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Summary: This article proposes a methodology based on artificial intelligence to enhance methane production from waste paper. The proposed methodology combines fuzzy logic‐based modelling and modern optimization. Firstly, a robust Adaptive Network‐based Fuzzy Inference System model of methane production process through fuzzy logic modelling is created using experimental datasets. Second, a particle swarm optimizer was used to obtain the optimal process conditions. During the optimization procedure, the beating time and feedstock/inoculum ratio are employed as decision variables in order to maximize methane production. The obtained resulted from the proposed methodology are compared with those obtained by response surface methodology. The results of the comparison confirmed the superiority of the proposed methodology. The fuzzy model shows a better fitting to the experimental data compared to ANOVA. The fuzzy model showed a higher coefficient of determination and a lower value of root mean squared errors compared to ANOVA. Moreover, the proposed strategy, that is, modelling and optimization, is an effective method for increasing the biomethane yield at extended range conditions. [ABSTRACT FROM AUTHOR]

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]

In recent years, in order to reduce the damage from the blackout accident, the demand of the power system for its basic resistance ability has gradually increased. Therefore, this paper proposes a double‐layer optimization method for the prevention of cascading failure of the power system that takes into account the transient stability constraint for cascading trips that directly lead to the accident. In terms of models, the paper reconstructs an outer model for optimizing the safety margin based on the inner model that solves the safety margin of cascading failure prevention. In terms of algorithms, the paper uses the Extended Particle Swarm Optimization (EPSO) algorithm with higher precision and accuracy to solve the inner model and innovatively introduced the Practical Dynamic Security Region (PDSR), which uses PSASP software for offline calculation and online application, as a dynamic analysis method to quickly realize transient stability constraint to solve the outer model. Three evaluation indexes of the preventive optimization effect can be obtained by this method; dispatchers can plan more flexible and reliable optimal dispatching schemes through these indexes, to comprehensively improve the safety margin of cascading failure prevention in the current operating state and enhance the risk resistance ability of the power grid. This paper has used MATLAB and PSASP to perform simulation analysis using the IEEE39 node system as a test example to verify the effectiveness and superiority of the proposed model and algorithm. [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]

The integration of IoT systems and AI technology is very suitable for interconnected things to enhance intelligence, thus the artificial intelligence of things (AIoT), which enables the establishment of innovative IoT systems and applications for the modern smart city and smart home. The Internet of Things (IoT) consists of interconnected things with built-in and function-oriented sensors, essentially constituting a network of physical devices. The paper titled "Utilizing Artificial Intelligence and Lotus Effect in an Emerging Intelligent Drone for Persevering Solar Panel Efficiency" by F. A. Almalki et al. proposes a drone system with AI framework to help clean solar panels mounted on the top of buildings and enhance their efficiency. [Extracted from the article]

In recent years, in the classic battles and armed conflicts around the world, battlefield environment reconnaissance and the collection and processing of operational information play an increasingly critical role in the victory and defeat of the battlefield. Unmanned equipment, especially UAV equipment, is used by more and more countries in the field of combat reconnaissance. Meanwhile, the types of UAV are gradually diversified with the change of operational requirements. UAVs adapted to different combat environments shine brightly on the battlefield. In terms of naval battle field, due to the limitations and deficiencies of reconnaissance methods such as surface radar, UAVs play a more prominent role in combat reconnaissance. There are more scenarios for UAVs to be used in combat reconnaissance in naval battle field and higher requirements for UAVs' combat effectiveness. Therefore, this paper takes UAVs' naval battle reconnaissance missions as the research object. By using PSO as the research method, this paper studies the combat reconnaissance task configuration of UAVs, hoping to contribute to the improvement of UAVs' combat reconnaissance capability and combat effectiveness. [ABSTRACT FROM AUTHOR]

Traditional logistics delivery route optimization algorithm has some problems such as long time to find the optimal route. Based on this, this paper discusses swarm intelligence optimization algorithm and logistics delivery route optimization. To solve the logistics vehicle routing problem, considering that the basic ACO (ant colony optimization) has the disadvantages of slow convergence speed and easy to fall into local optimum, this paper proposes a new hybrid population optimization algorithm and applies it to VRPTW (vehicle routing problem with time windows). In addition, the concept of crowding degree in AFSA (artificial fish swarm algorithm) is introduced into ACO. In the early stage of the optimization process, a strong crowding degree limit is set to ensure that most ants are not affected by pheromone concentration to conduct random optimization. The simulation results show that the AC (accuracy rate) of this algorithm is 95.08%, which is higher than the traditional PSO (particle swarm optimization) algorithm and general heuristic algorithm. The hybrid algorithm can effectively improve the optimization efficiency of VRPTW, lay a foundation for solving large-scale VRPTW, and provide new research ideas and methods. At the same time, the results fully show that the algorithm in this paper has certain advantages in performance, and it can be applied to logistics delivery route optimization. [ABSTRACT FROM AUTHOR]

With the increasing openness and development of network technology, the network based on the wireless sensor network system has increasingly become an important tool for human social life and production, but it also brings some network security problems. Among them, focus is on network privacy disclosure and foreign intrusion and the research of intrusion detection and privacy protection has increasingly become an important topic of network security. This paper deeply studies the wireless sensor network system based on neural network on the basis of traditional privacy protection and intrusion detection system. Firstly, it applies particle swarm optimization algorithm and constructs a wireless sensor network intrusion detection system based on particle swarm optimization algorithm. The system includes important modules such as data extraction, data analysis, data feedback, and auxiliary decision-making. Compared with other algorithms, particle swarm optimization algorithm does not rely on problem information. It mainly uses real numbers to solve, so the algorithm has strong universality. At the same time, its corresponding principle is simple and easy to implement and less parameters need to be adjusted. Compared with other algorithms, particle swarm optimization algorithm has fast convergence speed and little memory requirement for the computer. At the same time, this paper uses the leap of particle swarm optimization algorithm to make it easier to find the global optimal solution. At the corresponding level of wireless sensor network privacy protection, based on the original data aggregation privacy protection scheme, this paper proposes a privacy protection scheme based on polynomial regression and a user privacy protection scheme based on the same state encryption, which further improves the security of privacy protection and facilitates the management of information. To realize the integrity of user privacy information protection, this paper realizes the decryption of data based on the correlation between binary metadata and compares the corresponding decrypted data with aggregated data, so as to complete the integrity of privacy data protection. The experimental results show that the binary metadata correlation decryption method proposed in this paper and the introduction of the corresponding particle swarm optimization algorithm improve the stability of the system by about 10%, the corresponding system security by a positive proportion, and the integrity of private data by about 10%; therefore, the algorithm proposed in this paper has obvious advantages. [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]

The extraction algorithm of target characteristics resonance set at the high-frequency band plays a significant role in defense applications like early warning. The paper proposes a new method to extract the characteristic resonance set of radar targets in the resonance frequency region using an autoregressive moving average model optimized by root mean square propagation particle swarm optimization. Considering that the total scattering response in the resonant region consists of early-time and late-time responses, the autoregressive moving average model approximate the total scattering responses to avoid errors caused by intercepting the late-time response. Further, the paper investigated the impact of the swarm optimization algorithm on the accuracy of moving average model parameters when obtaining the resonance set at different target aspects. The extracted characteristic resonance results within a range of azimuth directions through an aircraft target paradigm indicate that the new method is more convenient and precise than the matrix beam prediction method. [ABSTRACT FROM AUTHOR]

With the deep integration of science and technology and culture, the estimation of human movements in dance video images will become an important application field of computer vision technology, which can be used not only for professional dancers' movement correction, dance self-help teaching, and other application scenarios but also for athletes' movement analysis. Therefore, it will greatly promote the implementation of teaching students in accordance with their aptitude by applying information technology to estimate dancers' movements and postures in real time and obtaining information of classroom dance teaching status in time. In this paper, human motion recognition in dance video images is studied based on an attitude estimation algorithm. When the number of experiments reaches 20, the average value of deep learning algorithm and particle swarm optimization algorithm is 76.23 and 75.23, respectively, while the average value of attitude estimation algorithm in this paper is 77.95. Therefore, the average results of attitude estimation algorithm in this paper are slightly higher than those of other algorithms. [ABSTRACT FROM AUTHOR]

Slope stability estimation is an engineering problem that involves several parameters. The problems of low accuracy of the model and blind data preprocessing are commonly existent in slope stability prediction research. To address these problems, 10 quantitative indicators are selected from 135 field cases to improve the accuracy of the model. These indicators were analyzed and visualized to examine their reliabilities after preprocessing. Combined with random forest (RF), particle swarm optimization (PSO), and least squares support vector machine (LSSVM) algorithms, a hybrid prediction model that the RF–PSO–LSSVM model is proposed for identifying slope stability, and its reliability is verified by other prediction models that SVM, logistic regression, decision trees, k-nearest neighbor, naive Bayes, and linear discriminant analysis. Besides, the importance score of each indicator in the prediction of slope stability is discussed by employing the RF algorithm. The research results show that the proposed hybrid model exhibits the best accuracy and superiority in slope stability prediction than other models in this paper, which its values of the best fitness, area under the curve, T-measure, and accuracy are 98.15%, 96.4%, 96.55%, and 95.82%, respectively. The most influential factors affecting slope stability are precipitation and gravity, and the slope type and pore water ratio are identified as the least significant factors in this paper. The results provide a novel approach toward slope stability prediction in the field of geotechnical engineering. [ABSTRACT FROM AUTHOR]

In this paper, the vehicle ISD (inerter-spring-damper) suspension and power-driven-damper control strategy are combined to the suspension design, and the power-driven-damper semiactive ISD suspension is proposed. The dynamic models of the passive suspension S1 and two semiactive ISD suspensions S2 and S3 are established. Based on the port-controlled Hamiltonian theory, the power-driven-damper semiactive control strategy is designed by analyzing the power transfer of suspension S3. Then, the parameters of the two models are optimized by the particle swarm optimization algorithm, and the optimization results show that the suspension S3 has better performance. The influence of the semiactive damping coefficient, the spring stiffness, and the inertance on the vibration suppression performance is investigated based on the suspension S3. The effect of parameter perturbation on power-driven-damper semiactive vehicle ISD suspension illustrates that the designed semiactive vehicle ISD suspension has better ride comfort in a wider range frequency and good robust performance. [ABSTRACT FROM AUTHOR]

This paper presents a multistrategy fusion particle swarm optimization model for dynamic economic dispatching of renewable energy in distribution networks. The objective is to minimize active network losses and system voltage deviation while considering the integration of distributed energy sources and static reactive power compensators. The algorithm incorporates specific strategies, including a particle position change strategy based on the midpipeline convergence approach, a strategy for generating exploding particles near the optimal particles, and a particle velocity update strategy relying on the global optimal particle position. The inertia weights and particle position update methods of the simplified particle swarm optimization algorithm are also utilized. Simulation experiments are conducted on an IEEE 33 bus radial distribution system, demonstrating the effective optimization of system losses while ensuring system voltage stability. This research contributes to the scientific understanding of renewable energy integration in distribution networks and its economic dispatching. [ABSTRACT FROM AUTHOR]

In this paper, a 30 dB broadband microstrip directional coupler is designed by using a discrete coding type coupling structure with a co‐simulation technique. Combined with the proposed hybrid binary particle swarm genetic optimization algorithm (HBPSGOA) and the carefully designed fitness function, the discrete coding type structure can provide satisfactory flexibility and excellent performance. For a fixed design space, the discrete space can firstly be encoded and mapped to the corresponding coding matrix, and then the optimal coding matrix searched by HBPSGOA will be used to design a broadband coupler with high directivity. The effectiveness of the proposed HBPSGOA, binary particle swarm algorithm (BPSO), and genetic algorithm (GA) to design a discrete coding type coupler is tested. Experimental results prove that the HBPSGOA proposed in this paper has higher optimization efficiency. For the 30 dB broadband microstrip directional coupler designed in this paper, the center frequency is at 2 GHz and the fractional bandwidth is 39.4%. The minimum directivity of the designed coupler is 20 dB in the operating band and the maximum directivity is 39.3 dB at 1.9 GHz. In addition, the reason why the coupler can work in a wideband is investigated. [ABSTRACT FROM AUTHOR]

Optimization challenges effectively mirror numerous complex real‐world problems. These are typically addressed using two methodologies: approximate methods and exact methods. Within the approximate category, meta‐heuristic methods are prominent. These algorithms employ systematic principles to avoid local optima or prevent their occurrence altogether. The evolution of computing technology has spurred the development of various meta‐heuristic algorithms designed for precise and expedient resolution of optimization issues. This paper conducts a comparative analysis of meta‐heuristic algorithms applied to structural optimization. It encompasses 11 distinct meta‐heuristic optimization algorithms: atomic orbital search algorithm, chaos game optimization, energy valley optimizer, firefly algorithm, genetic algorithm, gray wolf optimization, harmony search, material generation algorithm, particle swarm optimization, social network search, and whale optimization algorithm. The efficacy of these algorithms is assessed through their application to structural problems such as trusses, cantilever‐cracked beams, welded beams, tubular columns, and I‐shaped beams. Evaluation criteria include computational efficiency, accuracy, and statistical analysis based on reports among the 50 iterations. The findings confirm the effectiveness of the algorithms, with particle swarm optimization and social network search frequently delivering superior results across most problem sets. [ABSTRACT FROM AUTHOR]

A healthy operation of photovoltaic installations (similar to all electrical systems) is always limited by breakdown, degradation due to aging, or imbalance caused by weather conditions. In this context, producing the maximum energy possible with an acceptable form factor is a significant challenge for autonomous systems, especially those connected to the grid. In this paper, we have two main issues to address. The first is determining the maximum power point of an unbalanced photovoltaic field (due to a defect or nonuniform weather conditions affecting the photovoltaic generators). For such a system, the particle swarm optimization (PSO) algorithm remains highly effective because it can easily handle the existence of multiple maxima simultaneously to provide the best possible solution. The second challenge is managing the imbalance between the three phases of the photovoltaic system. In this context, the results of conducted studies propose two approaches to balance and maximize the power supplied by the photovoltaic generator and converters. In addition, the presence of a battery storage system plays dual roles: firstly, compensating the power fluctuations due to nonuniform operating conditions between phases, and secondly, ensuring system power supply during periods of no sunlight exposure. The proposed approaches take into account the constraints imposed on DC voltages and currents to ensure optimal integration with the multilevel inverter stage (cascaded H‐bridge multilevel inverters). This is achieved through selective harmonic elimination control without the need for a filtering system. A comparative study between these two approaches will be conducted to assess their advantages and disadvantages. The battery‐based storage system efficiently absorbs excess energy and provides energy during deficits, thanks to a flexible control mechanism that allows easy switching between different battery groups and phases. [ABSTRACT FROM AUTHOR]

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]

Distribution process is the core of logistics enterprise system. Efficient distribution process is the key for enterprises to improve logistics service level, gain competitive advantage, and win customers. In order to reduce the design error of the system and ensure the effective operation of the system, this paper establishes the system model and analyzes the properties of the model through the evaluation and analysis of various resources of the distribution system. The future development of intelligent logistics system is not only the key for logistics enterprises to win competition, but also a new measure to promote China's economic development. This is not only a typical NP hard problem, but also a major challenge for the rational and scientific development of the intelligent logistics industry. Based on the previous theoretical research results, this paper intends to explore the intelligent logistics distribution route selection scheme by using mathematical modeling methods such as particle swarm optimization, so as to provide new technologies and methods for logistics distribution operation and management decision-making. The results show that compared with the traditional genetic algorithm, the accuracy of the improved particle swarm optimization algorithm is improved by 10.36%. This method effectively improves the operation cycle and link efficiency and achieves the purpose of optimization. The improved particle swarm optimization algorithm proposed in this paper is not only more suitable and effective for enterprise decision makers to deal with subjective judgments in an imprecise environment. It is also based on the evaluation sequence of each indicator of the alternative obtained from the evaluation and the comprehensive evaluation of all indicators. By weighting and considering the results, the best solution for enterprise logistics and distribution is selected. [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 view of the low level of gymnastics facilities monitoring and intelligent management of gymnastics venues, which cannot effectively manage gymnastics venues in real time, this paper proposes a method of gymnastics facilities monitoring and intelligent optimization distribution of gymnastics venues in the Internet of Things. This paper will build an information monitoring model, introduce a particle swarm optimization algorithm to participate in the location layout, and explore the actual effect of gymnastics and the Internet of Things. The research results show that (1) the system can measure the mechanical error of facilities and ensure that the controllable fault tolerance rate is less than 1%. (2) The quality of system monitoring is evaluated in accuracy, time, delay, and satisfaction, and the results are basically satisfactory, but the accuracy and time still need to be improved. (3) Using the evaluation system of sports facilities to test the temperature, humidity, facility pressure, and energy consumption suitable for gymnastics and to verify the injury tendency of athletes. When the damage tendency is between 70% and 100%, the actual damage rate is 1. (4) The speed of the PSO algorithm is faster than other methods, which is used to optimize the layout of gymnastics venues and has a certain role in promoting the construction of gymnastics venues. The system model designed in this paper performs well in gymnastics and needs to be further improved and optimized. [ABSTRACT FROM AUTHOR]

Aiming at the limited learning ability of a single model, the objective of this paper is to investigate situational awareness of the network security which is established on the fusion model. In this paper, a convolutional neural network (CNN) and long short-term memory (LSTM)-based model for situational assessment of the network security condition are provided. According to different fusion methods, the parallel and serial CNN-LSTM fusion models were constructed to evaluate the UNSW-NB15 data set, and both the situation values and levels were obtained. The investigational outcomes illustrate that the evaluation accuracy of the two models can reach up to 85.19% and 92.59%, respectively. A situation prediction model called IPSO-ABiLSTM is suggested and is based on improved particle swarm optimization (IPSO) and attention fusion bidirectional long short-term memory (ABiLSTM). The IPSO has the characteristics of faster convergence speed to optimize the ABiLSTM network parameters and obtain the optimal parameters for situation prediction. The investigational outcomes illustrate that the suggested IPSO-ABiLSTM model has a fitting degree of up to 0.9922, which can effectively achieve the situation prediction in the network security. [ABSTRACT FROM AUTHOR]

The establishment of enterprise target inventory is directly related to the forecast of drug sales volume. Accurate sales forecasting can help businesses not only set accurate target inventory but also avoid inventory backlogs and shortages. In this paper, NN technology is used to forecast sales and is optimized using the PSO algorithm, resulting in the creation of a drug sale forecast model. The model optimizes the weights and thresholds of NN using the improved PSO optimization algorithm and predicts the periodic components based on time correlation characteristics, effectively describing the trend growth and seasonal fluctuations of sales forecast data. Furthermore, the model in this paper has been creatively improved according to the needs of practical application, which has improved the shortcomings of traditional NN, such as long training time, slow convergence speed, and ease to fall into local minima, to improve performance and quality, and has received positive results in application. The experimental results show that this model has a prediction accuracy of 96.14 percent, which is 12.78 percent higher than the traditional BP model. The optimized model can be used to forecast drug sales in a practical and feasible way. [ABSTRACT FROM AUTHOR]

In the postmodern era of tourism, tourists' behavior has undergone a substantial change and the demand of customized experience dominates the tourism market. The traditional single-objective travel route recommendation method fails to meet the multiobjective needs of users. To handle this problem, a multiobjective hybrid tabu search algorithm for urban travel route recommendations is proposed in this paper. First, the rating and level of attractions, as well as the corresponding information of hotels and restaurants within a certain radius, are considered. Then, based on this information, a crowdsensing scoring method is established. Second, the hybrid particle swarm genetic optimization algorithm is exploited to generate a single-object route, and the fast nondominated Pareto sorting algorithm is exploited to find the optimized solution. Then, the hybrid tabu algorithm is used to optimize the personalized route chosen according to multiple objects set by users. This algorithm combines the global search ability of the genetic algorithm and the neighborhood search ability of the tabu algorithm to prevent convergence from falling into a local optimum. Finally, the experiments are conducted on the real-world data collected from the Dianping and Ctrip web sites. The comparison with baseline algorithms indicates that the algorithm proposed in this paper provides accurate and reasonable route recommendations for users. [ABSTRACT FROM AUTHOR]

With the improvement of the complexity and reliability of mechanical equipment, it has been difficult for the commonly used variational modal decomposition method of vibration signal of rotating machinery to meet the current practical engineering requirements. In order to further improve the adaptability, processing efficiency, and robustness of rotating machinery fault diagnosis methods, a collaborative hybrid element heuristic to multiobjective optimization algorithm is introduced in this paper. Combined with variational modal decomposition (VMD) method, the fault diagnosis method of rolling bearing under complex working conditions is studied. This paper mainly uses a collaborative hybrid metaheuristic algorithm to improve the nondominated sorting genetic algorithm II (NSGA II) and multiobjective particle swarm optimization (MOPSO), which improves the convergence efficiency of multiobjective optimization method and solves the problem of uneven distribution of optimal solutions. Then, the improved multiobjective optimization algorithm is combined with VMD to solve the problem of parameter selection of the VMD method under complex working conditions of rotating machinery. At the same time, the variation relationship between various signal features and VMD decomposition results is compared and studied, and the features with good effect are taken as the objective function of the optimization algorithm; the ability of denoising and feature extraction of VMD in rotating machinery fault diagnosis is improved. In this paper, the proposed method is explored by using analog signals and experimental data of rolling bearings. Through comparison, the improvement of adaptive ability, operation speed, and robustness of the proposed method in rotating machinery fault diagnosis is verified. [ABSTRACT FROM AUTHOR]

With the extensive application of virtual technology and simulation algorithm, motion behavior recognition is widely used in various fields. The original neural network algorithm cannot solve the problem of data redundancy in behavior recognition, and the global search ability is weak. Based on the above reasons, this paper proposes an algorithm based on genetic algorithm and neural network to build a prediction model of behavior recognition. Firstly, genetic algorithm is used to cluster the redundant data, so that the data are in fragment order, and then it is used to reduce the data redundancy of different behaviors and weaken the influence of dimension on behavior recognition. Then, the genetic algorithm clusters the data to form subgenetic particles with different dimensions and carries out coevolution and optimal location sharing for subgenetic particles with different dimensions. Through simulation test, the algorithm constructed in this paper is better than genetic algorithm and neural network algorithm in terms of calculation accuracy and convergence speed. Finally, the prediction model is constructed by setting the initial value and threshold to predict the behavior recognition, and the results show that the accuracy of the model constructed in this paper is improved in the analysis of behavior recognition. [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]

This paper investigates the threat assessment method and target assignment algorithm in multi-UAV cooperative air combat decision-making. To address the uncertainty and dynamic changes in multiple threat attributes and attribute information of UAV targets, we propose a UAV target dynamic threat assessment method based on intuitionistic fuzzy multiattribute decision-making. Firstly, we propose a mixed situation information representation method to represent interval-valued fuzzy data appropriately. Secondly, we employ the normal distribution weight assignment method to fuse the multi-time situation information. Then, by incorporating the analytic hierarchy process and entropy method, we determine the normalized threat value of the target considering both objective situation data characteristics and decision-maker preferences. Finally, a simulation example is provided to validate the rationality of our proposed algorithm. For solving the multi-weapon multi-target assignment problem, a target assignment method based on the VNS-IBPSO algorithm is introduced. This method improves upon the limitations of the BPSO algorithm, such as limited local search capability and premature convergence, by combining variable neighborhood search and an improved binary particle swarm optimization algorithm. Simulation results show that the proposed threat assessment method can obtain reasonable threat assessment results under complex dynamic environments. The proposed VNS-IBPSO algorithm can solve the target assignment model quickly and efficiently based on the assessment results, therefore ensuring that the UAV mission planning system makes the correct combat plan. [ABSTRACT FROM AUTHOR]

In China's rural credit system, the problem of credit constraints is prominent. Due to the imperfect credit market, a large number of rural residents have credit constraints. Rural credit constraint is a serious problem restricting China's rural economic development. Aimed at solving the rural credit constraints, this paper makes an optimization analysis on the rural credit system and loan decision-making. To more reasonably evaluate customers' borrowing ability, the credit risk based on farmers' data on the big data platform is evaluated in this paper. The stacked denoising autoencoder network is improved by adopting the deep learning framework to improve the accuracy of credit evaluation. For improving the loan decision-making ability of rural credit system, a loan optimization strategy based on multiobjective particle swarm optimization algorithm is proposed. The simulation results show that the optimization ability, speed, and stability of the proposed algorithm have achieved good results in dealing with the loan portfolio decision-making problem. [ABSTRACT FROM AUTHOR]

In the context of the rapid development of the Internet +, the e-commerce industry has developed very rapidly and is playing an increasingly important role in the market. More and more investors are also paying attention to the financial risks of the e-commerce industry. Therefore, how effectively identifying and controlling the credit risks of e-commerce enterprises corporate financial management is particularly important. Its financial-related business will be affected by various factors such as financing, credit, and the environment. The particle swarm optimization in the machine learning algorithm optimizes the support vector machine model; using its nonoverlapping clustering properties, it can fully display the density relationship between each data and the density relationship between its subclasses in a graphical way. It is hoped that the research on the financial risk of this enterprise can provide some insights into the identification, management, and related governance of the financial risk of e-commerce enterprises. In this context, this paper studies the credit risk simulation of corporate financial management based on machine learning algorithms, aiming to provide a new research direction for the current subject of corporate financial management credit risk. This paper optimizes the support vector machine model in the machine learning algorithm, integrates it into the financial risk of the enterprise, and analyzes the convergence of the algorithm. The experimental sample selected a bad e-commerce platform owned by a company, and the data were collected from the financial statements released by the company from 2018 to 2021. The experimental results show that in 2018, the two platforms lost −151.80 and −223.45, respectively, and the B platform lost more. By 2021, both platforms have achieved profits, which are 244.76 and 241.71, respectively. However, in the past few years, platform B has achieved positive profit growth every year, and the growth rate is average, and the overall growth rate is higher than that of platform A. This shows the limitations of managers' decision-making and also shows the importance of enterprises to adjust their strategies in a timely manner through market feedback. The study was well completed. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the career decision-making self-efficacy model cannot accurately mine the implicit relationship between students and career decision-making, resulting in the low structural validity and reliability of the model, a career decision-making self-efficacy model of flight attendant students under social cognitive theory is proposed. This paper analyzes the characteristics of self-efficacy under the social cognitive theory, reduces the dimension of student career decision-making score matrix into two matrices: student implicit feature and implicit feature career decision-making, and excavates the implicit relationship between flight attendant students and career decision-making. Students are described as a multidimensional decision vector to determine the professional cognitive level of flight attendant students. The self-efficacy model of students' career decision-making is established from five aspects: self-evaluation, collecting information, selecting goals, making plans, and solving problems, which is solved by the particle swarm optimization algorithm. The experimental results show that the structural validity and reliability of the model constructed in this paper are higher than those based on cluster analysis and user portrait. Therefore, it can effectively evaluate and measure the career decision-making self-efficacy of flight attendant students. [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]

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]

How to start from the fitness needs of people and effectively improve the precision of the supply of public fitness services everyone is an important issue that needs to be solved first at the current stage. This requires us to proceed from the reality, conduct accurate research, and find a method that can match the current problem. In this paper, taking basketball projects in national fitness as an example, by introducing a proposition about the development of small basketball events, the corresponding big data modeling and analysis methods are studied. The research methods and research objectives involved in this paper are based on the relevant parameters of the multivariate statistical model. First, the article introduces the calculation principle of the multiple linear regression model. We introduce the concept of variance inflation factor involved in this principle and carry out the modeling and analysis of big data based on this variable. In order to illustrate the application effect of big data in this kind of research, this paper introduces three different big data technologies, including immune selection optimization algorithm, particle swarm optimization algorithm, and Elman neural network, to predict and analyze the variance inflation factor (VIF) corresponding to the small basketball project. The analysis results show that the Elman network exhibits certain advantages in terms of computing convergence time. And, as the number of calculation steps increases, the superiority of the Elman network is more obvious. As far as the prediction performance is concerned, the square of the correlation coefficient corresponding to the immune selection optimization algorithm is the largest and the sum of the squares of the residuals is the smallest, showing superior prediction performance. [ABSTRACT FROM AUTHOR]

In order to improve the efficiency of information management of open laboratories in colleges and universities and reduce the cost of information management of open laboratories in colleges and universities, this paper proposes the open sharing management of artificial intelligence laboratories based on the deep integration of big data. The infrastructure layer of the system transmits the information management data of the university open laboratory to the cloud platform layer. The cloud platform layer completes the data management, cloud computing, and access control and transmits the processed data to the application layer. The application layer realizes the information management of the university open laboratory according to the data fed back by the cloud platform layer. Among them, in the process of data transfer in open laboratories of colleges and universities, pyramid technology is used to realize data transfer, and the optimization calculation of particle swarm optimization algorithm is used to complete the transfer of multidimensional and complex data transmission information, so as to improve the efficiency of data transfer. The experimental results show that the system has high accuracy in transferring multidimensional and complex data in university open laboratories, the average resource utilization rate of the system is as high as 92%, the average response of the system is as fast as 8.5 ms, and the management cost is effectively reduced. Conclusion. The system designed in this paper can reduce the information management cost of open laboratories in colleges and universities. [ABSTRACT FROM AUTHOR]

This paper presents an in-depth analysis and study of traditional corridor construction and tourism system planning using intelligent sensing networks. This paper proposes a node deployment and coverage optimization method for HWSNs based on particle swarm optimization emperor butterfly algorithm and extreme learning machine, with the objectives of reducing node deployment cost, reducing coverage redundancy and voids, improving monitoring area coverage, and increasing the agility of data processing by 15.03%, efficiency by 12.15%, and stability by 11.31%. In the paper, the static parameter table of the sensor is stored inside the smart sensor or stored on the IoT cloud platform to achieve automatic registration of the static parameters of the smart sensor. According to the characteristics of dynamic parameters of smart sensors, an autonegotiation algorithm is designed to realize the dynamic parameters of smart sensors to be determined by autonegotiation and complete registration without human intervention, which includes four parts: utility function, concussion protocol, proposal generation strategy, and response mechanism. The intelligent sensing system is composed of three parts: smart sensor, IoT gateway, and IoT cloud platform; the functions of each part in the system are analyzed, and MAR and STM32F4 are selected as the core board of smart sensor and IoT gateway. And for the problems of time-consuming and tedious access to smart sensors in the system, we propose to study the adaptive sensor UART serial port parameters and automatic registration of sensor parameters. Based on the analysis of the current environment and resource characteristics of the traditional corridor, the current situation of ecotourism resources of the traditional corridor is sorted out, an evaluation index system is established to evaluate ecotourism resources, and the current development basis is analyzed from four aspects: natural environment, tourism resources, product development, and infrastructure. Finally, to promote the sustainable development of the ecotourism corridor in the Chengdu section of the southern foot of the Qinling Mountains, the planning and development strategies to match the ecotourism corridor in the Chengdu section of the southern foot of the Qinling Mountains are proposed in four aspects: transportation organization optimization, ecological landscape enhancement, support system construction, and regional tourism development. [ABSTRACT FROM AUTHOR]

Land use and comprehensive land evaluation are essential. Based on fuzzy mathematics theory and biological heuristic algorithm, the land input-output benefits are evaluated comprehensively. This paper firstly selects six indicators from six aspects of land resource input, capital input, and economic output, and so on. Based on land input and output, this paper constructs the evaluation indicator system of comprehensive benefit for land use. Then, based on the theory of fuzzy mathematics, the improved particle swarm optimization (PSO) algorithm is used to identify the fuzzy density value of the evaluation index. Combined with the analytic hierarchy process (AHP), comprehensive evaluation method, through the combination of subjective and objective methods, is comprehensively evaluating land benefits. Finally, the Tobit model is constructed to further analyze the influencing factors of total factor productivity of urban land use and explore the influencing mechanism of government regulation, land opening, and other factors and land development. The research results of this paper can provide reference for future urban planning, land structure adjustment, land resource utilization and protection, food security, ecological security, economic security, and so on. [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]

Under the current trend of economic globalization and international competition, more and more production enterprises are introducing the project management model, that is, customer or order projection, and using the concept of project management to manage operations. In an enterprise, there are multiple customers at the same time, and the production line can also generate multiple orders from different customers at the same time. That is to say, multiple projects are running at the same time, resulting in continuous changes in management processes, heavy project coordination tasks, and a serious waste of corporate resources. The resources that can be used are limited. In the production business using project management, how to rationally utilize the limited business scheduling resources has become the focus of research. Aiming at the method of business management resource scheduling, this paper investigates the application of enhanced particle swarm algorithm on the cloud deep learning platform, and the mapping problem between virtual machines and physical machines. As a heuristic algorithm, particle swarm optimization is suitable for solving combinatorial optimization problems. By improving the diversity of particle hosts and setting parameters, it improves the integration speed and accuracy of the algorithm. Through the analysis of the current situation of multi-project business management, it examines the allocation of resources. In terms of business management, it has established a resource allocation model for multiple projects. The results show that the method improves the efficiency of the enterprise by 35% compared to the traditional method with a 20% reduction in personnel. A better configuration scheme is simulated by MATLAB, which verifies the scientificity and effectiveness of the method studied in this paper. [ABSTRACT FROM AUTHOR]

Winter wheat is one of the most important food products. Increasing food demand and limited land resources have forced the development of agricultural production to be more refined and efficient. The most important part of agricultural production is sowing. With the promotion of precision agriculture, precision seeding has become the main component of modern agricultural seeding technology system, and the adoption of precision seeding technology is an important means of large-scale production and cost saving and efficiency enhancement. However, the current sowing technology and sowing equipment cannot meet the requirements of wheat sowing accuracy. In this context, a differential perturbation particle swarm optimization (DPPSO) algorithm is proposed by embedding differential perturbation into particle swarm optimization, which shows fast convergence speed and good global performance. After that the DPPSO is used to optimize the convolutional neural network (CNN) to build an optimized CNN (DPPSO-CNN) model and applied to the field of crops fine sowing. Finally, the experimental results show that the proposed method not only has a faster convergence rate but also achieves better wheat seeding performance. The research of this paper an effectively improves the accuracy and uniformity of wheat seeding and lay a foundation for improving wheat yield per unit area and promotes the intelligent development of agriculture in the future. [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]

People's lives are undergoing tremendous changes with the development of the times. Compared with the past, people's pursuit of spiritual and cultural life also makes our education field usher in a huge development to adapt to the changes in the context of the times. But, at the same time, the development of labor education is gradually being downplayed by people, resulting in a series of problems such as people preferring comfort and not working. Aiming at this common problem, this paper will use the ant colony algorithm and particle swarm optimization algorithm in the computer intelligent algorithm to optimize the way of labor education. It includes the principle and basic process of the ant colony algorithm, the establishment of the mathematical model of the original ant colony algorithm, and the improved algorithm of the ant colony algorithm. The research results of the optimization method of labor education showed the following: when the number of ant colonies reaches 51, the number of iterations of the algorithm will be the least, and the corresponding shortest path is also the best solution; when the combination of pheromone intensity and volatility factor is 3, the optimal solution can be quickly found, and the algorithm inflection point of MMAS is 44.82. From the research results, it can be seen that the computer intelligent algorithm has a good choice for the optimization of labor education and can achieve a major breakthrough in the traditional model of labor education. [ABSTRACT FROM AUTHOR]

Multipath data transmission is a key problem that needs to be solved urgently in wireless sensor networks. In this paper, sensor node failure, link failure, energy exhaustion, and external interference affect the stability and reliability of network data transmission. A multipath transmission strategy for wireless sensor networks based on improved shuffled frog leaping algorithm is proposed. A mathematical model of multipath transmission in wireless sensor networks is established. In the shuffled frog leaping algorithm, combined with the transition probability in the particle swarm optimization algorithm, random individuals in the subgroup are introduced to assist the search when updating the frog individual position, which improves the algorithm's ability to jump out of the local optimum and improves the quality of the optimization algorithm solution. The model is applied to multipath transmission in wireless sensor networks. Then, the shuffled frog leaping algorithm is used to update, divide, and reorganize the sensor nodes to select the optimal node to establish the optimal transmission path and improve the stability and reliability of the network. Simulation experiments show that the algorithm in this paper can ensure the reliability of data transmission, reduce the network packet loss rate and network energy consumption, and reduce the average delay of data transmission. [ABSTRACT FROM AUTHOR]

The application of robots reduces repetitive and dangerous tasks for humans, especially the spraying robot, because many spraying materials are corrosive, toxic, and harmful. This paper designs the motion trajectory planning of the material spraying service robot. With the increasing demand for technology, there are strict requirements for the uniformity and thickness of spraying. In view of this, this paper proposes an algorithm for modeling kinematics using a joint screw and optimizes the modeling algorithm using particle swarm optimization. This makes the industrial spraying robot more intelligent and more capable of completing high-standard tasks. The experimental results in this paper show that when the spraying radii on the large-curvature cone surface are 44.5 and 49.5 mm, respectively, the coating distribution in the intersection area of the curved surfaces can be well controlled, and the optimized algorithm can better plan the path of the spraying robot. [ABSTRACT FROM AUTHOR]

Swarm intelligence algorithm is an emerging evolutionary computing technology, which has become the focus of more and more researchers. It has a very special connection with artificial life, especially evolutionary strategies and genetic algorithms. The swarm intelligence algorithms you see include genetic algorithm, particle swarm optimization algorithm, and ant colony algorithm. This part of the content has been supplemented in the article. Evolutionary computing is a group-oriented random search technology and method produced by simulating the evolutionary process of organisms in nature. Evolutionary computing is based on natural selection strategy: survival of the fittest, elimination of the unfit, and individuals with large fitness values have a higher survival probability than individuals with small fitness values. The purpose of this paper is to study the structure optimization of carbon nanotubes based on swarm intelligence algorithm and evolutionary computation. It is expected to optimize the structure of carbon nanotube materials with the help of intelligent evolution algorithm, so that it can be used in more fields. In this paper, the preparation process and principle of carbon nanotube-based gas sensors are studied, and the preparation process of the side-heated gas sensor is selected. This paper focuses on the strain sensing performance of carbon nanotubes, analyzes various parameters that characterize the sensing performance, and proposes feasible technical routes for improvement, optimization and improvement. The experimental results in this paper show that when different proportions of oxides are added, the tensile strength of carbon nanotube materials is increased by about 8%, and the elastic modulus is increased by up to 40%. After adding CNFs, the tensile strength increased by up to 18%, and the elastic modulus increased by up to 50%. [ABSTRACT FROM AUTHOR]

In order to improve the quality of English translation teaching, this paper combines the theory of multiple intelligence to classify the English translation teaching process. Moreover, this paper adopts Fisher's discriminant method and Bayesian discriminant method to classify the English translation teaching samples. In order to improve the discrimination accuracy of the extreme learning machine algorithm, this paper applies the particle swarm optimization extreme learning machine algorithm to the research on the classification of English translation teaching samples and proposes an intelligent English classification teaching model based on the actual situation of English translation teaching. In addition, this paper verifies the system model proposed in this paper by evaluating the teaching method. The research shows that the classification model of English translation teaching mode based on the theory of multiple intelligence proposed in this paper has a certain effect, which can promote the effect of English translation teaching. [ABSTRACT FROM AUTHOR]

Interbank offer rate is the interest rate at which banks lend money to each other in the money market. As a market-oriented core interest rate, Shibor can accurately and timely reflect the capital supply and demand relationship in the money market, and its changes will quickly transmit and affect China's financial market. Therefore, the purpose of this paper is to predict and study the fluctuation and trend of Shibor. In this paper, the overnight varieties of Shibor were studied and predicted from two time dimensions, namely, daily fluctuation and monthly trend. In the prediction of overnight Shibor daily data, a comparison prediction model based on BP neural network algorithm was first established, and then WNN was applied in the prediction, and the effect was found to be better. When predicting the monthly mean value of overnight Shibor, nine indicators were selected and tested for correlation based on the factors affecting the trend of interest rate, and a regression model of support vector machine was established. Particle swarm optimization algorithm was used to improve the SVR algorithm, and the PSO-SVR prediction model was established to improve the prediction accuracy. The model could basically predict the trend of overnight Shibor. Furthermore, a prediction model of WNN based on cuckoo search (CS) optimization was proposed, which improved the prediction accuracy by 78% and fitted the daily fluctuation of overnight Shibor well. [ABSTRACT FROM AUTHOR]

In recent years, the application of wireless sensor networks (WSN) in power systems has received a great deal of attention. As we all know, the most important issue for the power system is security and stability, especially due to the massive outages caused by cascading trips. Therefore, in today's era, from the perspective of cascading trips, how to effectively use WSN to analyze and evaluate the security level of the power grid is an important direction for future power development. In this paper, an algorithm based on the WSN collection of online data to calculate the corresponding security level of the system is proposed for the cascading trip phenomenon, to achieve the online evaluation of the cascading trips. First, this paper proposes a hybrid layered network structure based on WSN for monitoring system and details the acquisition of power grid parameters by its acquisition layer. Secondly, combined with the manifestation of cascading trips and the action equation of current-type line backup protection, the mathematical representation of the grid cascading trips is given, and the mathematical form corresponding to the critical situation is strictly proved, and an index for evaluating the security level of the power grid is proposed and then further combined with the actual physical constraints of the power grid and the establishment of a mathematical model for calculating the security level of the grid cascading trips. For this model, this paper relies on evolution particle swarm optimization (EPSO) to give specific ideas for solving the model. Finally, a case analysis is performed by the IEEE39 node system and the results of the case show the effectiveness of the model and method. [ABSTRACT FROM AUTHOR]

Parameter identification is of great significance for the postearthquake performance evaluation of structure equipped with tuned mass damper (TMD). However, the soil-structure interaction (SSI) effects have not been considered in the parameter identification of structure with TMD yet, which influence the dynamic characteristics and seismic responses of structures. This paper aims at proposing a framework for identifying the physical parameters of soil-structure-TMD system. Firstly, the accelerated particle swarm optimization (APSO) algorithm is combined with the search space reduction (SSR) method. Then, the frequency response function and transmissibility function are adopted for output-input and input-only cases, respectively, and a simplified mechanical model for soil-structure-TMD system is employed. Next, the measured responses are used to identify the physical parameters of structure with TMD considering SSI effects. Finally, the effectiveness of the proposed identification method is investigated, and the influences of frequency band and noise pollution on the identification performance are discussed. The results show that the proposed strategy can identify the system physical parameters accurately and quickly. It is worth noting that high frequency bands and noise pollution may lead to estimation error, especially for output-only case. [ABSTRACT FROM AUTHOR]