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By caching popular content on edge servers closer to users to respond to users' content requests in 6G networks, the transmission load of backhaul links can be reduced. However, the time-varying characteristics of content prevalence leads to the issue that the cache content may not match the user's needs, resulting in a decrease in cache success ratio. To solve these issues, we proposed a cache distribution strategy based on epidemic dynamics (CDSED) for 6G edge network. First, a 6G edge caching content model (6G ECCM) is constructed to establish the process of cache content propagation among users as an infectious disease propagation process, analyze the distribution of users' interest in cache content and obtain the cache content state probability prediction equation, and use the cache content state probability prediction equation to predict the cache content prevalence. Second, based on the predicted prevalence results, a prevalence predictive genetic-annealing cache content algorithm (PGAC) is proposed with the optimization objective of maximizing the cache success ratio. The algorithm designs the selection function of the traditional genetic algorithm as a simulated annealing selection function based on the cache content success ratio, which avoids the defect of the genetic algorithm that converges to the locally optimum cache strategy too early and enhances the cache success ratio. Finally, the optimum cache content decision is solved by iterative alternation. Simulation results demonstrate that CDSED strategy can enhance cache success ratio than the LRU strategy, the LFU strategy, and the MPC strategy. [ABSTRACT FROM AUTHOR]

In order to improve the calculation efficiency and reduce the uncertainty in the reliability evaluation process of flat car underframe, a new method based on an improved genetic simulated annealing algorithm-back propagation neural network (GSA-BP) and probability box is proposed. Firstly, a certain type of flat car underframe is taken as the research object, genetic evolution algorithm (GA) and simulated annealing algorithm (SA) are utilized to optimize the weights and thresholds of the GSA-BP neural network and obtain the best initial parameter values. Applying the values, the BP neural network is trained, and the improved GSA-BP neural network is established which is verified by test functions. Secondly, the central plate sub-model of the flat car underframe is obtained by sub-model technology and the correctness of its boundary conditions is tested. On this basis, the static strength analysis of the sub-model is carried out. Finally, the improved GSA-BP neural network and probability box are utilized to evaluate the reliability of the sub-model of flat car underframe, and the Monte Carlo method is utilized to verify it. The results show that the proposed method not only improves the accuracy of reliability evaluation but also shortens the calculation time from 10 hours to 6 hours. The calculation efficiency is increased by 40%, which further verifies its superiority and feasibility in structural reliability evaluation. [ABSTRACT FROM AUTHOR]

In this paper, the integrated demand response (IDR) optimization problem is studied under the consideration of herd mentality. The contradiction between power supply and demand is becoming increasingly prominent. IDR can maintain the balance between power supply and demand, ensuring the safe operation of the system. The establishment of an IDR model that takes into account the herd mentality of consumers and the renewable energy supply, fully enhances consumers’ enthusiasm for participating in demand response and input renewable energy into the comprehensive energy system to improve energy conversion rate and supply, which is closer to the actual IDR process. Using the Genetic Simulated Annealing Algorithm (GSA) to solve multi-objective optimization problems with comprehensive energy efficiency, operating costs and peak-to-average ratio as optimization objectives has significant advantages in terms of iteration speed and solution accuracy. The analysis of the simulation shows that the IDR optimization model can not only improve the stability and comprehensive energy efficiency of the system but also reduce the operating cost of the system, which proves the effectiveness of the proposed method.

In the context of global climate warming and rising sea levels, the frequency of tropical cyclones in the South China Sea region has shown a significant upward trend in recent years. Consequently, the coastal areas of the South China Sea are increasingly vulnerable to storm surge disasters induced by typhoon, posing severe challenges to disaster prevention and mitigation in affected cities. Therefore, establishing a multi-indicator assessment system for typhoon storm surges is crucial to provide scientific references for effective defense measures against disasters in the region. This study examines 25 sets of typhoon storm surge data from the South China Sea spanning the years 1989-2020. A comprehensive assessment system was constructed to evaluate the damages caused by storm surges by incorporating the maximum wind speed of typhoons. To reduce redundancy among multiple indicators in the assessment system and enhance the stability and operational efficiency of the storm surge-induced disaster loss model, the entropy method and bootstrap toolbox were employed to process post-disaster data. Furthermore, the genetic simulated annealing algorithm was utilized to optimize a backpropagation neural network intelligent model (GSA-BP), enabling pre-assessment of the risks associated with storm surge disasters induced by typhoon and related economic losses. The results indicate that the GSA-BP model outperforms the genetic algorithm optimized BP model (GA-BP) and the simulated annealing algorithm-optimized BP model (SA-BP) in terms of predicting direct economic losses caused by storm surges. The GSA-BP model exhibits higher prediction accuracy, shorter computation time, and faster convergence speed. It offers a new approach to predicting storm surge losses in coastal cities along the South China Sea. [ABSTRACT FROM AUTHOR]

As the subway system is beginning to operate interval trains in the early morning, it is necessary to discuss the first train timetable synchronization with interval trains. In this paper, with the consideration of both ordinary trains and interval trains, we study the first train timetable problem with minimum total transfer waiting time. The first train timetable model is formulated as a mixed integer programming problem and a genetic simulated annealing algorithm is developed to deal with the case study of Beijing subway network. The proposed model and the algorithm are effective in improving the performance of the first train timetable synchronization. After optimization, the total transfer waiting time and the number of long waiting time transfer directions for Beijing subway network have decreased significantly. The results indicate that operating interval trains is an effective way to solve the problem of long waiting time in early morning. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Railway Science & Engineering is the property of Journal of Railway Science & Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The traditional BP-PID control algorithm uses the gradient descent method to solve, which has problems such as slow convergence speed, easy trapping in local extremum, and performance degradation under low signal-to-noise ratio (LSNR) conditions. In order to solve the above problems, a BP PID belt conveyor speed control method (ImGSAA-BP-PID) based on improved genetic simulated annealing algorithm (ImGSAA) optimization is proposed. Firstly, the values of crossover and mutation probabilities are correlated with the iteration time. The inverse cosine function is introduced to enhance the dynamic adjustment and nonlinear change adaptability of GSAA. Secondly, by weighting the traditional Metropolis criterion, a weighted Metropolis criterion is proposed to modify the new population individuals and improve the noise robustness of genetic simulated annealing algorithm (GSAA). Finally, ImGSAA is used to optimize the initial parameters of BP-PID, automatically determining the optimal parameter combination for BP-PID. It improves its real-time parameter tuning, control precision, and adaptability to the LSNR environment. The experimental results show the following points. ① ImGSAA only needs 11 iterations to converge, indicating that optimizing the GSAA using the proposed improved crossover and mutation strategies and weighted Metropolis criteria can effectively improve the convergence speed and real-time performance of the algorithm. ② The control error of ImGSAA-BP-PID is −0.468 5-0.572 3 m/s, which is 224.88%, 104.07%, and 38.33% higher than the control methods based on genetic algorithm (GA)-BP PID, particle swarm optimization (PSO)-BP PID, and GSAA-BP-PID, respectively. ③ The performance of ImGSAA is least affected by LSNR. It converges to the global optimal solution after 15 iterations, which has strong noise robustness. ④ Under LSNR conditions, the average control error of ImGSAA-BP-PID decreases by 3.54%. The control performance is significantly better than GA-BP-PID, PSO-BP-PID, and GSAA-BP-PID, which better meets the practical engineering application requirements.

In the context of global climate warming and rising sea levels, the frequency of tropical cyclones in the South China Sea region has shown a significant upward trend in recent years. Consequently, the coastal areas of the South China Sea are increasingly vulnerable to storm surge disasters induced by typhoon, posing severe challenges to disaster prevention and mitigation in affected cities. Therefore, establishing a multi-indicator assessment system for typhoon storm surges is crucial to provide scientific references for effective defense measures against disasters in the region. This study examines 25 sets of typhoon storm surge data from the South China Sea spanning the years 1989–2020. A comprehensive assessment system was constructed to evaluate the damages caused by storm surges by incorporating the maximum wind speed of typhoons. To reduce redundancy among multiple indicators in the assessment system and enhance the stability and operational efficiency of the storm surge-induced disaster loss model, the entropy method and bootstrap toolbox were employed to process post-disaster data. Furthermore, the genetic simulated annealing algorithm was utilized to optimize a backpropagation neural network intelligent model (GSA-BP), enabling pre-assessment of the risks associated with storm surge disasters induced by typhoon and related economic losses. The results indicate that the GSA-BP model outperforms the genetic algorithm optimized BP model (GA-BP) and the simulated annealing algorithm-optimized BP model (SA-BP) in terms of predicting direct economic losses caused by storm surges. The GSA-BP model exhibits higher prediction accuracy, shorter computation time, and faster convergence speed. It offers a new approach to predicting storm surge losses in coastal cities along the South China Sea.

In this paper, a novel metasurface with shared perforating holes is proposed. The metasurface consists of two types of meta-surface units. The shared perforating holes of adjacent units are realized on the covering layer using the periodic property of the metasurface unit which contributes to widening the operating bandwidth of the unit. To satisfy the periodicity condition, 4 × 4 units are formed into a subarray. Then, for the first time, Genetic Simulated Annealing Algorithm (GSAA) and Convolutional Operation (CO) are used simultaneously to optimize the distribution of the subarray. The optimized meta-surface leads to a wider radar cross section (RCS) reduction bandwidth and more dispersed scattering patterns. The measured result shows a 10 dB RCS reduction observed for 126% of the relative bandwidth (8.2–36.4 GHz). The proposed strategy provides a new idea for the meta-surface to realize ultra-wideband RCS reduction. [ABSTRACT FROM AUTHOR]

With the popularization of PREV, the disorderly charging of large-scale PREV is likely to cause load peaks and affect the safety of the power grid. Therefore, aiming at this charging problem in commercial parking lot scenario, this paper proposed a vehicle admission mechanism to maximize the number of vehicles that could be fully charged within the predetermined time. It designed a charging scheduling of PREV for multi-objective optimization problem by using the genetic algorithm simulated annealing (GASA) based on the entropy weight method which determined the weights of the fitness function. This strategy comprehensively optimized the profits of parking lot operators and the charging satisfaction degree of PREV owners. The results show that the PREV charging scheduling based on GASA has good performance compared with the extreme case (disorderly charging with 600 vehicles). This method improves the profits, charging satisfaction degree and the value of objective function by 12.3%,109.7% and 35.2% respectively. In addition, it can effectively smooth the load distribution and achieve a win-win situation for parking lot operators and PREV owners under the premise of ensuring the safety of the power grid. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To reduce the operating cost and running time of demand responsive transit between urban and rural areas, a DBSCAN K-means (DK-means) clustering algorithm, which is based on the density-based spatial clustering of applications with noise (DBSCAN) and K-means clustering algorithm, was proposed to cluster pre-processing and station optimization for passenger reservation demand and to design a new variable-route demand responsive transit service system that can promote urban–rural integration. Firstly, after preprocessing the reservation demand through DBSCAN clustering algorithm, K-means clustering algorithm was used to divide fixed sites and alternative sites. Then, a bus scheduling model was established, and a genetic simulated annealing algorithm was proposed to solve the model. Finally, the feasibility of the model was validated in the northern area of Yongcheng City, Henan Province, China. The results show that the optimized bus scheduling reduced the operating cost and running time by 9.5% and 9.0%, respectively, compared with those of the regional flexible bus, and 4.5% and 5.1%, respectively, compared with those of the variable-route demand response transit after K-means clustering for passenger preprocessing. [ABSTRACT FROM AUTHOR]

The contents of cellulose and hemicellulose (C and H) in corn stover (CS) have an important influence on its biochemical transformation and utilization. To rapidly detect the C and H contents in CS by near-infrared spectroscopy (NIRS), the characteristic wavelength selection algorithms of backward partial least squares (BIPLS), competitive adaptive reweighted sampling (CARS), BIPLS combined with CARS, BIPLS combined with a genetic simulated annealing algorithm (GSA), and CARS combined with a GSA were used to select the wavelength variables (WVs) for C and H, and the corresponding regression correction models were established. The results showed that five wavelength selection algorithms could effectively eliminate irrelevant redundant WVs, and their modeling performance was significantly superior to that of the full spectrum. Through comparison and analysis, it was found that CARS combined with GSA had the best comprehensive performance; the predictive root mean squared errors of the C and H regression model were 0.786% and 0.893%, and the residual predictive deviations were 3.815 and 12.435, respectively. The wavelength selection algorithm could effectively improve the accuracy of the quantitative analysis of C and H contents in CS by NIRS, providing theoretical support for the research and development of related online detection equipment. [ABSTRACT FROM AUTHOR]

To ensure the normal operation of China Railway Express, the Inland Railway Container Center Station (IRCCS) needs to replenish the freight sources outside its attraction area. The homogenization of the China Railway Express route, overlapping of attraction area and chaotic transportation organization brings about disordered competition of freight. Based on the attraction area division of the IRCCS, this paper establishes a mixed integer programming model, with minimum transportation time, transportation cost and carbon emission as target. The simulated annealing algorithm is used to solve the problem, and the Harbin Railway Container Center Station is taken as an example to determine the freight collection mode, operating route and operation frequency of China Railway Express. The results show that: The transportation costs, time and carbon emissions have different degree of influence on China Railway Express operation plan, from the largest to the smallest, carbon emissions, transportation costs, time costs. Under different objective weights, the influence on the freight collection mode and transportation route is great, while the frequency impact is small.

This study considers a service-deployment problem of C4ISR(Command, Control, Communication, Computer, Intelligence, Surveillance and Reconnaissance) system under service-oriented architecture. In order to make C4ISR sufficiently agile to meet the challenges brought by diverse combat missions and highly adversarial combat processes, this paper adopts a distributed deployment method to deploy the service processes that constitute C4ISR in multiple data centers. Considering the service information requirements and the location of specific data, a deployment constraint for a service is proposed. Further, in order to improve resource utilization and reduce communication overhead, this paper establishes a distributed service deployment model facing the characteristics of C4ISR. For the large search space and complex constraints of the optimal problem, a multi-objective genetic simulated annealing algorithm is proposed by designing the neighboring individual generation rules and combining the simulated annealing mechanism and the genetic algorithm to enhance the ability of both local search and global search. The simulation results show that this method can produce a high-quality C4ISR service-deployment scheme effectively.

The Back Propagation (BP) neural network has the problems of low accuracy and poor convergence in the process of binocular camera calibration. A method based on BP neural network optimized by improved genetic simulated annealing algorithm (IGSAA-BP) is proposed to solve these problems to complete the binocular camera calibration. The method of combining Gaussian scale space and Harris corner detection operator is used for corner detection. A matched algorithm of homonymous corner is proposed by combining point-to-point spatial mapping and grid motion statistics. The pixel values of the homonymous corner and three-dimensional coordinate values are taken as the input and output of BP neural network respectively. The crossover and mutation probability of genetic simulated annealing algorithm and the annealing criterion are improved, the IGSAA-BP neural network is used to calibrate the binocular camera. The average calibration accuracy of BP neural network and IGSAA-BP neural network is 0.71mm and 0.03mm, respectively. The average calibration accuracy of binocular camera is improved by 96%. The iteration speed is increased by 20 times and global optimization ability is improved. It can be seen that the IGSAA-BP neural network can improve the calibration accuracy of binocular camera and accelerate convergence speed.

Copyright of Journal of South China University of Technology (Natural Science Edition) is the property of South China University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, a proactive and reactive multi-project scheduling problem is addressed. This problem is related to the influences of uncertain factors, which leads to a deviation between actual scheduling and baseline scheduling, and a recovery strategy is established in order to generate a baseline scheduling scheme. This paper introduces a proactive multi-project scheduling sub-model. When the activity is interrupted, the proactive scheduling scheme is used as the baseline scheduling scheme, which is embedded in the reactive scheduling, and then, the reactive scheduling sub-model is established. The proposed model can be used to generate alternative schedules, and to meet this need, a genetic simulated annealing algorithm is proposed. A buffer change operator (SC) and a crossover operator are designed in a genetic simulated annealing algorithm so that in the early stages of the algorithm, an optimum individual is produced and protected. The performance comparison shows that the genetic simulated annealing algorithm significantly outperforms the previous algorithms.

The contents of cellulose and hemicellulose (C and H) in corn stover (CS) have an important influence on its biochemical transformation and utilization. To rapidly detect the C and H contents in CS by near-infrared spectroscopy (NIRS), the characteristic wavelength selection algorithms of backward partial least squares (BIPLS), competitive adaptive reweighted sampling (CARS), BIPLS combined with CARS, BIPLS combined with a genetic simulated annealing algorithm (GSA), and CARS combined with a GSA were used to select the wavelength variables (WVs) for C and H, and the corresponding regression correction models were established. The results showed that five wavelength selection algorithms could effectively eliminate irrelevant redundant WVs, and their modeling performance was significantly superior to that of the full spectrum. Through comparison and analysis, it was found that CARS combined with GSA had the best comprehensive performance; the predictive root mean squared errors of the C and H regression model were 0.786% and 0.893%, and the residual predictive deviations were 3.815 and 12.435, respectively. The wavelength selection algorithm could effectively improve the accuracy of the quantitative analysis of C and H contents in CS by NIRS, providing theoretical support for the research and development of related online detection equipment.

Copyright of Journal on Communication / Tongxin Xuebao is the property of Journal on Communications Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The calculation of factor of safety and the determination of the critical slip surface (a slip surface with the minimal factor of safety) are essential steps in stability analysis of soil slopes. In this study, a genetic simulated annealing algorithm (GSA), which combines the genetic algorithm and the simulated annealing algorithm, is proposed to quickly and accurately locate the critical slip surface of soil slopes. The corresponding factors of safety of slip surfaces are calculated based on the stress field of soil slopes that are computed by finite element method (FEM). The search ability of GSA for the critical slip surface of an earth-rock dam slope is proved to be more accurate and efficient compared with the golden-section method based on the same stress field calculated by FEM. In addition, a practical slope with complex soil layering is presented to further illustrate the reasonability of the calculation results of GSA in conjunction with FEM. The numerical simulations shows that the factor of safety and the critical slip surface determined using GSA with FEM is in good agreement with that using the shear strength reduction method. [ABSTRACT FROM AUTHOR]

• Rapid determination of rice protein content was completed using NIR spectroscopy. • BiPLS-GSA and BiPLS-SABPSO was proposed to select feature wavelengths of protein. • BiPLS-SABPSO was superior to BiPLS-GSA in feature wavelengths selection. • Detection accuracy of regression model was efficiently improved using BiPLS-SABPSO. Protein content is a key indicator for rice nutritional value, and its accurate determination has become a necessary tool for rice quality evaluation and breeding. To solve the shortcomings of slow and costly testing by traditional methods, a rapid detection model of rice protein content was constructed based on near-infrared spectroscopy coupled with feature wavelength selection. The backward interval partial least squares (BiPLS) was combined with the genetic simulated annealing algorithm (GSA) and the simulated annealing binary particle swarm optimization algorithm (SABPSO), respectively, to construct BiPLS-GSA and BiPLS-SABPSO for selecting protein feature wavelengths, thereby establishing a corresponding partial least squares quantitative calibration model. Among them, the regression model established using feature wavelengths selected by BiPLS-SABPSO had the best performance. The determination coefficient of the model for the validation set and the independent test set were 0.949 and 0.956, the root mean square error were 0.174% and 0.214%, the relative root mean square error were 2.621% and 3.118%, and the residual predictive deviation being 4.235 and 4.500, respectively, which could meet the requirement for rapid and accurate determination of rice protein content. The combination of BiPLS-SABPSO and near-infrared spectroscopy has developed into a new method to achieve fast and reliable determination of rice protein content, providing an alternative strategy for rapid qualitative detection of related agricultural products. [ABSTRACT FROM AUTHOR]

• Constructed a farmer portrait model based on production performance segmentation. • Proposed and validated an optimized clustering algorithm (GASA-WFCM). • Designed a labeling system to realize the description of farmers' portraits. • Identified typical characteristics of farmers with different production performance. • Findings can provide decision support for improving farmers' production effect. To identify the typical characteristics of farmers with different production performance, this paper constructs a farmer portrait model from the perspective of production performance segmentation. Firstly, we constructed the evaluation index system of production performance and quantified grape farmers' production performance. Then, an optimized clustering algorithm (GASA-WFCM) was proposed to cluster the grape farmers based on their production performance, which was based on fuzzy C-mean algorithm, and incorporating the genetic simulated annealing algorithm to overcome the problem of sensitive initial clustering centers and tendency to fall into local optimum, and the ReliefF algorithm was also integrated to strengthen the contribution ability of effective features to clustering. Finally, we designed a labeling system and feature transformation quantification method, and combined with the visualization effects to realize the description of grape farmers' portraits. The results of models training indicated that the convergence speed and division accuracy of the optimized clustering algorithm were significantly improved. By comparing the clustering results on the typical UCI datasets, it was demonstrated that GASA-WFCM algorithm improved the accuracy, NMI and ARI by 3.0%, 7.2% and 6.1%, respectively. Moreover, the segmentation method was applied in grape farmers dataset, and the findings revealed that Chinese fresh grape farmers may be clustered into three groups, namely high-yield-driven grape farmers, high-input-driven grape farmers and high -income grape farmers, respectively. This paper provided a set of methods of users clustering analysis and portrait from a theoretical point of view. The findings and suggestions can help provide accurate and focused decision support for improving the effect of agricultural technology promotion and farmers' production performance. [ABSTRACT FROM AUTHOR]

The configuration optimization method of Hadoop system performance based on genetic simulated annealing algorithm is studied. In view of the performance of Hadoop on open source cloud computing platform, an optimization method is proposed. Based on the genetic simulated annealing algorithm, each configuration scheme is used as a chromosome for multiple selection, crossover and mutation. Combined with the principle of simulated annealing, the survival of the new chromosome and the number of iterations of the whole algorithm are controlled, and the optimal scheme of the system configuration is found. The experimental results show that the method can effectively improve the operation efficiency of the operation. In addition, the overall effect of the group is very good at the end of the iteration. When the job types in the system are similar, according to the characteristics that the whole simulated annealing algorithm is approaching the optimal solution, a real-time optimization configuration model is proposed on the basic of genetic simulated annealing algorithm. [ABSTRACT FROM AUTHOR]

The facility configuration and layout design of hub stations play an essential role in building a practical and cost-efficient hub station system. In general practice, bi-level programming models are implemented based on specific objectives and constraints. In this study, an upper-level model is used to solve the facility configuration problem, which is constructed based on the predetermined facility types to calculate the quantity of each facility by three objectives, including the number of passengers queueing in line, passengers' average waiting time in the queueing system and the cost of facilities. A genetic simulated annealing (GSA) algorithm based on the ɛ-constraints method is presented to optimize the upper-level model. A lower-level model is used to solve the facility layout problem. An optimization solver, named CPLEX, is applied in the lower-level model to calculate the abscissas and ordinates of the facility location after determining the facility configuration in the upper-level model. A case study of the Lanzhou West station in Northern China is demonstrated to verify the efficiency of the optimal solution from the bi-level model by using a commercial pedestrian simulation software, MASSMOTION. This study reveals an innovative method for optimizing station facility configuration and layout by using the heuristic novel algorithm, a discrete event simulation technology and effective utilization of the Building Information Model (BIM) data. [ABSTRACT FROM AUTHOR]

Purpose: To improve the efficiency of information sharing among the innovation agents of customer collaborative product innovation and shorten the product design cycle, an improved genetic annealing algorithm of the time optimization was presented. Design/methodology/approach: Based on the analysis of the objective relationship between the design tasks, the paper takes job shop problems for machining model and proposes the improved genetic algorithm to solve the problems, which is based on the niche technology and thus a better product collaborative innovation design time schedule is got to improve the efficiency. Finally, through the collaborative innovation design of a certain type of mobile phone, the proposed model and method were verified to be correct and effective. Findings and Originality/value: An algorithm with obvious advantages in terms of searching capability and optimization efficiency of customer collaborative product innovation was proposed. According to the defects of the traditional genetic annealing algorithm, the niche genetic annealing algorithm was presented. Firstly, it avoided the effective gene deletions at the early search stage and guaranteed the diversity of solution; Secondly, adaptive double point crossover and swap mutation strategy were introduced to overcome the defects of long solving process and easily converging local minimum value due to the fixed crossover and mutation probability; Thirdly, elite reserved strategy was imported that optimal solution missing was avoided effectively and evolution speed was accelerated. Originality/value: Firstly, the improved genetic simulated annealing algorithm overcomes some defects such as effective gene easily lost in early search. It is helpful to shorten the calculation process and improve the accuracy of the convergence value. Moreover, it speeds up the evolution and ensures the reliability of the optimal solution. Meanwhile, it has obvious advantages in efficiency of information sharing among the innovation agents of customer collaborative product innovation. So, the product design cycle could be shortened.

The S-λ Curves have become an important research subject in computer aided geometric design (CAGD), which owes to its good geometric properties (such as affine invariance, symmetry, and locality). This paper presents a new method to approximate an S-λ curve of degree n by using an S-λ curve of degree n-1. We transform this degree reduction problem into the function optimization problem first, and then using a new genetic simulated annealing algorithm to determine the global optimal solution of the optimization problem. The method can be used to approximate S-λ curves with fixed or unconstrained endpoints. Examples are given to verify the effectiveness of the presented algorithm; and these numeric examples show that the algorithm is not only easy to implement, but also offers high precision, which makes it valuable in practical applications.

The original data signals have small amplitude with a long distance signal transmission from the underground to the ground. And thousands of meters of drilling mud channel in the drilling process will contain a lot of noise sources. Therefore, the original mud pulse signals are submerged by the complex noise environment. Detecting the original signal after receiving the signal on the ground is still a serious issue. In connection with mud pulse signal detection under complex background in measurement while drilling (MWD), nonlinear bistable stochastic resonance theory and the production condition was analyzed. On the basis of this, a method of using genetic simulated annealing algorithm to adaptive optimize the parameters of stochastic resonance was proposed to realize the detection of mud pulse signal in the background of complex noise. Finally, through the simulation experiment and the field verification, this method realizes the signal detection even under the low signal-to-noise ratio (SNR). And the proposed method can overcome the shortcomings of existing detection method of signal detection performance in low SNR. It proves the proposed method's effectiveness and feasibility. [ABSTRACT FROM AUTHOR]

Copyright of Electric Machines & Control / Dianji Yu Kongzhi Xuebao is the property of Electric Machines & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Waste household appliances contain hazardous substances and valuable components. The timely disposal of these waste products can avoid environmental pollution and improve the recycling rate of resources. Robotic disassembly can realize efficient and precise disassembly to achieve sustainable and clean production. Reasonable allocation of robots and disassembly tasks is the key to achieving efficient production. Reducing energy consumption and improving profits are the focus of the government and enterprises. The tool replacement of the robot needs to be considered in the actual disassembly process, which increases the difficulty of the problem. This study establishes a multi-objective robotic disassembly line balancing and sequencing model to quantify the problem. The model aims to shorten the disassembly completion time and perform disassembly operations with energy-saving and high-profit. An improved genetic simulated annealing algorithm based on problem characteristics is designed. The superiority of the proposed algorithm is verified by solving 21 benchmarks with the number of tasks ranging from 7 to 148 and comparing with five algorithms. In a disassembly case of waste household appliances represented by a microwave oven, 66 disassembly schemes are obtained for decision makers to choose from different emphases. The results show that these schemes can not only achieve efficient and green disassembling but also obtain higher disassembly profits. [ABSTRACT FROM AUTHOR]

The rapid growth of travel demand and the overuse of private cars give rise to not only the severe traffic jam in the urban area, but also the adverse air pollution due to lots of vehicles' emissions. This paper re-explores the traditional road pricing problem by involving the emission constraints or targets. The primary objective of this study is to investigate the impact of road pricing schemes on the route and mode choices of travelers for a multi-modal transportation network so as to achieve the congestion alleviation and emissions reduction simultaneously. The concerned problem is formulated into a mathematical programming with equilibrium constraints (MPEC), which is a bi-level model. In the lower level, a variational inequality model is constructed to model the combined mode-split and traffic-assignment problem considering the asymmetric interactions among travel modes. The upper level is a mathematical programming with various management objectives and constraints. The governor in the upper level would make the decisions on road tolls based on the responses from the lower road users. The genetic simulated annealing algorithm (GSAA) is proposed to solve the bi-level model. The effectiveness of the algorithm and the effect of road tolls on the system performance are illustrated in the numerical example. [ABSTRACT FROM AUTHOR]

• Rapid evaluation of ammonia nitrogen concentration in biogas slurry was performed. • CARS-GSA was superiority to CARS in wavelengths selection for NIR spectroscopy. • Detection accuracy of NIR spectroscopy regression model was improved by CARS-GSA. As an important intermediate product of the anaerobic digestion (AD) process, ammonia nitrogen (AN) is an essential indicator for analyzing the nitrogen nutrition level in the biogas system. To realize rapid evaluation of ammonia nitrogen concentration (ANC) in biogas slurry during AD, a detection model of ANC was established by combining near infrared (NIR) transmission spectroscopy with partial least squares. The CARS-GSA was constructed based on the competitive adaptive reweighted sampling (CARS) algorithm combined with the genetic simulated annealing algorithm (GSA), which was applied to characteristic wavelength selection of AN for improving the accuracy and efficiency of the spectral detection model. Through the selection of wavelengths by CARS-GSA, the modeling wavelengths decreased to 47 from 1557 of raw spectra, and the performance of the ANC regression model was effectively improved. For the CARS-GSA model, the determination coefficient, root mean squared error and residual predictive deviation of the validation set were 0.989, 12.549 and 9.662, respectively. For the independent test set, the above indexes were 0.990, 21.341 and 9.821, and consistent with those of the calibration set. The results showed that CARS-GSA could effectively reduce modeling wavelength variables and enhance prediction ability of the model. This study provides a theoretical support for the application of NIR transmission spectroscopy in online detection of ANC in biogas slurry. [ABSTRACT FROM AUTHOR]

The objective function of the vehicle routing problem with time windows (VRPTW) considered here is to achieve the minimization of total cost in a single distribution, which includes the travel costs and the penalty coefficient of time, under the constraints of time limits, volume and capacity. There are two main lines of development in relation to the exact algorithms for the VRPTW. One is concerned with the general decomposition approach and the solution to certain dual problems associated with the VRPTW. Another, more recent direction is concerned with the analysis of the polyhedral structure of the VRPTW. The algorithm to solve this model is a new stochastic approach called Genetic Simulated Annealing Algorithm (GSAA), which added both memory function and annealing operation to the algorithm. The result was compared to the former which operated by genetic algorithm, and showed that the approach to solve the VRPTW is very competitive. [ABSTRACT FROM AUTHOR]

Abstract: The heating balance of central heating network is an important part in the central heating design. This article makes theoretical analysis of the heating for the heating balance control. Through the analysis of central heating networks, the total control solution for heating balance is established. In this article the genetic algorithm is combined with the simulated annealing algorithm to find global optimal solutions, to achieve the balance of heating network and uniform heating. Simulation results indicate that the method has better effect. [Copyright &y& Elsevier]

Abstract: Support Vector Machine (SVM) is a novel machine learning method of soft sensor modeling in fermentation process, which has the ability to approximate nonlinear process with arbitrary accuracy. Learning results and generalization ability are key performance indicators of a soft sensor model. Parameters settings and input variable selection are crucial for SVM learning results and generalization ability. In this paper, input variable selection and parameter setting are regarded as a combinatorial optimization problem, and a combinatorial optimal objective function is constructed based on the Akaike Information Criterion (AIC). Genetic simulated annealing algorithm (GSAA) is used to search the an optimal model with the function extremum. Simulations show that the proposed soft sensor modeling method based on SVM has good performance in fermentation process. [Copyright &y& Elsevier]