this paper can fully explore and efficiently utilize and converge the results to a better solution at a faster speed. The number of iterations can be reduced to more than 50%, which proves the feasibility and effectiveness of the method applied to cognitive electronic jamming decision-making. [ABSTRACT FROM AUTHOR]

Summary: Automatic generation control (AGC) plays a crucial role in modern power system to control the frequency with change in power demand. A novel fractional order (FO) hybrid PID‐fuzzy‐PID (FO‐hPID‐FPID) controller is proposed in this paper as the secondary controller for AGC of a three unequal area interconnected nonlinear reheat thermal power system considering generation rate constraint. This paper also deals with the implementation of FO fuzzy PID (FOFPID) and FO PID (FOPID) controllers in addition to the proposed controller along with their counterpart integer order controllers to establish the dominance of FO controllers over integer order controllers. Various parameters of the controllers are optimized by hybridizing two global optimization techniques: particle swarm optimization and modified sine cosine algorithm (MSCA) and the optimization techniques so obtained is called as hybrid PSO‐MSCA (hPSO‐MSCA) technique. A step load perturbation of 0.01 p.u. is injected in area‐1, and the system response is investigated independently with different controllers. It is revealed that the proposed FO‐hPID‐FPID delivers superior performance than the other controllers. Sensitivity analysis for parametric variations is carried out, and random load test is performed to validate the robustness of the proposed controller. Further, it is observed that the controllers deliver result in an ascending order of control performance as PID, FOPID, FPID, FOFPID, hPID‐FPID and FO‐hPID‐FPID controllers with FO‐hPID‐FPID controller delivering the best result and the FO controllers deliver better control performance than their integer order counterparts. [ABSTRACT FROM AUTHOR]

Summary: This paper presents an optimal parameter identification strategy of the lithium‐ion (Li‐ion) battery model applying a recent metaheuristic artificial ecosystem‐based optimization (AEO) algorithm, which proves its ability in terms of both convergence speed and complexity. The key idea is to update the battery model parameters using the optimizer outputs. In the current paper, the battery model is based on the Shepherd model. To demonstrate the superiority of the suggested method of identification, the test results are compared in terms of efficiency, convergence speed, and accuracy of identification with those obtained by the salp swarm algorithm, the political optimizer, the equilibrium optimizer, and particle swarm optimization. Through the optimization procedure, the undetermined parameters of the battery model are employed as decision variables, but the root‐mean‐square error between estimated data and battery data is assigned to be an objective function must be minimal. The results showed the superior identification ability of the AEO compared to the other optimizers. This optimizer achieved 99.9% identification efficiency, which makes it an ideal solution for battery identification. Besides its identification efficiency, the AEO is much faster than the other optimizers, as the results show. [ABSTRACT FROM AUTHOR]

In order to deeply understand the current situation, problems, and satisfaction of undergraduate education courses in Contemporary Colleges and universities, and further improve the teaching level of professional courses, first, this paper makes an in-depth investigation on the teaching status and satisfaction of undergraduate education majors in two colleges and universities. The results show that the teaching and satisfaction of undergraduate education majors are at a medium level (m = 2.27). On this basis, combined with the existing problems, combined with the SVM parameter optimization algorithm of improved machine learning and particle swarm optimization algorithm, this paper puts forward the optimization strategy of undergraduate education curriculum teaching reform. [ABSTRACT FROM AUTHOR]

Due to continuous urban sprawl, large-scale bus network design has become a major challenge in urban transport planning. The continuous increase in urban population and scale makes the factors considered in the urban route network design increasingly complex. Contemporary public transportation network design problems are based more on efficiency goals such as the accessibility and comfort of the transportation network, which increases the difficulty of analyzing the problem. Bus network design is not only an NP-hard (nondeterministic polynomial) problem but also a multivariable and multiobjective problem. This paper focuses on the bivariate and multiobjective bus network design problem of route generation and station selection. This paper proposes an algorithm called the Pseudo Force Field. By combining the idea of Particle Swarm Optimization (PSO) and the properties of the force field, a feasible route generation scheme is provided for the design of the bus network. The algorithm does not need to determine the end station and has a high degree of completion of the demand. This solves the problem of the selection of terminal stations in large-scale road network design. On this basis, the article combines Genetic Algorithm (GA) and Pareto frontier to provide a new route optimization algorithm and proves the effectiveness of the algorithm. The model has achieved theoretical results in the design of the bus route network in the megacity of Shenzhen, China. [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]

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]

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]

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]

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]

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]

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]

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]

Summary: Optimal allocation of measurement devices is a necessity in order to carry out state estimation of a distribution system. In this paper, the placement problem of power measurement devices is modeled using a multi‐objective method. The objectives of the problem are to minimize measurement devices' costs while increasing the accuracy of state estimation and improving the state estimation quality. Also, operational priorities are considered as another objective, which are based on power losses, lines' capacities, the number of lines connected to a specific line, and the change in lines' flows direction. A multi‐objective evolutionary algorithm based on decomposition (MOEA/D) is used to optimize the allocation of measurement devices within the problem of distribution system state estimation. The state estimation problem is optimized by particle swarm optimization (PSO) algorithm and the Monte Carlo simulation is used to develop some conditions within the network to guarantee the robustness of the proposed method. The method is tested by simulation results on an IEEE 33‐bus and IEEE 123‐bus radial network. [ABSTRACT FROM AUTHOR]

The photovoltaic(PV) system's efficiency can be improved with the aid of effective solar PV cell modeling. However, flawed solar cell parameters affect PV cell modeling incorrectly. The manufacturers are usually not providing all the necessary data for the exact modeling of PV cells. It is therefore necessary to efficiently predict the parameters of the PV cell. The literature discusses various optimization algorithms, but the suboptimal outcomes are obtained by most of the algorithms due to their convergence toward local minima. This paper introduces a new stochastic optimization algorithm for the estimation of solar PV cell parameters. Therefore, for the evaluation of the solar cell, a new novel chaotic algorithm is introduced in this paper called the chaotic tunicate swarm algorithm (CTSA). The proposed algorithm has a new function called an outstanding mathematical model of adaptive weights to stimulate negative and positive inputs from the spreading wave in order to find the best way to connect food with an excellent exploitation tendency and exploratory ability. For the double‐diode model, the computation time of the chaotic variants (CTSA1‐1.1885, CTSA2‐1.2502, CTSA3‐1.2962, CTSA4‐1.1204, and CTSA5‐1.2214) is better than the rest of the compared algorithms (particle swarm optimization [PSO]‐17.4532, tunicate swarm algorithm (TSA)‐2.5278, Harris hawks optimization [HHO]‐10.8942, ant lion optimizer [ALO]‐15.0214, and atom search optimization [ASO]‐9.5214). For the triple‐diode model, the computation time of the chaotic variants (CTSA1‐1.2658, CTSA2‐1.3520, CTSA3‐1.3056, CTSA4‐1.2290, and CTSA5‐1.3859) is better than the rest of the compared algorithms (PSO‐18.5421, TSA‐3.2568, HHO‐9.8754, ALO‐13.5841, and ASO‐11.8412). The superiority of proposed algorithm is established using ranking test, statistical error analysis, and sensitivity temperature variation. [ABSTRACT FROM AUTHOR]

Aiming at the problems of low detection rate and high false detection rate of intrusion detection algorithms in the traditional cloud computing environment, an intrusion detection-data security protection scheme based on particle swarm-BP network algorithm in a cloud computing environment is proposed. First, based on the four modules of data collection, data preprocessing, feature selection, and intrusion detection, the overall framework of the intrusion detection model is constructed by designing corresponding functions. Then, by introducing the decision tree algorithm, the overfitting is reduced and the data processing speed of the model is improved, and on this basis, the feature selection is carried out through the "gain rate" optimization method, which reduces the redundant information of the feature vector. Finally, by introducing the Particle Swarm Optimization (PSO) algorithm into the optimization of the initial weights and thresholds of the BP neural network, the BP neural network is improved based on the momentum factor and adaptive learning rate, and the high detection rate and low false detection rate are realized. Through simulation experiments, the proposed intrusion detection method and the other three methods are compared and analyzed under the same conditions. The results show that the detection rate and false detection rate of the method proposed in this paper are the best under five different types of sample data, the highest detection rate reaches 95.72%, and the lowest false detection rate drops to 2.03%. The performance of the proposed algorithm is better than that of the other two comparison algorithms. [ABSTRACT FROM AUTHOR]

The power-voltage (P-V) characteristic curve of the centralized thermoelectric generation (TEG) system under nonuniform temperature distribution (NTD) exhibits multiple extreme point characteristics, and the traditional maximum power point tracking (MPPT) algorithm is prone to fall into the local maximum power point (LMPP) and takes a long time to track. This paper designs a BP-IPSO algorithm based on back propagation neural network (BPNN) and improved particle swarm optimization (IPSO) for MPPT. The algorithm firstly utilizes the good nonlinear function fitting ability of BPNN to obtain the fitting curve of the relationship between system control input and power output to establish the TEG array power prediction model. Then, the dynamic learning factor and weight coefficient are introduced into the traditional particle swarm optimization (PSO) algorithm to search the output power prediction model and realize MPPT control. MATLAB/Simulink experiment results show that BP-IPSO algorithm can effectively avoid falling into LMPP, quickly and accurately track the global maximum power point (GMPP), and effectively suppress the oscillation of voltage and power during the tracking process. Especially in the start-up test experiment, compared with perturb and observe (P&O), PSO, and grey wolf optimizer (GWO), the energy generated by BP-IPSO increased by 12.84%, 3.18%, and 4.75%, respectively, which improved the system power generation efficiency. [ABSTRACT FROM AUTHOR]

Intelligent manufacturing capability evaluation is the key for enterprises to scientifically formulate the implementation path and continuously improve the level of intelligent manufacturing. To help manufacturing enterprises diagnose the level of intelligent manufacturing capability, this paper conducts research on intelligent manufacturing capability maturity evaluation based on maturity theory. The evaluation problem is a complex nonlinear problem, and BP neural network is particularly suitable for solving such complex mapping problems. Aiming at the problem that the BP neural network is sensitive to initial weights and thresholds, the sparrow search algorithm (SSA) is used to optimize the initial weights and thresholds of the BP neural network. In order to overcome the shortcoming of SSA that it is easy to fall into the local optimum, the firefly disturbance strategy is introduced to improve it, a new sparrow search algorithm (FASSA) is proposed, and on this basis, an intelligent manufacturing capability maturity evaluation model based on the FASSA-BP algorithm is constructed. Finally, a large battery manufacturing enterprise in China is selected for empirical research, and the comparison experiments are carried out on the FASSA-BP model, BP model, SSA-BP model, and PSO-BP model in terms of accuracy, stability, etc. The results show that the evaluation of intelligent manufacturing capability maturity through this model can effectively help companies diagnose problems in the construction of intelligent manufacturing and provide a reference for companies to accurately improve their intelligent manufacturing capabilities. [ABSTRACT FROM AUTHOR]

Smart sensor network has the characteristics of low cost, low power consumption, real time, strong adaptability, etc., and it has a wide range of application prospects in the agricultural field. However, the smart sensor node is limited by its own energy; it also faces many bottlenecks in agricultural applications. Therefore, balancing the energy consumption of nodes and extending the life of the network are important considerations in the design of efficient routing for smart sensor networks. Aiming at the problem of energy constraints, this paper proposes an intelligent sensor network clustering algorithm based on adaptive chaotic ant colony optimization (ACACO). ACACO introduces logical chaotic mapping to interfere with the pheromone on the initial path and uses the adaptive strategy to improve the transition probability formula. After selecting the best next hop node, the advancing ants are released to update the local pheromone, and the current pheromone content is adjusted by the chaos factor. When the ants determine the path, they release subsequent ants to update the global pheromone. The simulation results show that ACACO has obvious advantages over genetic algorithm (GA) and particle swarm optimization (PSO). [ABSTRACT FROM AUTHOR]

Aiming at low efficiency of agricultural machinery scheduling, this paper proposes an adaptive immune‐following algorithm (AIFA) based on immune algorithm and artificial fish swarm algorithm. The adaptive crossover operator is used to accelerate convergence, and adaptive mutation operator ensures good diversity of population. After the adaptive evolution operations are performed, the following operator based on the following behavior of artificial fish swarm algorithm is embedded into the algorithm, which improves the convergence precision and obtains the promising optimization results. Experiments on scheduling considering the breakdown of agricultural machinery are performed based on multiple regions and multiple agricultural machineries. Compared with the immune algorithm and genetic algorithm, the simulation results demonstrate that AIFA can converge faster and achieve a better optimal solution. [ABSTRACT FROM AUTHOR]

Water level (WL) forecasting has become a difficult undertaking due to spatiotemporal fluctuations in climatic factors and complex physical processes. This paper proposes a novel hybrid machine learning model based on an artificial neural network (ANN) and the Marine Predators algorithm (MPA) for modeling monthly water levels of the Tigris River in Al-Kut, Iraq. Data preprocessing techniques are employed to enhance data quality and determine the optimal input model. Historical data for water level and climatic factors data are utilized from 2011 to 2020 to build and assess the model. MPA-ANN algorithm's performance is compared with recent constriction coefficient-based particle swarm optimization and chaotic gravitational search algorithm (CPSOCGSA-ANN) and slime mold algorithm (SMA-ANN) to reduce uncertainty and raise the prediction range. The finding demonstrated that singular spectrum analysis is a highly effective method to denoise time series. MPA-ANN outperformed CPSOCGSA-ANN and SMA-ANN algorithms based on different statistical criteria. The suggested novel methodology offers good results with scatter index (SI) = 0.0009 and coefficient of determination (R2 = 0.98). [ABSTRACT FROM AUTHOR]

The New Caledonian crow learning algorithm (NCCLA) is a novel metaheuristic algorithm inspired by the learning behavior of New Caledonian crows learning to make tools to obtain food. However, it suffers from the problems of easily falling into local optima and insufficient convergence accuracy and convergence precision. To further improve the convergence performance of NCCLA, an improved New Caledonian crow learning algorithm (INCCLA) is proposed in this paper. By determining the parent individuals based on the cosine similarity, the juveniles are guided to search toward different ranges to maintain the population diversity; a novel hybrid mechanism of complete and incomplete learning is proposed to balance the exploration and exploitation capabilities of the algorithm; the update strategy of juveniles and parent individuals is improved to enhance the convergence speed and precision of the algorithm. The test results of the CEC2013 and CEC2020 test suites show that, compared with the original NCCLA algorithm and four of the best metaheuristics to date, INCCLA has significant advantages in terms of convergence speed, convergence precision, and stability. [ABSTRACT FROM AUTHOR]

Order picking, which is collecting a set of products from different locations in a warehouse, has repeatedly been described as one of the most laborious and time-consuming internal logistic processes. Each order is issued to pick some products located at given locations in the warehouse. In this paper, we consider an order picking problem, in which a number of orders with different delivery due dates are going to be retrieved by a limited number of order pickers in multiperiods such that the total tardiness is minimized. The aim is to determine a retrieval plan in terms of order batching and order picker multitrip routing as decision variables. Besides, products are arrived and replenished at the predetermined locations at different periods. Therefore, products sitting in those locations should be delivered soon to provide empty rooms for replenishment. A mixed integer linear programming formulation is proposed for this new problem. The model is optimally solved for small-size problems. For larger instances, grouping metaheuristic algorithms are proposed based on particle swarm optimization and the league championship algorithm that use group-based operators to generate reasonable batches of orders. Improvement heuristics are designed as well. The performance of the MILP formulation and metaheuristic algorithms is analyzed for different problem instances whose designs are based on real data gathered from an auto parts warehouse. Results indicate that our algorithms can stably solve large instances of the problem in a reasonable time. [ABSTRACT FROM AUTHOR]

Accurate wind power forecasting can help reduce disturbance to the grid in wind power integration. In this paper, a short-term power forecasting model is established by using complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) and nonlinear fitting characteristics of support vector machines (SVM) to accurately predict wind power. First, the wind power data are preprocessed and decomposed to 6 stable power components using CEEMDAN, thus reducing the impact of excessive forecasting errors of oscillatory points at peaks and valleys. Then, particle swarm optimization (PSO) based on improved empirical mode decomposition is designed to optimize the kernel function and penalty factor of the SVM. It establishes a new short-term power forecasting CEEMDAN-combined model. Finally, each stable component data is processed using the power forecasting model, and then, the results are combined to get the final power forecasting value. Analysis of test results and comparative studies show that the RMSE and MAPE of the new model are only one-third of that of the traditional SVM algorithm. The forecasting accuracy and speed meet the requirements for safe operation of wind farms. [ABSTRACT FROM AUTHOR]

Pure electric vehicles provide an enticing ecofriendly alternative to traditional fossil fuel combustion engine locomotives. Batteries have primarily been used to store energy in electric vehicles; however, peak load demand and transient power leading to decreased battery lifespan have bred interest in hybrid energy storage systems in electric vehicles. Management of energy drawn from a hybrid energy storage system (HESS) in electric vehicles is a real-time multistage optimization problem aimed at minimizing energy consumption while aptly distributing energy drawn from the battery and capacitor to enhance the battery life cycle. This paper explores the feasibility of a master-slave salp swarm optimization algorithm (MSSSA) (metaheuristic algorithm) in a HESS control strategy for electric vehicles. Introducing a master-slave learning approach to the salp swarm algorithm (SSA) improves its performance by increasing its convergence rate while maintaining a balance between exploration and exploitation phases of the algorithm. A comparison of the MSSSA results with the SSA (salp swarm algorithm), DA (dynamic algorithm), WOA (whale optimization algorithm), MFO (moth flame optimization algorithm), GA (genetic algorithm), and PSO (particle swarm optimization algorithm) on benchmark test functions and dynamic program simulation of an electric vehicle's HESS control strategy and shows preeminence of the MSSSA control strategy for HESS. [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]

Aiming at the shortcomings of standard particle swarm optimization (PSO) algorithms that easily fall into local optimum, this paper proposes an optimization algorithm (LTQPSO) that improves quantum behavioral particle swarms. Aiming at the problem of premature convergence of the particle swarm algorithm, the evolution speed of individual particles and the population dispersion are used to dynamically adjust the inertia weights to make them adaptive and controllable, thereby avoiding premature convergence. At the same time, the natural selection method is introduced into the traditional position update formula to maintain the diversity of the population, strengthen the global search ability of the LTQPSO algorithm, and accelerate the convergence speed of the algorithm. The improved LTQPSO algorithm is applied to landscape trail path planning, and the research results prove the effectiveness and feasibility of the algorithm. [ABSTRACT FROM AUTHOR]

When using Gaussian process (GP) machine learning as a surrogate model combined with the global optimization method for rapid optimization design of electromagnetic problems, a large number of covariance calculations are required, resulting in a calculation volume which is cube of the number of samples and low efficiency. In order to solve this problem, this study constructs a deep GP (DGP) model by using the structural form of convolutional neural network (CNN) and combining it with GP. In this network, GP is used to replace the fully connected layer of the CNN, the convolutional layer and the pooling layer of the CNN are used to reduce the dimension of the input parameters and GP is used to predict output, while particle swarm optimization (PSO) is used algorithm to optimize network structure parameters. The modeling method proposed in this paper can compress the dimensions of the problem to reduce the demand of training samples and effectively improve the modeling efficiency while ensuring the modeling accuracy. In our study, we used the proposed modeling method to optimize the design of a multiband microstrip antenna (MSA) for mobile terminals and obtained good optimization results. The optimized antenna can work in the frequency range of 0.69–0.96 GHz and 1.7–2.76 GHz, covering the wireless LTE 700, GSM 850, GSM 900, DCS 1800, PCS1900, UMTS 2100, LTE 2300, and LTE 2500 frequency bands. It is shown that the DGP network model proposed in this paper can replace the electromagnetic simulation software in the optimization process, so as to reduce the time required for optimization while ensuring the design accuracy. [ABSTRACT FROM AUTHOR]

Summary: This paper elaborates a novel optimum programming‐based algorithm that embeds the stochastic multiobjective particle swarm optimization (PSO) method and the deterministic interior point method to calculate the maximum wind power penetration level. With the optimization target of promoting the wind generation capacity, the three‐stage optimization strategy is established to contemplate the transient stability constraint (TSC) as well as the uncertainty factors in the high wind penetrated system. To address the uncertainty factors in system, the chance‐constrained optimization approach is practiced to figure out the initial optimal operating point in the first stage. On the ground of which, the TSC is reinforced in the second stage to delineate the dynamic feasible region. Among the obtained security domain, the ultimate operation solution is calculated in the last stage and provides operators with specific operating scheme. The framework is capable of supporting alternative optimal methods and can be extended to more complex system modeling. The feasibility of the algorithm framework has been demonstrated by simulations on two benchmark systems. And it has prosperous application prospects for optimization problems that need to consider system dynamic security and uncertainty simultaneously. [ABSTRACT FROM AUTHOR]

Contactors are important components in circuits. To solve the multiobjective optimization problems (MOPs) of contactors, a niching multiobjective particle swarm optimization (NMOPSO) with the entropy weight ideal point theory is proposed in this paper. The new algorithm selecting and archiving the nondominated solutions based on the niching theory to ensure the diversity of the nondominated solutions. To avoid missing the extreme solutions of each objective during the multiobjective optimization process, extra particle swarms used to search the independent optimal solution of each objective are supplemented in this algorithm. In order to determine the best compromise solution, a method to select the compromise solution based on entropy weight ideal point theory is also proposed in this paper. Using the algorithm to optimize the characteristics of a typical direct-acting contactor, the results show that the proposed algorithm can obtain the best compromise solution in MOPs. [ABSTRACT FROM AUTHOR]

In order to overcome the defect that sparrow search algorithm converges very fast but is easy to fall into the trap of local optimization, based on the original mechanism of sparrow algorithm, this paper proposes game predatory mechanism and suicide mechanism, which makes sparrow algorithm more in line with its biological characteristics and enhances the ability of the algorithm to get rid of the attraction of local optimization while retaining the advantages of fast convergence speed. By initializing the population with the good point set strategy, the quality of the initial population is guaranteed and the diversity of the population is enhanced. In view of the current situation that the diversity index evaluation does not consider the invalid search caused by individuals beyond the boundary in the search process, an index to measure the invalid search beyond the boundary in the search process is proposed, and the measurement of diversity index is further improved to make it more accurate. The improved algorithm is tested on six basic functions and CEC2017 test function to verify its effectiveness. Finally, the improved algorithm is applied to the three-dimensional path planning of UAV with threat area. The results show that the improved algorithm has stronger optimization performance, has strong competitiveness compared with other algorithms, and can quickly plan the effective and stable path of UAV, which improves an effective method for the application in this field and other fields. [ABSTRACT FROM AUTHOR]

With the rapid increase of the data size, there are increasing demands for feature selection which has been a powerful tool to handle high-dimensional data. In this paper, we propose a novel feature selection of niche particle swarm optimization based on the chaos group, which is used for evaluating the importance of feature selection algorithms. An iterative algorithm is proposed to optimize the new model. It has been proved that solving the new model is equivalent to solving a NP problem with a flexible and adaptable norm regularization. First, the whole population is divided into two groups: NPSO group and chaos group. The two groups are iterated, respectively, and the global optimization is updated. Secondly, the cross-iteration of NPSO group and chaos group avoids the particles falling into the local optimization. Finally, three representative algorithms are selected to be compared with each other in 10 UCI datasets. The experimental results show that the feature selection performance of the algorithm is better than that of the comparison algorithm, and the classification accuracy is significantly improved. [ABSTRACT FROM AUTHOR]

The magnetorheological elastomer (MRE) is a kind of smart material, which is often processed as vibration isolation and mitigation devices to realize the vibration control of the controlled system. The key to the effective isolation of vibration and shock absorption is how to accurately and in real time determine the magnitude of the applied magnetic field according to the motion state of the controlled system. In this paper, an optimal fuzzy fractional-order PID (OFFO-PID) algorithm is proposed to realize the vibration isolation and mitigation control of the precision platform with MRE devices. In the algorithm, the particle swarm optimization algorithm is used to optimize initial values of the fractional-order PID controller, and the fuzzy algorithm is used to update parameters of the fractional-order PID controller in real time, and the fractional-order PID controller is used to produce the control currents of the MRE devices. Numerical analysis for a platform with the MRE device is carried out to validate the effectiveness of the algorithm. Results show that the OFFO-PID algorithm can effectively reduce the dynamic responses of the precision platform system. Also, compared with the fuzzy fractional-order PID algorithm and the traditional PID algorithm, the OFFO-PID algorithm is better. [ABSTRACT FROM AUTHOR]

To improve the machining accuracy and production efficiency of precision components with deep hole structures, an online prediction method of the inner hole roundness error, which cannot be directly measured in real time during the machining process, is proposed in this paper. For online prediction of the workpiece roundness error (WRE) during machining, a predictive model based on correlation analysis and a proportional method is proposed according to the spindle synchronous error motion (SSEM) by three-probe method testing. To improve the prediction accuracy of the WRE, a particle swarm optimization (PSO) algorithm is introduced for optimizing a probe mounting angle of a three-probe method, and a harmonic wavelet method for SSEM feature extraction is proposed. Using the PSO algorithm, the optimal probe mounting angle of the three-probe method is obtained, the influence of spindle surface roundness on SSEM is eliminated, and the higher-order harmonic suppression of the three-probe method is avoided effectively. By the harmonic wavelet method, the accurate SSEM extraction is enhanced and the WRE prediction accuracy is promoted. The experiments show that the inner hole roundness error online prediction method proposed in this paper has high prediction accuracy. [ABSTRACT FROM AUTHOR]

Most of the traditional tourism route planning algorithms only consider single-factor planning, that is, the influence of scenic spots on route planning. Particle swarm optimization algorithm is favored by many people because of its simple concept, easy implementation, and good robustness. Aiming at this problem, this paper takes the actual geographic data as the research object of the tourism route problem and describes the model of the discrete particle swarm algorithm based on geographic coordinates to solve the tourism route problem, which is used to solve practical problems. In order to further improve the global search ability of the algorithm, a self-balancing mechanism is proposed, which makes the algorithm process simple and the algorithm performance improved. At the same time, multithread parallelism is adopted to improve the solution speed of the algorithm, which makes up for the deficiency of the parallelization research of the algorithm. [ABSTRACT FROM AUTHOR]

Neural network algorithms and intelligent algorithms are hot topics in the field of deep learning. In this study, the neural network algorithm and intelligence are optimized, and it is used in simulation experiments to improve the target image recognition ability of the algorithm in the machine vision environment. First, this paper introduces the application of neural networks in the field of machine vision. Second, in the experiment, the improved VGG-16 convolutional neural network (CNN) model is applied to metal block defect detection. Experimental results show that the optimized network can classify metal block defects with the maximum accuracy of 99.28%. Then, the intelligent algorithm based on neural network is studied, and the CIFAR-10 data set is taken as the experimental target for training test and verification test. Using BP algorithm, particle swarm optimization algorithm (PSO-BP), and improved neural network algorithm, respectively, the convergence speed of ICS algorithm based on BP neural network is compared. In contrast, ICS-BP algorithm has the fastest convergence speed and converges when the number of iterations is 32, followed by PSO-BP algorithm. [ABSTRACT FROM AUTHOR]