This paper presents an improved hybrid algorithm and a multi-objective model to tackle the scheduling problem of multiple Automated Guided Vehicles(AGVs)under the composite operation mode.The multi-objective model aim...This paper presents an improved hybrid algorithm and a multi-objective model to tackle the scheduling problem of multiple Automated Guided Vehicles(AGVs)under the composite operation mode.The multi-objective model aims to minimize the maximum completion time,the total distance covered by AGVs,and the distance traveled while empty-loaded.The improved hybrid algorithm combines the improved genetic algorithm(GA)and the simulated annealing algorithm(SA)to strengthen the local search ability of the algorithm and improve the stability of the calculation results.Based on the characteristics of the composite operation mode,the authors introduce the combined coding and parallel decoding mode and calculate the fitness function with the grey entropy parallel analysis method to solve the multi-objective problem.The grey entropy parallel analysis method is a combination of the grey correlation analysis method and the entropy weighting method to solve multi-objective solving problems.A task advance evaluation strategy is proposed in the process of crossover and mutation operator to guide the direction of crossover and mutation.The computational experiments results show that the improved hybrid algorithm is better than the GA and the genetic algorithm with task advance evaluation strategy(AEGA)in terms of convergence speed and solution results,and the effectiveness of the multi-objective solution is proved.All three objectives are optimized and the proposed algorithm has an optimization of 7.6%respectively compared with the GA and 3.4%compared with the AEGA in terms of the objective of maximum completion time.展开更多
In this research,an integrated classification method based on principal component analysis-simulated annealing genetic algorithm-fuzzy cluster means(PCA-SAGA-FCM)was proposed for the unsupervised classification of tig...In this research,an integrated classification method based on principal component analysis-simulated annealing genetic algorithm-fuzzy cluster means(PCA-SAGA-FCM)was proposed for the unsupervised classification of tight sandstone reservoirs which lack the prior information and core experiments.A variety of evaluation parameters were selected,including lithology characteristic parameters,poro-permeability quality characteristic parameters,engineering quality characteristic parameters,and pore structure characteristic parameters.The PCA was used to reduce the dimension of the evaluation pa-rameters,and the low-dimensional data was used as input.The unsupervised reservoir classification of tight sandstone reservoir was carried out by the SAGA-FCM,the characteristics of reservoir at different categories were analyzed and compared with the lithological profiles.The analysis results of numerical simulation and actual logging data show that:1)compared with FCM algorithm,SAGA-FCM has stronger stability and higher accuracy;2)the proposed method can cluster the reservoir flexibly and effectively according to the degree of membership;3)the results of reservoir integrated classification match well with the lithologic profle,which demonstrates the reliability of the classification method.展开更多
Fleets of autonomous vehicles including shuttle buses,freight trucks,and road sweepers will be deployed in the Olympic Vil-lage during Beijing 2022 Winter Olympics.This requires intelligent charging infrastructure bas...Fleets of autonomous vehicles including shuttle buses,freight trucks,and road sweepers will be deployed in the Olympic Vil-lage during Beijing 2022 Winter Olympics.This requires intelligent charging infrastructure based on wireless power transfer technology to be equipped.To increase the misalignment tolerance of a high-power wireless charger,the robustness of the magnetic coupler should be optimized.This paper presents a new type of unipolar coupler,which is composed of three con-nected coils in series.The dimensional configuration of the coils is analyzed by the finite element method.The characteristic parameters of the coil are identified with their influence on the self-inductance and coupling coefficient.An expert model is built,whose feasibility can be verified in the aimed design domain.Combined with the expert model,an improved simulated annealing algorithm with a backtracking mechanism is proposed.The primary coil can reach the expected characteristics from any starting parameter combination through the proposed optimization algorithm.Under the same conditions in terms of external circuit parameters,ferrite usage,and aluminum shielding,the offset sensitivity of the magnetic coupler can be reduced from 58.79%to 18.89%.A prototype is established,validating the feasibility of the proposed coil structure with the optimized parameter algorithm.展开更多
Simulated annealing algorithm is a mathematic model,which imitates the physical process of annealing. And optical thin film is widely used in many industry.Its design is difficult and can be regarded as an optimizatio...Simulated annealing algorithm is a mathematic model,which imitates the physical process of annealing. And optical thin film is widely used in many industry.Its design is difficult and can be regarded as an optimization problem.In this paper,we use the simulated annealing algorithm to design an edge filter,which is composed of 20 dielectric thin film layers with TiO2 and SiO2.The simulated annealing algorithm is a very robust algorithm for optical thin film design.展开更多
With the current integration of distributed energy resources into the grid,the structure of distribution networks is becoming more complex.This complexity significantly expands the solution space in the optimization p...With the current integration of distributed energy resources into the grid,the structure of distribution networks is becoming more complex.This complexity significantly expands the solution space in the optimization process for network reconstruction using intelligent algorithms.Consequently,traditional intelligent algorithms frequently encounter insufficient search accuracy and become trapped in local optima.To tackle this issue,a more advanced particle swarm optimization algorithm is proposed.To address the varying emphases at different stages of the optimization process,a dynamic strategy is implemented to regulate the social and self-learning factors.The Metropolis criterion is introduced into the simulated annealing algorithm to occasionally accept suboptimal solutions,thereby mitigating premature convergence in the population optimization process.The inertia weight is adjusted using the logistic mapping technique to maintain a balance between the algorithm’s global and local search abilities.The incorporation of the Pareto principle involves the consideration of network losses and voltage deviations as objective functions.A fuzzy membership function is employed for selecting the results.Simulation analysis is carried out on the restructuring of the distribution network,using the IEEE-33 node system and the IEEE-69 node system as examples,in conjunction with the integration of distributed energy resources.The findings demonstrate that,in comparison to other intelligent optimization algorithms,the proposed enhanced algorithm demonstrates a shorter convergence time and effectively reduces active power losses within the network.Furthermore,it enhances the amplitude of node voltages,thereby improving the stability of distribution network operations and power supply quality.Additionally,the algorithm exhibits a high level of generality and applicability.展开更多
Multi-objective optimization of crashworthiness in automobile front-end structure was performed,and finite element model(FEM)was validated by experimental results to ensure that FEM can predict the response value with...Multi-objective optimization of crashworthiness in automobile front-end structure was performed,and finite element model(FEM)was validated by experimental results to ensure that FEM can predict the response value with sufficient accuracy.Seven design variables and four crashworthiness indicators were defined.Through orthogonal design method,18 FEMs were established,and the response values of crashworthiness indicators were extracted.By using the variable-response specimen matrix,Kriging surrogate model(KSM)was constructed to replace FEM to refect the function correlation between variables and responses.The accuracy of KSM was also validated.Finally,the simulated annealing optimization algorithm was implemented in KSM to seek optimal and reliable solutions.Based on the optimal results and comparison analysis,the 9096-th iteration point was the optimal solution.Although the intrusion of firewall and the mass of optimal structure increased slightly,the vehicle acceleration of the optimal solution decreased by 6.9%,which fectively reduced the risk of occupant injury.展开更多
Considering the effects of increased economic globalization and global warming,developing methods for reducing shipping costs and greenhouse gas emissions in ocean transportation has become crucial.Owing to its key ro...Considering the effects of increased economic globalization and global warming,developing methods for reducing shipping costs and greenhouse gas emissions in ocean transportation has become crucial.Owing to its key role in modern navigation technology,ship weather routing is the research focus of several scholars in this field.This study presents a hybrid genetic algorithm for the design of an optimal ship route for safe transoceanic navigation under complicated sea conditions.On the basis of the basic genetic algorithm,simulated annealing algorithm is introduced to enhance its local search ability and avoid premature convergence,with the ship’s voyage time and fuel consumption as optimization goals.Then,a mathematical model of ship weather routing is developed based on the grid system.A measure of fitness calibration is proposed,which can change the selection pressure of the algorithm as the population evolves.In addition,a hybrid crossover operator is proposed to enhance the ability to find the optimal solution and accelerate the convergence speed of the algorithm.Finally,a multi-population technique is applied to improve the robustness of the algorithm using different evolutionary strategies.展开更多
The premise and basis of load modeling are substation load composition inquiries and cluster analyses.However,the traditional kernel fuzzy C-means(KFCM)algorithm is limited by artificial clustering number selection an...The premise and basis of load modeling are substation load composition inquiries and cluster analyses.However,the traditional kernel fuzzy C-means(KFCM)algorithm is limited by artificial clustering number selection and its convergence to local optimal solutions.To overcome these limitations,an improved KFCM algorithm with adaptive optimal clustering number selection is proposed in this paper.This algorithm optimizes the KFCM algorithm by combining the powerful global search ability of genetic algorithm and the robust local search ability of simulated annealing algorithm.The improved KFCM algorithm adaptively determines the ideal number of clusters using the clustering evaluation index ratio.Compared with the traditional KFCM algorithm,the enhanced KFCM algorithm has robust clustering and comprehensive abilities,enabling the efficient convergence to the global optimal solution.展开更多
This paper introduces the quantum control of Lyapunov functions based on the state distance, the mean of imaginary quantities and state errors.In this paper, the specific control laws under the three forms are given.S...This paper introduces the quantum control of Lyapunov functions based on the state distance, the mean of imaginary quantities and state errors.In this paper, the specific control laws under the three forms are given.Stability is analyzed by the La Salle invariance principle and the numerical simulation is carried out in a 2D test system.The calculation process for the Lyapunov function is based on a combination of the average of virtual mechanical quantities, the particle swarm algorithm and a simulated annealing algorithm.Finally, a unified form of the control laws under the three forms is given.展开更多
Considering the uncertainty of the speed of horizontal transportation equipment,a cooperative scheduling model of multiple equipment resources in the automated container terminal was constructed to minimize the comple...Considering the uncertainty of the speed of horizontal transportation equipment,a cooperative scheduling model of multiple equipment resources in the automated container terminal was constructed to minimize the completion time,thus improving the loading and unloading efficiencies of automated container terminals.The proposed model integrated the two loading and unloading processes of“double-trolley quay crane+AGV+ARMG”and“single-trolley quay crane+container truck+ARMG”and then designed the simulated annealing particle swarm algorithm to solve the model.By comparing the results of the particle swarm algorithm and genetic algorithm,the algorithm designed in this paper could effectively improve the global and local space search capability of finding the optimal solution.Furthermore,the results showed that the proposed method of collaborative scheduling of multiple equipment resources in automated terminals considering hybrid processes effectively improved the loading and unloading efficiencies of automated container terminals.The findings of this study provide a reference for the improvement of loading and unloading processes as well as coordinated scheduling in automated terminals.展开更多
With the development of society and the exhaustion of fossil energy,researcher need to identify new alternative energy sources.Nuclear energy is a very good choice,but the key to the successful application of nuclear ...With the development of society and the exhaustion of fossil energy,researcher need to identify new alternative energy sources.Nuclear energy is a very good choice,but the key to the successful application of nuclear technology is determined primarily by the behavior of nuclear materials in reactors.Therefore,we studied the radiation performance of the fusion material reduced activation ferritic/martensitic(RAFM)steel.The main novelty of this paper are the statistical analysis of RAFM steel data sets through related statistical analysis and the formula derivation of the gradient descent method(GDM)which combines the gradient descent search strategy of the Convex Optimization Theory to get the best value.Use GDM algorithm to upgrade the annealing stabilization process of simulated annealing algorithm.The yield stress performance of RAFM steel is successfully predicted by the hybrid model which is combined by simulated annealing(SA)with support vector machine(SVM)as the first time.The effect on yield stress by the main physical quantities such as irradiation temperature,irradiation dose and test temperature is also analyzed.The related prediction process is:first,we used the improved annealing algorithm to optimize the SVR model after training the SVR model on a training data set.Next,we established the yield stress prediction model of RAFM steel.The model can predict up to 96%of the data points with the prediction in the test set and the original data point in the 2range.The statistical test analysis shows that under the condition of confidence level=0.01,the calculation results of the regression effect significance analysis pass the T-test.展开更多
A major challenge of network virtualization is the virtual network resource allocation problem that deals with efficient mapping of virtual nodes and virtual links onto the substrate network resources. However, the ex...A major challenge of network virtualization is the virtual network resource allocation problem that deals with efficient mapping of virtual nodes and virtual links onto the substrate network resources. However, the existing algorithms are almost concentrated on the randomly small-scale network topology, which is not suitable for practical large-scale network environments, because more time is spent on traversing SN and VN, resulting in VN requests congestion. To address this problem, virtual network mapping algorithm is proposed for large-scale network based on small-world characteristic of complex network and network coordinate system. Compared our algorithm with algorithm D-ViNE, experimental results show that our algorithm improves the overall performance.展开更多
Incoming materials sometimes must be slit according to the customers’ order requirements.A reasonable shear model can reduce residual materials and improve the rate of finished products.In this paper, a shear model i...Incoming materials sometimes must be slit according to the customers’ order requirements.A reasonable shear model can reduce residual materials and improve the rate of finished products.In this paper, a shear model is established, and several commonly used optimization algorithms are compared, with the minimum residual materials, running time, and CPU ratio as the evaluation index.Results show that the genetic algorithm is the best algorithm that can be used in online automatic shear control to achieve the highest yield and highest computational efficiency.展开更多
Identifying the factors affecting drinking water consumption is essential to the rational management of water resources and effective environment protection. In this study, the effects of the factors on rural drinking...Identifying the factors affecting drinking water consumption is essential to the rational management of water resources and effective environment protection. In this study, the effects of the factors on rural drinking water demand were studied using the adaptive neuro-fuzzy inference system (ANFIS) and hybrid models, such as the ANFIS-genetic algorithm (GA), ANFIS-particle swarm optimization (PSO), and support vector machine (SVM)-simulated annealing (SA). The rural areas of Hamadan Province in Iran were selected for the case study. Five drinking water consumption factors were selected for the assessment according to the literature, data availability, and the characteristics of the study area (such as precipitation, relative humidity, temperature, the number of subscribers, and water price). The results showed that the standard errors of ANFIS, ANFIS-GA, ANFIS-PSO, and SVM-SA were 0.669, 0.619, 0.705, and 0.578, respectively. Therefore, the hybrid model SVM-SA outperformed other models. The sensitivity analysis showed that of the parameters affecting drinking water consumption, the number of subscribers significantly affected the water consumption rate, while the average temperature was the least significant factor. Water price was a factor that could be easily controlled, but it was always one of the least effective parameters due to the low water fee.展开更多
Polycrystalline materials are extensively employed in industry.Its surface roughness significantly affects the working performance.Material defects,particularly grain boundaries,have a great impact on the achieved sur...Polycrystalline materials are extensively employed in industry.Its surface roughness significantly affects the working performance.Material defects,particularly grain boundaries,have a great impact on the achieved surface roughness of polycrystalline materials.However,it is difficult to establish a purely theoretical model for surface roughness with consideration of the grain boundary effect using conventional analytical methods.In this work,a theoretical and deep learning hybrid model for predicting the surface roughness of diamond-turned polycrystalline materials is proposed.The kinematic–dynamic roughness component in relation to the tool profile duplication effect,work material plastic side flow,relative vibration between the diamond tool and workpiece,etc,is theoretically calculated.The material-defect roughness component is modeled with a cascade forward neural network.In the neural network,the ratio of maximum undeformed chip thickness to cutting edge radius RT S,work material properties(misorientation angle θ_(g) and grain size d_(g)),and spindle rotation speed n s are configured as input variables.The material-defect roughness component is set as the output variable.To validate the developed model,polycrystalline copper with a gradient distribution of grains prepared by friction stir processing is machined with various processing parameters and different diamond tools.Compared with the previously developed model,obvious improvement in the prediction accuracy is observed with this hybrid prediction model.Based on this model,the influences of different factors on the surface roughness of polycrystalline materials are discussed.The influencing mechanism of the misorientation angle and grain size is quantitatively analyzed.Two fracture modes,including transcrystalline and intercrystalline fractures at different RTS values,are observed.Meanwhile,optimal processing parameters are obtained with a simulated annealing algorithm.Cutting experiments are performed with the optimal parameters,and a flat surface finish with Sa 1.314 nm is finally achieved.The developed model and corresponding new findings in this work are beneficial for accurately predicting the surface roughness of polycrystalline materials and understanding the impacting mechanism of material defects in diamond turning.展开更多
Optimal weights are usually obtained in neural network through a fixed network conformation,which affects the practicality of the network.Aiming at the shortage of conformation design and weight training algorithm in ...Optimal weights are usually obtained in neural network through a fixed network conformation,which affects the practicality of the network.Aiming at the shortage of conformation design and weight training algorithm in neural network application,the back propagation(BP)neural network learning algorithm combined with simulated annealing genetic algorithm(SAGA)is put forward.The multi-point genetic optimization of neural network topology and network weights is performed using hierarchical coding schemes and genetic operations.The simulated annealing mechanism is incorporated into the Genetic Algorithm(GA)to optimize the design and optimization of neural network conformation and network weights simultaneously.The SAGA takes advantage of GA excellent ability in grasping the overall ability of the search process,also uses the SA algorithm to control the convergence of the algorithm to avoid premature phenomenon.The fault diagnosis of one certain on-board electrical control box of helicopter and one certain flight control box of aircraft autopilot were used as a test platform to simulate the algorithm.The simulation conclusions reveal that the algorithm has good convergence rate and high diagnostic accurateness.展开更多
Based on the characteristics of nonlinearity,multi-case,and multi-disturbance,it is difficult to establish an accurate parameter mod-el on the hydraulic turbine system which is limited by the degree of fitting between...Based on the characteristics of nonlinearity,multi-case,and multi-disturbance,it is difficult to establish an accurate parameter mod-el on the hydraulic turbine system which is limited by the degree of fitting between parametric model and actual model,and the design of con-trol algorithm has a certain degree of limitation.Aiming at the modeling and control problems of hydraulic turbine system,this paper proposes hydraulic turbine system identification and predictive control based on genetic algorithm-simulate anneal and back propagation neural network(GASA-BPNN),and the output value predicted by GASA-BPNN model is fed back to the nonlinear optimizer to output the control quantity.The results show that the output speed of the traditional control system increases greatly and the speed of regulation is slow,while the speed of GASA-BPNN predictive control system increases little and the regulation speed is obviously faster than that of the traditional control system.Compared with the output response of the traditional control of the hydraulic turbine governing system,the neural network predictive control-ler used in this paper has better effect and stronger robustness,solves the problem of poor generalization ability and identification accuracy of the turbine system under variable conditions,and achieves better control effect.展开更多
In this paper,we set out to study the ensemble forecast for tropical cyclones.The case study is based on the Conditional Nonlinear Optimal Perturbation related to Parameter(CNOP-P)method and the WRF model to improve t...In this paper,we set out to study the ensemble forecast for tropical cyclones.The case study is based on the Conditional Nonlinear Optimal Perturbation related to Parameter(CNOP-P)method and the WRF model to improve the prediction accuracy for track and intensity,and two different typhoons are selected as cases for analysis.We first select perturbed parameters in the YSU and WSM6 schemes,and then solve CNOP-Ps with simulated annealing algorithm for single parameters as well as the combination of multiple parameters.Finally,perturbations are imposed on default parameter values to generate the ensemble members.The whole proposed procedures are referred to as the PerturbedParameter Ensemble(PPE).We also conduct two experiments,which are control forecast and ensemble forecast,termed Ctrl and perturbed-physics ensemble(PPhyE)respectively,to demonstrate the performance for contrast.In the article,we compare the effects of three experiments on tropical cyclones in aspects of track and intensity,respectively.For track,the prediction errors of PPE are smaller.The ensemble mean of PPE filters the unpredictable situation and retains the reasonably predictable components of the ensemble members.As for intensity,ensemble mean values of the central minimum sea-level pressure and the central maximum wind speed are closer to CMA data during most of the simulation time.The predicted values of the PPE ensemble members included the intensity of CMA data when the typhoon made landfall.The PPE also shows uncertainty in the forecast.Moreover,we also analyze the track and intensity from physical variable fields of PPE.Experiment results show PPE outperforms the other two benchmarks in track and intensity prediction.展开更多
The existing optimized performance prediction of carbon fiber protofilament process model is still unable to meet the production needs. A way of performance prediction on carbon fiber protofilament was presented based...The existing optimized performance prediction of carbon fiber protofilament process model is still unable to meet the production needs. A way of performance prediction on carbon fiber protofilament was presented based on support vector regression( SVR) which was optimized by an optimization algorithm combining simulated annealing algorithm and genetic algorithm( SAGA-SVR). To verify the accuracy of the model,the carbon fiber protofilament production test data were analyzed and compared with BP neural network( BPNN). The results show that SAGA-SVR can predict the performance parameters of the carbon fiber protofilament accurately.展开更多
To improve the efficiency of gate reassignment and optimize the plan of gate reassignment,the concept of disruption management is introduced,and a multi-objective programming model for airport gate reassignment is pro...To improve the efficiency of gate reassignment and optimize the plan of gate reassignment,the concept of disruption management is introduced,and a multi-objective programming model for airport gate reassignment is proposed.Considering the interests of passengers and the airport,the model minimizes the total flight delay,the total passengers′walking distance and the number of flights reassigned to other gates different from the planned ones.According to the characteristics of the gate reassignment,the model is simplified.As the multi-objective programming model is hard to reach the optimal solutions simultaneously,a threshold of satisfactory solutions of the model is set.Then a simulated annealing algorithm is designed for the model.Case studies show that the model decreases the total flight delay to the satisfactory solutions,and minimizes the total passengers′walking distance.The least change of planned assignment is also reached.The results achieve the goals of disruption management.Therefore,the model is verified to be effective.展开更多
基金the Shandong Province Key Research and Development Program under Grant No.2021SFGC0601.
文摘This paper presents an improved hybrid algorithm and a multi-objective model to tackle the scheduling problem of multiple Automated Guided Vehicles(AGVs)under the composite operation mode.The multi-objective model aims to minimize the maximum completion time,the total distance covered by AGVs,and the distance traveled while empty-loaded.The improved hybrid algorithm combines the improved genetic algorithm(GA)and the simulated annealing algorithm(SA)to strengthen the local search ability of the algorithm and improve the stability of the calculation results.Based on the characteristics of the composite operation mode,the authors introduce the combined coding and parallel decoding mode and calculate the fitness function with the grey entropy parallel analysis method to solve the multi-objective problem.The grey entropy parallel analysis method is a combination of the grey correlation analysis method and the entropy weighting method to solve multi-objective solving problems.A task advance evaluation strategy is proposed in the process of crossover and mutation operator to guide the direction of crossover and mutation.The computational experiments results show that the improved hybrid algorithm is better than the GA and the genetic algorithm with task advance evaluation strategy(AEGA)in terms of convergence speed and solution results,and the effectiveness of the multi-objective solution is proved.All three objectives are optimized and the proposed algorithm has an optimization of 7.6%respectively compared with the GA and 3.4%compared with the AEGA in terms of the objective of maximum completion time.
基金funded by the National Natural Science Foundation of China(42174131)the Strategic Cooperation Technology Projects of CNPC and CUPB(ZLZX2020-03).
文摘In this research,an integrated classification method based on principal component analysis-simulated annealing genetic algorithm-fuzzy cluster means(PCA-SAGA-FCM)was proposed for the unsupervised classification of tight sandstone reservoirs which lack the prior information and core experiments.A variety of evaluation parameters were selected,including lithology characteristic parameters,poro-permeability quality characteristic parameters,engineering quality characteristic parameters,and pore structure characteristic parameters.The PCA was used to reduce the dimension of the evaluation pa-rameters,and the low-dimensional data was used as input.The unsupervised reservoir classification of tight sandstone reservoir was carried out by the SAGA-FCM,the characteristics of reservoir at different categories were analyzed and compared with the lithological profiles.The analysis results of numerical simulation and actual logging data show that:1)compared with FCM algorithm,SAGA-FCM has stronger stability and higher accuracy;2)the proposed method can cluster the reservoir flexibly and effectively according to the degree of membership;3)the results of reservoir integrated classification match well with the lithologic profle,which demonstrates the reliability of the classification method.
基金This work was supported by the National Key Research and Development Program of China(No.2019YFE0104700)。
文摘Fleets of autonomous vehicles including shuttle buses,freight trucks,and road sweepers will be deployed in the Olympic Vil-lage during Beijing 2022 Winter Olympics.This requires intelligent charging infrastructure based on wireless power transfer technology to be equipped.To increase the misalignment tolerance of a high-power wireless charger,the robustness of the magnetic coupler should be optimized.This paper presents a new type of unipolar coupler,which is composed of three con-nected coils in series.The dimensional configuration of the coils is analyzed by the finite element method.The characteristic parameters of the coil are identified with their influence on the self-inductance and coupling coefficient.An expert model is built,whose feasibility can be verified in the aimed design domain.Combined with the expert model,an improved simulated annealing algorithm with a backtracking mechanism is proposed.The primary coil can reach the expected characteristics from any starting parameter combination through the proposed optimization algorithm.Under the same conditions in terms of external circuit parameters,ferrite usage,and aluminum shielding,the offset sensitivity of the magnetic coupler can be reduced from 58.79%to 18.89%.A prototype is established,validating the feasibility of the proposed coil structure with the optimized parameter algorithm.
文摘Simulated annealing algorithm is a mathematic model,which imitates the physical process of annealing. And optical thin film is widely used in many industry.Its design is difficult and can be regarded as an optimization problem.In this paper,we use the simulated annealing algorithm to design an edge filter,which is composed of 20 dielectric thin film layers with TiO2 and SiO2.The simulated annealing algorithm is a very robust algorithm for optical thin film design.
基金This research is supported by the Science and Technology Program of Gansu Province(No.23JRRA880).
文摘With the current integration of distributed energy resources into the grid,the structure of distribution networks is becoming more complex.This complexity significantly expands the solution space in the optimization process for network reconstruction using intelligent algorithms.Consequently,traditional intelligent algorithms frequently encounter insufficient search accuracy and become trapped in local optima.To tackle this issue,a more advanced particle swarm optimization algorithm is proposed.To address the varying emphases at different stages of the optimization process,a dynamic strategy is implemented to regulate the social and self-learning factors.The Metropolis criterion is introduced into the simulated annealing algorithm to occasionally accept suboptimal solutions,thereby mitigating premature convergence in the population optimization process.The inertia weight is adjusted using the logistic mapping technique to maintain a balance between the algorithm’s global and local search abilities.The incorporation of the Pareto principle involves the consideration of network losses and voltage deviations as objective functions.A fuzzy membership function is employed for selecting the results.Simulation analysis is carried out on the restructuring of the distribution network,using the IEEE-33 node system and the IEEE-69 node system as examples,in conjunction with the integration of distributed energy resources.The findings demonstrate that,in comparison to other intelligent optimization algorithms,the proposed enhanced algorithm demonstrates a shorter convergence time and effectively reduces active power losses within the network.Furthermore,it enhances the amplitude of node voltages,thereby improving the stability of distribution network operations and power supply quality.Additionally,the algorithm exhibits a high level of generality and applicability.
文摘Multi-objective optimization of crashworthiness in automobile front-end structure was performed,and finite element model(FEM)was validated by experimental results to ensure that FEM can predict the response value with sufficient accuracy.Seven design variables and four crashworthiness indicators were defined.Through orthogonal design method,18 FEMs were established,and the response values of crashworthiness indicators were extracted.By using the variable-response specimen matrix,Kriging surrogate model(KSM)was constructed to replace FEM to refect the function correlation between variables and responses.The accuracy of KSM was also validated.Finally,the simulated annealing optimization algorithm was implemented in KSM to seek optimal and reliable solutions.Based on the optimal results and comparison analysis,the 9096-th iteration point was the optimal solution.Although the intrusion of firewall and the mass of optimal structure increased slightly,the vehicle acceleration of the optimal solution decreased by 6.9%,which fectively reduced the risk of occupant injury.
基金funded by the Russian Foundation for Basic Research(RFBR)(No.20-07-00531).
文摘Considering the effects of increased economic globalization and global warming,developing methods for reducing shipping costs and greenhouse gas emissions in ocean transportation has become crucial.Owing to its key role in modern navigation technology,ship weather routing is the research focus of several scholars in this field.This study presents a hybrid genetic algorithm for the design of an optimal ship route for safe transoceanic navigation under complicated sea conditions.On the basis of the basic genetic algorithm,simulated annealing algorithm is introduced to enhance its local search ability and avoid premature convergence,with the ship’s voyage time and fuel consumption as optimization goals.Then,a mathematical model of ship weather routing is developed based on the grid system.A measure of fitness calibration is proposed,which can change the selection pressure of the algorithm as the population evolves.In addition,a hybrid crossover operator is proposed to enhance the ability to find the optimal solution and accelerate the convergence speed of the algorithm.Finally,a multi-population technique is applied to improve the robustness of the algorithm using different evolutionary strategies.
基金supported by the Planning Special Project of Guangdong Power Grid Co.,Ltd.:“Study on load modeling based on total measurement and discrimination method suitable for system characteristic analysis and calculation during the implementation of target grid in Guangdong power grid”(0319002022030203JF00023).
文摘The premise and basis of load modeling are substation load composition inquiries and cluster analyses.However,the traditional kernel fuzzy C-means(KFCM)algorithm is limited by artificial clustering number selection and its convergence to local optimal solutions.To overcome these limitations,an improved KFCM algorithm with adaptive optimal clustering number selection is proposed in this paper.This algorithm optimizes the KFCM algorithm by combining the powerful global search ability of genetic algorithm and the robust local search ability of simulated annealing algorithm.The improved KFCM algorithm adaptively determines the ideal number of clusters using the clustering evaluation index ratio.Compared with the traditional KFCM algorithm,the enhanced KFCM algorithm has robust clustering and comprehensive abilities,enabling the efficient convergence to the global optimal solution.
基金Project supported by the National Natural Science Foundation of China (Grant No.62176140)。
文摘This paper introduces the quantum control of Lyapunov functions based on the state distance, the mean of imaginary quantities and state errors.In this paper, the specific control laws under the three forms are given.Stability is analyzed by the La Salle invariance principle and the numerical simulation is carried out in a 2D test system.The calculation process for the Lyapunov function is based on a combination of the average of virtual mechanical quantities, the particle swarm algorithm and a simulated annealing algorithm.Finally, a unified form of the control laws under the three forms is given.
基金supported by the National Key R&D Program of China(Grant No.2017YFC0805309)Natural Science Foundation of Fujian Province(Grant No.2021J01820)Department of Education of Fujian Province Project(Grant Nos.JAT190294 and JAT210230).
文摘Considering the uncertainty of the speed of horizontal transportation equipment,a cooperative scheduling model of multiple equipment resources in the automated container terminal was constructed to minimize the completion time,thus improving the loading and unloading efficiencies of automated container terminals.The proposed model integrated the two loading and unloading processes of“double-trolley quay crane+AGV+ARMG”and“single-trolley quay crane+container truck+ARMG”and then designed the simulated annealing particle swarm algorithm to solve the model.By comparing the results of the particle swarm algorithm and genetic algorithm,the algorithm designed in this paper could effectively improve the global and local space search capability of finding the optimal solution.Furthermore,the results showed that the proposed method of collaborative scheduling of multiple equipment resources in automated terminals considering hybrid processes effectively improved the loading and unloading efficiencies of automated container terminals.The findings of this study provide a reference for the improvement of loading and unloading processes as well as coordinated scheduling in automated terminals.
基金The research is supported by“National Natural Science Foundation of China”under Grant No.61572526thanks to Mr.He from the material radiation effect team of the China Institute of Atomic Energy.With the help and guidance of Mr.He and Mr.Deng,the experiment was successfully conducted,and the results were greatly improved,which enhanced the structure of this article.Thanks to the editor for giving detailed comments,the quality of the article can be improved.
文摘With the development of society and the exhaustion of fossil energy,researcher need to identify new alternative energy sources.Nuclear energy is a very good choice,but the key to the successful application of nuclear technology is determined primarily by the behavior of nuclear materials in reactors.Therefore,we studied the radiation performance of the fusion material reduced activation ferritic/martensitic(RAFM)steel.The main novelty of this paper are the statistical analysis of RAFM steel data sets through related statistical analysis and the formula derivation of the gradient descent method(GDM)which combines the gradient descent search strategy of the Convex Optimization Theory to get the best value.Use GDM algorithm to upgrade the annealing stabilization process of simulated annealing algorithm.The yield stress performance of RAFM steel is successfully predicted by the hybrid model which is combined by simulated annealing(SA)with support vector machine(SVM)as the first time.The effect on yield stress by the main physical quantities such as irradiation temperature,irradiation dose and test temperature is also analyzed.The related prediction process is:first,we used the improved annealing algorithm to optimize the SVR model after training the SVR model on a training data set.Next,we established the yield stress prediction model of RAFM steel.The model can predict up to 96%of the data points with the prediction in the test set and the original data point in the 2range.The statistical test analysis shows that under the condition of confidence level=0.01,the calculation results of the regression effect significance analysis pass the T-test.
基金Sponsored by the Funds for Creative Research Groups of China(Grant No. 60821001)National Natural Science Foundation of China(Grant No.60973108 and 60902050)973 Project of China (Grant No.2007CB310703)
文摘A major challenge of network virtualization is the virtual network resource allocation problem that deals with efficient mapping of virtual nodes and virtual links onto the substrate network resources. However, the existing algorithms are almost concentrated on the randomly small-scale network topology, which is not suitable for practical large-scale network environments, because more time is spent on traversing SN and VN, resulting in VN requests congestion. To address this problem, virtual network mapping algorithm is proposed for large-scale network based on small-world characteristic of complex network and network coordinate system. Compared our algorithm with algorithm D-ViNE, experimental results show that our algorithm improves the overall performance.
文摘Incoming materials sometimes must be slit according to the customers’ order requirements.A reasonable shear model can reduce residual materials and improve the rate of finished products.In this paper, a shear model is established, and several commonly used optimization algorithms are compared, with the minimum residual materials, running time, and CPU ratio as the evaluation index.Results show that the genetic algorithm is the best algorithm that can be used in online automatic shear control to achieve the highest yield and highest computational efficiency.
文摘Identifying the factors affecting drinking water consumption is essential to the rational management of water resources and effective environment protection. In this study, the effects of the factors on rural drinking water demand were studied using the adaptive neuro-fuzzy inference system (ANFIS) and hybrid models, such as the ANFIS-genetic algorithm (GA), ANFIS-particle swarm optimization (PSO), and support vector machine (SVM)-simulated annealing (SA). The rural areas of Hamadan Province in Iran were selected for the case study. Five drinking water consumption factors were selected for the assessment according to the literature, data availability, and the characteristics of the study area (such as precipitation, relative humidity, temperature, the number of subscribers, and water price). The results showed that the standard errors of ANFIS, ANFIS-GA, ANFIS-PSO, and SVM-SA were 0.669, 0.619, 0.705, and 0.578, respectively. Therefore, the hybrid model SVM-SA outperformed other models. The sensitivity analysis showed that of the parameters affecting drinking water consumption, the number of subscribers significantly affected the water consumption rate, while the average temperature was the least significant factor. Water price was a factor that could be easily controlled, but it was always one of the least effective parameters due to the low water fee.
基金National Natural Science Foundation of China(Nos.52175430,51935008 and 52105478)China National Postdoctoral Program for Innovative Talents(BX20200234)Open Fund of Tianjin Key Laboratory of Equipment Design and Manufacturing Technology(EDMT)for the support of this work。
文摘Polycrystalline materials are extensively employed in industry.Its surface roughness significantly affects the working performance.Material defects,particularly grain boundaries,have a great impact on the achieved surface roughness of polycrystalline materials.However,it is difficult to establish a purely theoretical model for surface roughness with consideration of the grain boundary effect using conventional analytical methods.In this work,a theoretical and deep learning hybrid model for predicting the surface roughness of diamond-turned polycrystalline materials is proposed.The kinematic–dynamic roughness component in relation to the tool profile duplication effect,work material plastic side flow,relative vibration between the diamond tool and workpiece,etc,is theoretically calculated.The material-defect roughness component is modeled with a cascade forward neural network.In the neural network,the ratio of maximum undeformed chip thickness to cutting edge radius RT S,work material properties(misorientation angle θ_(g) and grain size d_(g)),and spindle rotation speed n s are configured as input variables.The material-defect roughness component is set as the output variable.To validate the developed model,polycrystalline copper with a gradient distribution of grains prepared by friction stir processing is machined with various processing parameters and different diamond tools.Compared with the previously developed model,obvious improvement in the prediction accuracy is observed with this hybrid prediction model.Based on this model,the influences of different factors on the surface roughness of polycrystalline materials are discussed.The influencing mechanism of the misorientation angle and grain size is quantitatively analyzed.Two fracture modes,including transcrystalline and intercrystalline fractures at different RTS values,are observed.Meanwhile,optimal processing parameters are obtained with a simulated annealing algorithm.Cutting experiments are performed with the optimal parameters,and a flat surface finish with Sa 1.314 nm is finally achieved.The developed model and corresponding new findings in this work are beneficial for accurately predicting the surface roughness of polycrystalline materials and understanding the impacting mechanism of material defects in diamond turning.
文摘Optimal weights are usually obtained in neural network through a fixed network conformation,which affects the practicality of the network.Aiming at the shortage of conformation design and weight training algorithm in neural network application,the back propagation(BP)neural network learning algorithm combined with simulated annealing genetic algorithm(SAGA)is put forward.The multi-point genetic optimization of neural network topology and network weights is performed using hierarchical coding schemes and genetic operations.The simulated annealing mechanism is incorporated into the Genetic Algorithm(GA)to optimize the design and optimization of neural network conformation and network weights simultaneously.The SAGA takes advantage of GA excellent ability in grasping the overall ability of the search process,also uses the SA algorithm to control the convergence of the algorithm to avoid premature phenomenon.The fault diagnosis of one certain on-board electrical control box of helicopter and one certain flight control box of aircraft autopilot were used as a test platform to simulate the algorithm.The simulation conclusions reveal that the algorithm has good convergence rate and high diagnostic accurateness.
基金This work was financially supported by the Fundamental Research Funds for the Central Universities,China(No.2020YJSJD15)the Ministry of industry and Information Technology of the China:Plateau hydro turbine construction project.
文摘Based on the characteristics of nonlinearity,multi-case,and multi-disturbance,it is difficult to establish an accurate parameter mod-el on the hydraulic turbine system which is limited by the degree of fitting between parametric model and actual model,and the design of con-trol algorithm has a certain degree of limitation.Aiming at the modeling and control problems of hydraulic turbine system,this paper proposes hydraulic turbine system identification and predictive control based on genetic algorithm-simulate anneal and back propagation neural network(GASA-BPNN),and the output value predicted by GASA-BPNN model is fed back to the nonlinear optimizer to output the control quantity.The results show that the output speed of the traditional control system increases greatly and the speed of regulation is slow,while the speed of GASA-BPNN predictive control system increases little and the regulation speed is obviously faster than that of the traditional control system.Compared with the output response of the traditional control of the hydraulic turbine governing system,the neural network predictive control-ler used in this paper has better effect and stronger robustness,solves the problem of poor generalization ability and identification accuracy of the turbine system under variable conditions,and achieves better control effect.
基金National Key Research and Development Program of China(2020YFA0608002)Key Project Fund of Shanghai 2020“Science and Technology Innovation Action Plan”for Social Development(20dz1200702)+2 种基金National Natural Science Foundation of China(42075141)Meteorological Joint Funds of the National Natural Science Foundation of China(U2142211)Fundamental Research Funds for the Central Universities(13502150039/003)。
文摘In this paper,we set out to study the ensemble forecast for tropical cyclones.The case study is based on the Conditional Nonlinear Optimal Perturbation related to Parameter(CNOP-P)method and the WRF model to improve the prediction accuracy for track and intensity,and two different typhoons are selected as cases for analysis.We first select perturbed parameters in the YSU and WSM6 schemes,and then solve CNOP-Ps with simulated annealing algorithm for single parameters as well as the combination of multiple parameters.Finally,perturbations are imposed on default parameter values to generate the ensemble members.The whole proposed procedures are referred to as the PerturbedParameter Ensemble(PPE).We also conduct two experiments,which are control forecast and ensemble forecast,termed Ctrl and perturbed-physics ensemble(PPhyE)respectively,to demonstrate the performance for contrast.In the article,we compare the effects of three experiments on tropical cyclones in aspects of track and intensity,respectively.For track,the prediction errors of PPE are smaller.The ensemble mean of PPE filters the unpredictable situation and retains the reasonably predictable components of the ensemble members.As for intensity,ensemble mean values of the central minimum sea-level pressure and the central maximum wind speed are closer to CMA data during most of the simulation time.The predicted values of the PPE ensemble members included the intensity of CMA data when the typhoon made landfall.The PPE also shows uncertainty in the forecast.Moreover,we also analyze the track and intensity from physical variable fields of PPE.Experiment results show PPE outperforms the other two benchmarks in track and intensity prediction.
基金the Key Project of National Natural Science Foundation of China(No.61134009)Program for Changjiang Scholars and Innovation Research Team in University from the Ministry of Education,China(No.IRT1220)+1 种基金Specialized Research Fund for Shanghai Leading Talents,Project of the Shanghai Committee of Science and Technology,China(No.13JC1407500)the Fundamental Research Funds for the Central Universities,China(No.2232012A3-04)
文摘The existing optimized performance prediction of carbon fiber protofilament process model is still unable to meet the production needs. A way of performance prediction on carbon fiber protofilament was presented based on support vector regression( SVR) which was optimized by an optimization algorithm combining simulated annealing algorithm and genetic algorithm( SAGA-SVR). To verify the accuracy of the model,the carbon fiber protofilament production test data were analyzed and compared with BP neural network( BPNN). The results show that SAGA-SVR can predict the performance parameters of the carbon fiber protofilament accurately.
基金Supported by the National Natural Science Foundation of China(71103034)the Natural Science Foundation of Jiangsu Province(bk2011084)
文摘To improve the efficiency of gate reassignment and optimize the plan of gate reassignment,the concept of disruption management is introduced,and a multi-objective programming model for airport gate reassignment is proposed.Considering the interests of passengers and the airport,the model minimizes the total flight delay,the total passengers′walking distance and the number of flights reassigned to other gates different from the planned ones.According to the characteristics of the gate reassignment,the model is simplified.As the multi-objective programming model is hard to reach the optimal solutions simultaneously,a threshold of satisfactory solutions of the model is set.Then a simulated annealing algorithm is designed for the model.Case studies show that the model decreases the total flight delay to the satisfactory solutions,and minimizes the total passengers′walking distance.The least change of planned assignment is also reached.The results achieve the goals of disruption management.Therefore,the model is verified to be effective.
