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With the advancement of the linkage between financial markets, the probability of credit risk infection is also increasing. Traditional financing methods, which mostly relied on corporate credit to give credit to the whole supply chain, have been replaced by supply chain finance. This paper studies the supply chain financial credit risk through the logistic model and chooses the financial data and supply chain financial operation indicators of relevant listed companies from 2014 to 2016 for analysis. Because not all of companies can find the bad debt rate of accounts receivable from 2014 to 2016, and some agricultural listed companies only have one or two years of relevant data, this paper creates an unbalanced panel data with 91 sample sizes, which is larger than previous studies. Binary logistic regression and principal component analysis are mainly used to accurately calculate the compliance probability of cooperative customers in agricultural supply chain financial products. Unlike the existing literature, which mainly uses s.t to define whether an enterprise defaults, this paper uses Z value to define the default risk of listed companies in agricultural supply chain finance. In terms of the default risk value of the company, Z value not only has high accurate value but also has advantages in accurate prediction, which effectively complements and improves the existing research on supply chain finance. [ABSTRACT FROM AUTHOR]

This paper proposes a high-satisfaction evaluation method for health-care product design based on the genetic optimization algorithm in order to deeply understand consumers' satisfaction with forest health-care products. This article builds a consumer demand index system for the service content of forest health-care bases from the four levels of environmental conditions, service items, supporting facilities, and service levels. According to the questionnaire survey results of 1000 randomly selected forest health-care consumers, based on the genetic optimization algorithm, the overall design takes the four levels of satisfaction evaluation into account, resulting in a more reasonable evaluation of health-care products. Experiments show that the method proposed in this paper outperforms the traditional method for evaluating health-care products. [ABSTRACT FROM AUTHOR]

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

Traditional nondestructive testing technology for wood defects has a series of problems such as low identification accuracy, high cost, and cumbersome operation, and traditional testing methods cannot accurately show the specific location and size of wood internal defects; it is urgent to explore a new nondestructive testing scheme for wood defects. Aiming at this problem, this paper designs and develops an automatic detection method for wood surface defects based on deep learning algorithm and multicriteria framework. By comparing the performance of different deep learning detection methods on the data set, the advantages and disadvantages of the detection method in this paper are proved. After a series of works, such as the development and optimization of the experimental algorithm, the algorithm proposed meets the requirements in both the detection accuracy and training time. [ABSTRACT FROM AUTHOR]

In the processing of panoramic video, projection mapping is a very critical step. The selection of the projection mapping format will affect the performance, transmission mode, and rendering mode of the panoramic video codec. Therefore, this article starts from the projection mapping format, analyzes the mapping process of the standard mapping format, and then proposes a method of rendering panoramic video in the projection mapping format. By analyzing the parallel design schemes of swarm intelligence algorithms under different granularities, this paper proposes a parallel swarm intelligence optimization algorithm design method and then designs and implements a parallel artificial bee colony algorithm. With the help of the ArcGIS Engine development platform, this paper defines the interface for data exchange. With the support of Multipatch format data in ArcGIS software, through secondary development, the three-dimensional pipeline automatic modeling module is established, and the pipeline model is automatically generated. The digital construction and visualization of the company play a driving role. Based on the understanding of the characteristics of the pipeline image itself, combined with the analysis of the shortcomings of the existing methods, this paper proposes a new deep learning-based high-definition rendering solution for the pipeline image. In this paper, the pipeline image is preprocessed, and then the processed pipeline image is converted into a style pipeline image through the pipeline image style transfer technology, and the obtained style pipeline image is postprocessed to enhance the effect. The preprocessing of pipeline images mainly includes pipeline image enhancement and pipeline image filtering operations. Its purpose is to change the distribution of pipeline images to improve the quality of pipeline images and make them more suitable for subsequent style conversion. In the part of pipeline image style conversion, this paper proposes a new deep learning-based pipeline image high-definition rendering network, which consists of three subnetworks: pipeline image feature modeling module, feature model alignment module, and pipeline image re-rendering module. This article has conducted sufficient experiments to fully compare the processing results of the method proposed in this article and other existing methods and at the same time shows the high-quality high-definition rendering results. The experimental results verify the excellent performance of the method proposed in this paper. [ABSTRACT FROM AUTHOR]

In this paper, a special design system is developed based on the design of the packaging bottle to achieve the effective acquisition of the image of the cross-section of the packaging bottle to be measured under the condition of limited space size, avoiding the distortion of the object to be measured. At the same time, the image of the area where the target packaging bottle is located is segmented, and the curve features are quickly determined and effectively matched with the template library to realize the recognition of the shape features of the bottle. In this paper, the design of the packaging bottle is first designed by mechanism design and 3D modeling, followed by rapid prototyping methods such as 3D printing, and the prototype is made for functional verification. Finally, the transmission speed and stability of the design system for packaging bottle recognition are improved through structural analysis and optimization methods. To realize the intelligent control of the packaging bottle transmission and identification system, the hardware control circuit is designed and the relevant intelligent control program is prepared based on the embedded system so that the packaging bottles in the transmission process can be quickly and accurately positioned and identified. An improved AdaBoost algorithm is proposed for packaging bottle detection. In the process of algorithm learning, the Haar features are too large and time-consuming, and the training sample is cropped to remove the sample edge pixels, which effectively reduces the number of features, thus reducing the computation. The proposed optical flow method is used to obtain the motion region in the video image as the region of interest, and the canny operator is used in the region of interest for edge detection, and the region of interest is filtered by the edge energy to exclude the noninterest region. Finally, the AdaBoost algorithm is used to detect the region of interest, which reduces the detection area and decreases the detection time. The improved AdaBoost algorithm has a high accuracy improvement over the traditional AdaBoost algorithm for the recognition of various packaging bottles with relatively suitable training set samples, and the system recognition time has reached the requirements of industrial recognition. [ABSTRACT FROM AUTHOR]

Considering the widely existing processing time uncertainty in the real-world production process, this paper constructs a fuzzy mathematical model for the silicon single crystal production batch scheduling problem to minimize the maximum completion time. In this paper, a two-stage hybrid optimization algorithm (TSHOA) is proposed for solving the scheduling model. Firstly, the improved differential evolution algorithm (IDE) is used to solve the order quantity allocation problem of silicon single crystal with different sizes to obtain the quantity of silicon single crystal rods with different sizes produced by different types of single crystal furnaces. Secondly, the variable neighborhood search (VNS) algorithm is adopted to optimize the order quantity sequencingof batch production processes. Finally, simulations and comparisons demonstrate the feasibility of the model and the effectiveness of TSHOA. [ABSTRACT FROM AUTHOR]

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

Circuit design plays a pivotal role in engineering, ensuring the creation of efficient, reliable, and cost-effective electronic devices. The complexity of modern circuit design problems has led to the exploration of multi-objective optimization techniques for circuit design optimization, as traditional optimization tools fall short in handling such problems. While metaheuristic algorithms, especially genetic algorithms, have demonstrated promise, their susceptibility to premature convergence poses challenges. This paper proposes a pioneering approach, the chaotic multi-objective Runge–Kutta algorithm (CMRUN), for circuit design optimization, building upon the Runge–Kutta optimization algorithm. By infusing chaos into the core RUN structure, a refined balance between exploration and exploitation is obtained, critical for addressing complex optimization landscapes, enabling the algorithm to navigate nonlinear and nonconvex optimization challenges effectively. This approach is extended to accommodate multiple objectives, ultimately generating Pareto Fronts for the multiple circuit design goals. The performance of CMRUN is rigorously evaluated against 11 multiobjective algorithms, encompassing 15 benchmark test functions and practical circuit design scenarios. The findings of this study underscore the efficiency and real-world applicability of CMRUN, offering valuable insights for tailoring optimization algorithms to the real-world circuit design challenges. [ABSTRACT FROM AUTHOR]

A multi-offspring genetic algorithm based on sorting grouping selection and combination pairing crossover was proposed in this paper. First, individuals in the population were sorted according to their objective function values, and the remaining individuals were divided into several groups after eliminating the worst. Then, the selection operation, which has the advantage of a simplified calculation that can be easily performed, was implemented. Second, a combination pairing crossover operator was designed. Individuals from different groups were selected for new combinations. In a combination, if a crossover condition was met, individuals pairing and transposons crossover operation were made. Otherwise, offspring copies of the individuals in the combination were used, which increase the opportunity of generating better offspring by producing multi-offspring. Finally, a new population was formed by adopting basic bit mutation operator, elitism policy and the strategy of multi-offspring population competition. Moreover, the catastrophe mechanism has been introduced into improved algorithm to avoid premature convergence. The test results on the functions of CEC 2017 test suites shown that the algorithm proposed in this paper has better search performance, stability and faster convergence to the global optimal solution. These results thus verified the effectiveness and feasibility of the algorithm proposed in this paper. Applying the improved algorithm to the location optimization problem of agricultural product logistics facilities, it shown that the improved algorithm is an effective method to solve the location optimization. [ABSTRACT FROM AUTHOR]

In the existing research, scholars have different views on the concept and structure of metacognition. Based on different theories, they produce different training methods of metacognition strategies. However, because the training design and training process in the experiment are too complicated and the time is difficult to control, it remains difficult to effectively extend this kind of training to more educational fields. This paper deeply integrates the optimization theory with the case teaching of college students' English writing in undergraduate colleges and proposes the optimal design of the case teaching process. The optimal design is mainly carried out according to four processes: English writing education consultation, English writing teaching case compilation, English writing case teaching implementation, and English writing case teaching evaluation. In ordinary English writing, students focus on four aspects: preplanning, selective attention, self-monitoring, self-evaluation, and adjusting, controlling, and managing one's own cognitive activities according to self-monitoring metacognitive strategies, mastering the optimization theory, and adjusting the writing speed are self-adjustment and self-evaluation according to one's own situation. In the selected dimensions of English writing, the mean of the postwriting test was higher than that of the prewriting test. The mean of the "average sentence length" increased from 8.6 to 19.1. Integrating metacognitive strategy training into English writing teaching can improve students' English writing ability. [ABSTRACT FROM AUTHOR]

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

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

A multi-offspring genetic algorithm based on sorting grouping selection and combination pairing crossover was proposed in this paper. First, individuals in the population were sorted according to their objective function values, and the remaining individuals were divided into several groups after eliminating the worst. Then, the selection operation, which has the advantage of a simplified calculation that can be easily performed, was implemented. Second, a combination pairing crossover operator was designed. Individuals from different groups were selected for new combinations. In a combination, if a crossover condition was met, individuals pairing and transposons crossover operation were made. Otherwise, offspring copies of the individuals in the combination were used, which increase the opportunity of generating better offspring by producing multi-offspring. Finally, a new population was formed by adopting basic bit mutation operator, elitism policy and the strategy of multi-offspring population competition. Moreover, the catastrophe mechanism has been introduced into improved algorithm to avoid premature convergence. The test results on the functions of CEC 2017 test suites shown that the algorithm proposed in this paper has better search performance, stability and faster convergence to the global optimal solution. These results thus verified the effectiveness and feasibility of the algorithm proposed in this paper. Applying the improved algorithm to the location optimization problem of agricultural product logistics facilities, it shown that the improved algorithm is an effective method to solve the location optimization. [ABSTRACT FROM AUTHOR]

Due to the national economic development form and social development demand, in recent years, the government has been vigorously promoting the control of government-enterprise collusion in the bidding process of government projects in order to promote the standardization of the market. How to predict the vertical collusion behavior under different internal and external environments has become an important research content. Although the prediction of individual behavior is difficult to achieve, the prediction of group behavior has certain possibilities. In this paper, we propose a method for predicting and evaluating the vertical collusion behavior of government investment project bidding based on BP neural network analysis optimized by an annealing algorithm. First, drawing on the traditional evaluation model, the evaluation index system of government-enterprise collusion behavior is constructed from five dimensions: internal environment, external environment, policy development, enforcement effort, and feedback channel. Secondly, a machine learning method based on BP neural network optimized by an annealing algorithm is introduced to evaluate the influence of the change of initial conditions on the bidding collusion behavior. This study has certain significance for government managers to discover the problems and causes in policy formulation, which in turn can support the government in further improving the policy utility. [ABSTRACT FROM AUTHOR]

Wavelet transformation is well applied in the field of image processing, and parameter optimization of wavelet transformation has always been an eternal topic on its performance improvement. In this paper, an adaptive self-organizing migration algorithm (ASOMA) is proposed to optimize the wavelet parameters to elevate the performance of wavelet denoising. Firstly, based on the original SOMA, an adaptive step size adjustment method is proposed by recording the step information of successful individuals, which improves the search ability of the SOMA. Secondly, an exploratory selection method of leader is proposed to effectively balance the exploration and exploitation of the SOMA. Finally, ASOMA is compared with the original SOMA and its variants using wavelet general threshold denoising on classical test images in denoising performance, which is evaluated by the indicators of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). The experimental results demonstrate that ASOMA has better denoising performance than the wavelet general threshold, the original SOMA, and the related variants of SOMA. [ABSTRACT FROM AUTHOR]

The modeling of a solar PV system is challenging due to its nonlinear current vs. voltage characteristics. Although various optimization techniques have been applied for the parameter estimation of the solar PV system, there is still a scope to attain the best-optimized results. This paper uses a new meta-heuristic optimization algorithm and a classical technique named Ali Baba and the Forty Thieves (AFT) with Newton Rapson (NR) method to estimate solar PV system parameters. The well-known story of Ali Baba and the Forty Thieves has inspired the AFT. Besides, the inappropriate objective function used in earlier research to extract parameters from solar PV models is recognized. The experimental findings demonstrate that the suggested approach performs better when compared to state-of-the-art algorithms. Between the measured data and the computed data for AFT, the root mean square error values for the five PV models, such as single diode model (SDM), double diode model (DDM), Photowatt-PWP201, STM6-40/36, and STP6-120/36, are respectively 7.72 × 10−04 ± 6.121 × 10−16, 7.412 × 10−04 ± 9.52 × 10−06, 2.052 × 10−03 ± 3.05 × 10−17, 0.001721922 ± 2.19 × 10−17, and 0.014450817 ± 3.42 × 10−16. In terms of accuracy, the obtained results indicate that the proposed AFT algorithm is more efficient than the other optimization techniques available in the literature. The excellent correlation between the estimated parameters from characteristic curves and observed data for SDM, DDM, Photowatt-PWP201, STM6-40/36, and STP6-120/36 demonstrates that the proposed AFT is a potential option among the techniques available in the literature. The Friedman and Wilcoxon tests have been used to assess the statistical validity of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Most of the current algorithms for solving distributed online optimization problems are based on the first-order method, which are simple in computation but slow in convergence. Newton's algorithm with fast convergence speed needs to calculate the Hessian matrix and its inverse, leading to computationally complex. A distributed online optimization algorithm based on Newton's step is proposed in this paper, which constructs a positive definite matrix by using the first-order information of the objective function to replace the inverse of the Hessian matrix in Newton's method. The convergence of the algorithm is proved theoretically and the regret bound of the algorithm is obtained. Finally, numerical experiments are used to verify the feasibility and efficiency of the proposed algorithm. The experimental results show that the proposed algorithm has an efficient performance on practical problems, compared to several existing gradient descent algorithms. [ABSTRACT FROM AUTHOR]

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

Since the twenty-first century, with the improvement of China's comprehensive national strength and cultural confidence, our country has paid more and more attention to the protection and development of Chinese traditional culture, and more and more attention to the protection and development of China's traditional culture, traditional culture has no longer become a simple symbol in history books but has gradually entered people's daily life with the characteristics of vitality and gradually evolved into various characteristics and forms and integrated into all walks of life. Under the background of the evolution of this culture, this paper discusses the application of Chinese decorative color for interior decoration design and puts forward an application model of Chinese decorative color for interior design based on the AlexNet network improved by Adam, BN, dropout, and data enhancement optimization algorithm. Through analysis, it is found that the AlexNet network improved based on the above optimization algorithm has a good application prospect for interior decoration design. For the traditional AlexNet network, the improved AlexNet network is obviously better than the traditional AlexNet network in color extraction and can greatly reduce the loss value in the process of color recognition, up to 70.77%. Finally, the application forms and decorative functions of Chinese decorative color elements based on the improved AlexNet network in interior decoration design are analyzed. Relevant research can not only promote the integration of new and new technologies into traditional art but also effectively carry forward Chinese traditional culture and promote the internationalization of Chinese color. [ABSTRACT FROM AUTHOR]

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

In order to study the deficiency of tuning PID controller in traditional boiler combustion air system, this paper proposes an embedded Internet of things monitoring system and puts forward a method of fuzzy PID controller with the help of nonlinear optimization algorithm to reasonably set the initial value of control parameters. Through the simulation comparison with the traditional engineering tuning PID controller, it is found that when the simulation process is t = 2000 s, the step disturbance with amplitude of 0.1 is added to the system input, and there will be slight disturbance in the combustion process. Through fuzzy PID and strong robustness support, the modeling error of the monitoring system model can be controlled between 10%∼25%, which proves that the nonlinear optimized PID has strong anti-interference and can meet the process requirements of the system. [ABSTRACT FROM AUTHOR]

In this article, a novel methodology has been created based on optimized fuzzy PI controller using position control optimization (PCO). This proposed PCO algorithm ensures better control of brushless direct current motor (BLDCM) in order to reduce steady-state error and oscillation as well as improve the system response by optimizing the speed and position of rotor. The suitability of this technique has been simulated in different stages, so as to infer the optimization in results. The main motivation of this paper is to get better efficiency and optimum response from BLDCM, by comparing its performance characteristics with existing methods. The detailed investigation research through simulation is carried out using MATLAB/Simulink. However, in order to obtain well-defined results, the PCO technique was applied and the performance obtained was highly optimized than previously applied techniques. Through the simulations, the proposed approach can outperform the existing approaches such as PSO and moth-flame algorithm (MFA). [ABSTRACT FROM AUTHOR]

Churn rate describes the rate at which customers abandon a product or service. Identifying churn-risk customers is essential for telecom sectors to retain old customers and maintain a higher competitive advantage. The purpose of this paper is to explore an effective method for detecting the risk of customer churn in telecom sectors through comparing the advanced machine learning methods and their optimization algorithms. Based on two different telecom datasets, Mutual Information classifier was firstly utilized to select the most critical features relevant to customer churn. Next, the controlled-ratio undersampling strategy was employed to balance both minority and majority classes. Key hyperparameter optimization algorithms of Grid Search, Random Search, and Genetic Algorithms were then combined to fit the three promising machine learning models-Random Forest, Support Vector Machines, and K-nearest neighbors into the customer churn prediction problem. Six evaluation metrics-Accuracy, Recall, Precision, AUC, F1-score and Mean Absolute Error, were last used to evaluate the performance of the proposed models. The experimental results have revealed that the RF algorithm optimized by Grid Search based on a low-ratio undersampling strategy (RF-GS-LR) outperformed other models in extracting hidden information and understanding future churning behaviors of customers on both datasets, with the maximum accuracy of 99% and 95% on the applied dataset 1-2 respectively. [ABSTRACT FROM AUTHOR]

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

Large-scale global optimization problems are ambitious and quite difficult to handle with deterministic methods. The use of stochastic optimization techniques is a good choice for dealing with these problems. Nature-inspired algorithms (NIAs) are stochastic in nature, computer-based, and quite easy to implement due to their population-based nature. The Grey wolf optimizer (GWO) and teaching-learning-based optimization are the most recently developed and well-known NIAs. GWO is based on the preying strategies of grey wolves while TLBO is based on the effect of the influence of a teacher on the output of learners in a class. NIAs are quite often stuck in the local basins of attraction due to the improper balancing of exploration versus exploitation. In this paper, an advanced amalgam of nature-inspired algorithms (ANIA) is developed by employing GWO and TLBO as constituent algorithms. Initially, an equal number of solutions are assigned to both NIAs to perform their search process of population evolution; then, in later iterations, the number of solutions are allocated to each constituent algorithm based on their individual performance and achievements gained by each algorithm in the previous iteration. The performance of an algorithm is determined at the end of iteration by calculating the ratio of total updated solutions to the total assigned solutions in the amalgam. The proposed strategy effectively balanced the exploration versus exploitation dilemma via compelling the parent algorithms to show continuous improvement during the whole course of the optimization process. The performance of the proposed algorithm, ANIA is evaluated on recently designed benchmark functions of large-scale global optimization problems. The approximated results found by the proposed algorithm are promising as compared to state-of-the-art evolutionary algorithms including the GWO and TLBO in terms of diversity and proximity. The proposed ANIA has tackled most of the benchmark functions efficiently in the parlance of evolutionary computing communities. [ABSTRACT FROM AUTHOR]

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

Autonomous anticollision of unmanned aerial vehicle (UAV) is one of the key technologies to realize intelligent decision-making and autonomous control, and it is of great significance to improve the flight safety and survivability of UAV in complex environment. Firstly, the UAV autonomous anticollision system configuration is constructed in this paper, and the UAV autonomous anticollision problem and related models are described. Then, the potential collision conflict prediction rules are defined, and a practical three-dimensional collision conflict prediction method is proposed. Finally, the UAV autonomous avoidance decision-making method is designed by using the optimization theory, and the corresponding simple and feasible flight control law is put forward. Numerical simulation results show that the proposed method can ensure the flight safety of UAV by relying on autonomous decision-making and control strategy, so as to realize the autonomous anticollision between a single UAV and non-cooperative dynamic obstacles in three-dimensional airspace. [ABSTRACT FROM AUTHOR]

Many optimization algorithms have been applied to solve high-dimensional instances of the 0-1 knapsack problem. However, these algorithms often fall into a local optimization trap and thus fail to obtain the global optimal solutions. To circumvent this shortcoming, a hybrid harmony search algorithm with distribution estimation is proposed in this paper. A few important features of the proposed algorithm are as follows: (i) the idea of probability distribution estimation is employed to design the adaptive search strategy, (ii) a fixed improvisation process is presented to improve the algorithm searching ability, (iii) a new method of initialization is used to ensure that the initialization is feasible harmony and (iv) an improved remediation approach is proposed to effectively repair the infeasible solutions. To assess the effectiveness of the proposed algorithm, some experiments are carried out. The experimental results reveal that the proposed algorithm is a reliable and promising alternative for solving the 0-1 knapsack problem. [ABSTRACT FROM AUTHOR]

During rainy times, the impact of outdoor vision systems gets considerably decreased owing to the visibility barrier, distortion, and blurring instigated by raindrops. So, it is essential to eradicate it from the rainy images for ensuring the reliability of outdoor vision system. To achieve this, several rain removal studies have been performed in recent days. In this view, this paper presents a new Faster Region Convolutional Neural Network (Faster RCNN) with Optimal Densely Connected Networks (DenseNet)-based rain removal technique called FRCNN-ODN. The presented involves weighted mean filtering (WMF) is applied as a denoising technique, which helps to boost the quality of the input image. In addition, Faster RCNN technique is used for rain detection that comprises region proposal network (RPN) and Fast RCNN model. The RPN generates high quality region proposals that are exploited by the Faster RCNN to detect rain drops. Also, the DenseNet model is utilized as a baseline network to generate the feature map. Moreover, sparrow search optimization algorithm (SSOA) is applied to choose the hyperparameters of the DenseNet model namely learning rate, batch size, momentum, and weight decay. An extensive experimental validation process is performed to highlight the effectual outcome of the FRCNN-ODN model and investigated the results with respect to several dimensions. The FRCNN-ODN method produced a higher UIQI of 0.981 for the applied image 1. Furthermore, on the applied image 2, the FRCNN-ODN model achieved a maximum UIQI of 0.982. Furthermore, the FRCNN-ODN algorithm produced a higher UIQI of 0.998 on the applied image 3. The simulation outcome showcased the superior outcome of the FRCNN-ODN (Optimal Densely Connected Networks) model with existing methods in terms of distinct measures. [ABSTRACT FROM AUTHOR]

Time-delayed feedback control is commonly used on the vehicle semiactive suspension system to improve ride comfort and safety. However, its performance on the suppression of road random excitation is less significant than that on the suppression of simple harmonic excitation. Therefore, this paper proposes a strategy of time-delayed feedback control with the vertical displacement of wheel and the method of optimizing its parameters based on equivalent harmonic excitation. The optimal parameters of the time-delayed feedback control are obtained in this way for the vehicle semiactive suspension system in its effective frequency band. The results of numerical simulation verify that the time-delayed feedback control with vertical wheel displacement and its parameter optimization based on equivalent harmonic excitation can significantly improve the ride comfort and stability. Its performance is much better than that of the passive suspension system. [ABSTRACT FROM AUTHOR]

Aiming at the energy multivariate heterogeneity of thermal system and natural gas system, a novel optimization model of integrated energy system considering thermal inertia and gas inertia is proposed. First, the dynamic characteristics of the thermal system in the integrated energy system are studied, and the inertia model of the heat network pipeline and thermal building is established. Second, the characteristics of natural gas pipeline network storage are studied, and the natural gas storage and pressure energy generation models are established. Then, the optimization objective of the minimum integrated operating cost of the integrated energy system is established, and the YALMIP solver is used to solve it. Finally, a numerical example is introduced to analyze the operating cost of the integrated energy system in different scenarios, and it is verified that the integrated energy system optimization model considering the inertia of the thermal and gas proposed in this paper can effectively improve the system regulation capability and reduce the system operating cost. [ABSTRACT FROM AUTHOR]

This paper proposes a stochastic search algorithm called improved hypercube optimisation search (HOS+) to find a better solution for optimisation problems. This algorithm is an improvement of the hypercube optimisation algorithm that includes initialization, displacement-shrink and searching area modules. The proposed algorithm has a new random parameters (RP) module that uses two control parameters in order to prevent premature convergence and slow finishing and improve the search accuracy considerable. Many optimisation problems can sometimes cause getting stuck into an interior local optimal solution. HOS+ algorithm that uses a random module can solve this problem and find the global optimal solution. A set of experiments were done in order to test the performance of the algorithm. At first, the performance of the proposed algorithm is tested using low and high dimensional benchmark functions. The simulation results indicated good convergence and much better performance at the lowest of iterations. The HOS+ algorithm is compared with other meta heuristic algorithms using the same benchmark functions on different dimensions. The comparative results indicated the superiority of the HOS+ algorithm in terms of obtaining the best optimal value and accelerating convergence solutions. [ABSTRACT FROM AUTHOR]

In this paper, photovoltaic generators (PVGs) are placed in radial distribution networks (RDNs) for reducing active power loss of one operation day by using three recently published metaheuristic algorithms including coot optimization algorithm (COOA), transient search algorithm (TSA), and crystal structure algorithm (CRSA). In one operation day, the variation of loads is considered, and the change of solar radiation over daytime is also taken. The study has two main contributions regarding the effectiveness of COOA: energy loss reduction and voltage improvement. COOA can reach high energy loss reduction, better solutions, and faster search speed than TSA and CRSA. In fact, COOA finds better energy loss than the algorithms by 1% and 1.77% for the IEEE 69-node system and 0.75% and 1.4% for the IEEE 85-node system. Furthermore, COOA is at least three times faster than CRSA and two times faster than TSA. As compared to a base system without PVGs, COOA can find better energy loss up to 60.96% and improve voltage up to 4.5%. Thus, COOA is a highly effective optimization tool with the optimal solution, high stability, and fast computation process for placing PVGs in RDNs. [ABSTRACT FROM AUTHOR]

In order to develop the dynamic effectiveness of the structures such as trusses, the application of optimisation methods plays a significant role in improving the shape and size of elements. However, conjoining two heterogeneous variables, nodal coordinates and cross-sectional elements, makes a challenging optimisation problem that is nonlinear, multimodal, large-scale with dynamic constraints. To handle these challenges, evolutionary and swarm optimisation algorithms can be robust and practical tools and show great potential to solve such complex problems. This paper proposed a comparative truss optimisation framework to solve two large-scale structures, including 314-bar and 260-bar trusses. The proposed framework consists of twelve state-of-the-art bio-inspired algorithms. The experimental results show that the marine predators algorithm (MPA) performed best compared with other algorithms in terms of convergence speed and the quality of the proposed designs of the trusses. [ABSTRACT FROM AUTHOR]

In this paper, we investigate two ideas in the context of the interpolation-based optimization paradigm tailored to derivative-free black-box optimization problems. The proposed architecture maintains a radial basis function interpolation model of the actual objective that is managed according to a trust-region globalization scheme. We focus on two distinctive ideas. Firstly, we explore an original sampling strategy to adapt the interpolation set to the new trust region. A better-than-linear interpolation model is guaranteed by maintaining a well-poised supporting subset that pursues a near regular simplex geometry of n + 1 points plus the trust-region center. This strategy improves the geometric distribution of the interpolation points whilst also optimally exploiting the existing interpolation set. On account of the associated minimal interpolation set size, the better-than-linear interpolation model will exhibit curvature, which is a necessary condition for the second idea. Therefore, we explore the generalization of the classic spherical to an ellipsoidal trust-region geometry by matching the contour ellipses with the inverse of the local problem hessian. This strategy is enabled by the certainty of a curved interpolation model and is introduced to accounts for the local output anisotropy of the objective function when generating new interpolation points. Instead of adapting the sampling strategy to an ellipsoid, we carry out the sampling in an affine transformed space. The combination of both methods is validated on a set of multivariate benchmark problems and compared with ORBIT. [ABSTRACT FROM AUTHOR]

This paper studies the energy efficiency optimization problem for coordinated multipoint (CoMP)-enabled and backhaul-constrained ultra-dense small-cell networks (UDNs). Energy efficiency is an eternal topic for future wireless communication networks; however, taking actual bottleneck of the backhaul link and the coordinated network architecture into consideration, it is difficult to find an effective way to improve the energy efficiency of the network. Aiming at this problem, we propose to combine cell association, subchannel allocation, backhaul resource allocation, and sleep/on of the cells together to develop an optimization algorithm for energy efficiency in UDN and then solve the formulated energy efficiency optimization problem by means of improved modified particle swarm optimization (IMPSO) and linear programming in mathematics. Simulation results indicate that nearly 13 % energy cost saving and 21 % energy efficiency improvement can be obtained by combining IMPSO with linear programming, and the backhaul link data rate can be improved by 30 % as the number of small cells increases. From the results, it can be found that by combining IMPSO with linear programming, the proposed algorithm can improve the network energy efficiency effectively at the expense of limited complexity. [ABSTRACT FROM AUTHOR]

Differential pricing of trains with different departure times caters to the taste heterogeneity of the time-dependent (departure time) demand and then improves the ticket revenue of railway enterprises. This paper studies optimal differential pricing for intercity high-speed railway services. The distribution features of the passenger demand regarding departure times are analyzed, and the time-dependent demand is formulated; a passenger assignment method considering departure periods and capacity constraints is constructed to evaluate the prices by simulating the ticket-booking process. Based on these, an optimization model is constructed with the aim of maximizing the ticket revenue and the decision variables for pricing train legs. A modified direct search simulated annealing algorithm is designed to solve the optimization model, and three random generation methods of new solutions are developed to search the solution space efficiently. Experimental analysis containing dozens of trains is performed on Wuhan-Shenzhen high-speed railway in China, and price solutions with different elastic demand coefficients (ϕ) are compared. The following results are found: (i) the optimization algorithm converges stably and efficiently and (ii) differentiation is shown in the price solutions, and the optimized ticket revenue is influenced greatly by ϕ , increasing by 7%–21%. [ABSTRACT FROM AUTHOR]

This paper provides a method to numerically measure the quality of working life based on the reduction of human resource risks. It is conducted through the improved metaheuristic grasshopper optimization algorithm in two phases. First, a go-to study is carried out to identify the relationship between quality of working life and human resource risks in the capital market and to obtain the factors from quality of working life which reduce the risks. Then, a method is presented for the numerical measurement of these factors using a fuzzy inference system based on an adaptive neural network and a new hybrid method called the improved grasshopper optimization algorithm. This algorithm consists of the grasshopper optimization algorithm and the bees algorithm. It is found that the newly proposed method performs better and provides more accurate results than the conventional one. [ABSTRACT FROM AUTHOR]

The mismatching of image features affects the calculation of the fundamental matrix and then leads to poor estimation accuracy of SLAM visual odometry. Aiming at the above problems, a visual odometry optimization method based on feature matching is proposed. Firstly, the initial matching set is roughly filtered by the minimum distance threshold method, and then, the relative transformation relationship between images is calculated by the RANSAC algorithm. If it conforms to the transformation relationship, it is an interior point. The iteration result with most interior points is the correct matching result. Then, the homography transformation between images is calculated, and the fundamental matrix is calculated by it. The interior points are determined by epipolar geometric constraints, and the fundamental matrix with the most interior points is obtained. Finally, the effects of the visual odometry optimization algorithm are verified by the TUM data set from two aspects: feature matching and fundamental matrix calculation. The experimental results show that an improved feature matching algorithm can effectively remove mismatched feature points while improving the operation efficiency. At the same time, the accuracy of feature point matching is increased by 15.8%. The fundamental matrix estimation algorithm not only improves the calculation accuracy of the fundamental matrix but also increases the interior point rate by 11.9%. A theoretical basis for improving the accuracy estimation of visual odometry will be provided. [ABSTRACT FROM AUTHOR]

Innovation and institutional governance are the key enabling factors of cluster ecosystem development. Its synergistic effects play an important role in enhancing ecosystem competitiveness. In this paper, pseudocode language is applied to cluster ecosystem cooperative model reasoning. The coordination and optimization of the innovation system and institutional governance system were studied in a biomedical cluster. Besides, Pearson algorithm was used to test the correlation degree of elements in three Chinese biomedical clusters. The results show that, in Zhangjiang and Nanchang biomedical clusters, the synergistic correlation coefficient between the innovation system and the institutional governance system fluctuates around 0.8. However, in Tonghua biomedical cluster, the synergy correlation coefficient fluctuated around -0.2. The fluctuation range between the two clusters was large. After adjusting the range of order parameters, the rank of synergy trend was Zhangjiang > Nanchang > Tonghua. Finally, further analysis shows that Zhangjiang and Nanchang biomedical clusters can achieve the optimal synergy state by adjusting innovation and institutional governance, but Tonghua cannot. Therefore, the collaboration between the innovation system and institutional governance system provides some reference for the high-quality development of the cluster ecosystem. [ABSTRACT FROM AUTHOR]

The purpose of this paper is twofold. First, it introduces a new hybrid computational intelligence algorithm to the optimization community. This novel hybrid algorithm has hyperheuristic (HH) neighborhood search movements embedded into a recently introduced migrating birds optimization (MBO) algorithm. Therefore, it is called HHMBO. Second, it gives the necessary mathematical model for a shift scheduling problem of a manufacturing company defined by including the fairness perspective, which is typically ignored especially in manufacturing industry. Therefore, we call this complex optimization problem fairness oriented integrated shift scheduling problem (FOSSP). HHMBO is applied on FOSSP and is compared with the well-known simulated annealing, hyperheuristics, and classical MBO algorithms through extended computational experiments on several synthetic datasets. Experiments demonstrate that the new hybrid computational intelligence algorithm is promising especially for large sized instances of the specific problem defined here. HHMBO has a high exploration capability and is a promising technique for all optimization problems. To justify this assertion, we applied HHMBO to the well-known quadratic assignment problem (QAP) instances from the QAPLIB. HHMBO was up to 14.6% better than MBO on converging to the best known solutions for QAP benchmark instances with different densities. We believe that the novel hybrid method and the fairness oriented model presented in this study will give new insights to the decision-makers in the industry as well as to the researchers from several disciplines. [ABSTRACT FROM AUTHOR]

The limited instantaneous overload available and the curved trajectory lead to adaptivity problems for the proportional navigation guidance (PNG) of a guided mortar with a fixed-canard trajectory correction fuze. In this paper, the optimization of a PNG law with gravity compensation is established. Instead of using the traditional empirical method, the selection of the proportional navigation constants is formulated as an optimization problem, which is solved using an intelligent optimization algorithm. Two optimization schemes are proposed for constructing corresponding optimization models. In schemes 1 and 2, the sum squared error between the impact point and target and the circular error probability (CEP), respectively, are taken as the objective function. Monte Carlo simulations are conducted to verify the effectiveness of the two optimization schemes, and their guidance performance is compared through trajectory simulations. The simulation results show that the impact point dispersion can be efficiently reduced under both proposed schemes. Scheme 2 achieves a lower CEP, which is approximately 2.9 m and 2.4 times smaller than that achieved by scheme 1. Moreover, the mean impact point is closer to the target. [ABSTRACT FROM AUTHOR]

Cloud computing platforms have been extensively using scientific workflows to execute large-scale applications. However, multiobjective workflow scheduling with scientific standards to optimize QoS parameters is a challenging task. Various metaheuristic scheduling techniques have been proposed to satisfy the QoS parameters like makespan, cost, and resource utilization. Still, traditional metaheuristic approaches are incompetent to maintain agreeable equilibrium between exploration and exploitation of the search space because of their limitations like getting trapped in local optimum value at later evolution stages and higher-dimensional nonlinear optimization problem. This paper proposes an improved Fruit Fly Optimization (IFFO) algorithm to minimize makespan and cost for scheduling multiple workflows in the cloud computing environment. The proposed algorithm is evaluated using CloudSim for scheduling multiple workflows. The comparative results depict that the proposed algorithm IFFO outperforms FFO, PSO, and GA. [ABSTRACT FROM AUTHOR]

In the context of cloud computing, one problem that is frequently encountered is task scheduling. This problem has two primary implications, which are the planning of tasks on virtual machines and the attenuation of performance. In order to address the problem of task scheduling in cloud computing, requisite nontraditional optimization attitudes to attain the optima of the problem, the present paper puts forth a hybrid multiple-objective approach called hybrid grey wolf and whale optimization (HGWWO) algorithms, that integrates two algorithms, namely, the grey wolf optimizer (GWO) and the whale optimization algorithm (WOA), with the purpose of conjoining the advantages of each algorithm for minimizing costs, energy consumption, and total execution time needed for task implementation, beside that improving the use of resources. Assessment of the aims of the proposed approach is carried out with the help of the tool known as CloudSim. As pointed out by the results of the experimental work undertaken, the proposed approach has the capability of performing at a superior level by comparison to the original algorithms GWO and WOA on their own with regard to costs, energy consumption, makespan, use of resources, and degree of imbalance. [ABSTRACT FROM AUTHOR]

Pharmaceutical waste management is a significant concern that poses risks to human and environmental health. The ineffective management of expired and unused medications can harm individuals and communities. This study proposes a novel approach to address the issue of pharmaceutical waste management by developing a location-routing problem (LRP) model using mixed-integer linear programing (MILP) to optimize the collection and disposal of pharmaceutical waste. The proposed model aims to minimize transportation costs, construction of collection centers, disposal costs, and carbon dioxide emissions, making it a cost-effective and environmentally sustainable approach to managing pharmaceutical waste. Initially, the feasibility, validity, and efficiency of the proposed model are examined by solving the problem in the GAMS software using CPLEX solver for small-scale problems. Sensitivity analyses are conducted to ensure the accuracy, reliability, robustness, and usefulness of the mathematical model for decision-making. In view of the inherent computational complexity of the proposed model, which is classified as nondeterministic polynomial time-hard and poses considerable difficulties when exact solutions are sought for large-scale problems, the present study resorts to two metaheuristic algorithms, specifically particle swarm optimization (PSO) and genetic algorithm (GA), as a means minimizing the computational burden. The results indicate that GA outperforms PSO in terms of objective function and solution time, with an average improvement of approximately 1% and 20%, respectively. The proposed model and algorithms provide a comprehensive approach to addressing the critical issue of pharmaceutical waste management, benefiting the healthcare industry, and society as a whole. [ABSTRACT FROM AUTHOR]

Biometric plays a vital role in human authentication systems. Unimodal and multimodal biometrics have been active research areas for the past few decades. The investigation of palmprint recognition under various illuminations, rotations, and translations is a challenging task. The research work on multimodal palmprint recognition systems has widely increased to improve the recognition rate and reduce execution time. In this article, a multimodal palmprint biometric system is formed by combining the left and right palmprint images to obtain an optimal recognition rate. A modified multilobe ordinal filter (MMLOF) is used to extract the features. Feature-level fusion is used to fuse the left and right palmprint images. This results in a high-dimension feature vector that requires larger memory to store. It creates redundant and irrelevant features that affect the recognition rate. To overcome these limitations, the optimal MMOF features are extracted by optimization techniques such as particle swarm optimization (PSO) and the genetic algorithm (GA). Finally, PSO and GA optimization algorithms are wrapped with the nearest neighbor classifier (NN) to evaluate the fitness function. The experimental analyses are conducted to identify the performance of GA and PSO using the IITD palmprint dataset. The 1st order MMLOF with GA (multimodal) converges faster and outperforms the 1st order MMLOF with PSO (multimodal) and obtains an optimal recognition rate of 96.95%. [ABSTRACT FROM AUTHOR]

Aiming at the problem of optimal path planning for deviation correction of full-width horizontal axis roadheader in the confined roadway of the coal mine, a deviation correction path planning method of full-width horizontal axis roadheader based on improved particle swarm optimization (I-PSO) algorithm is proposed. First, according to the driving characteristics of roadheader and deviation correction strategy, the mathematical model of full-width horizontal axis roadheader is established, and the constraints based on pose analysis are determined. Then, taking the shortest total distance from the initial position to the terminal position of roadheader as the objective function of path planning, an optimization model of deviation correction path planning is established. Finally, the I-PSO algorithm is proposed to quickly and accurately solve the deviation correction path model, in order to plan a safe and shorter distance path of full-width horizontal axis roadheader. In the I-PSO algorithm, the fitness difference of particles is used to dynamically adjust the inertia weight parameters to optimize the exploration of the whole or part of activity space, and the learning factor is adjusted to speed up particle convergence and optimization of particles, and a random factor is introduced to update the position of particles to avoid local traps as much as possible. The performance test of the I-PSO algorithm shows that the I-PSO algorithm is superior to other algorithms. The EJM340/4-2 full-width horizontal axis roadheader is used as the simulation object to verify the effectiveness of the I-PSO algorithm in the deviation correction path planning of roadheader. The simulation results show that the I-PSO algorithm proposed can quickly and accurately plan a safe and shorter distance path than the WPSO algorithm and CPSO algorithm and has better convergence and stability. It has laid a good foundation for the intelligent navigation of full-width horizontal axis roadheader in coal mine. [ABSTRACT FROM AUTHOR]

The implementation of the urban underground logistics system (ULS) can effectively mitigate the contradiction between the surging logistics demand and the increased negativity of urban logistics. The widespread implementation of ULS still suffers from a lack of research into its operation in the marketplace, although the research on ULS system technology and network design appears to be sufficient. A new supply chain for logistics service based on ULS (ULS-SSC) was proposed, as ULS embedded in the urban logistics system could lead to the evolution of the role of supply chain participants. This article analyzed the organizational structure and operation characteristics of ULS-SSC and designed a top-down ULS-SSC operation process model based on the designed functional structure and subsystems relationship using the hierarchical colored Petri net (HCPN). The simulation results show that the integrated information management platform based on ULS can integrate urban logistics service supply chain resources and operate effectively under the two main service modes designed. The high-time delay intermediate links can be upgraded by system optimization, and the links with initial pickup and terminal distribution can be improved through outsourcing and supply chain collaboration. The findings provide new insights into the feasibility of the operation of ULS in the market and help stimulate the implementation of ULS. [ABSTRACT FROM AUTHOR]