Due to the lack of accurate data and complex parameterization,the prediction of groundwater depth is a chal-lenge for numerical models.Machine learning can effectively solve this issue and has been proven useful in th...Due to the lack of accurate data and complex parameterization,the prediction of groundwater depth is a chal-lenge for numerical models.Machine learning can effectively solve this issue and has been proven useful in the prediction of groundwater depth in many areas.In this study,two new models are applied to the prediction of groundwater depth in the Ningxia area,China.The two models combine the improved dung beetle optimizer(DBO)algorithm with two deep learning models:The Multi-head Attention-Convolution Neural Network-Long Short Term Memory networks(MH-CNN-LSTM)and the Multi-head Attention-Convolution Neural Network-Gated Recurrent Unit(MH-CNN-GRU).The models with DBO show better prediction performance,with larger R(correlation coefficient),RPD(residual prediction deviation),and lower RMSE(root-mean-square error).Com-pared with the models with the original DBO,the R and RPD of models with the improved DBO increase by over 1.5%,and the RMSE decreases by over 1.8%,indicating better prediction results.In addition,compared with the multiple linear regression model,a traditional statistical model,deep learning models have better prediction performance.展开更多
Energy storage power plants are critical in balancing power supply and demand.However,the scheduling of these plants faces significant challenges,including high network transmission costs and inefficient inter-device ...Energy storage power plants are critical in balancing power supply and demand.However,the scheduling of these plants faces significant challenges,including high network transmission costs and inefficient inter-device energy utilization.To tackle these challenges,this study proposes an optimal scheduling model for energy storage power plants based on edge computing and the improved whale optimization algorithm(IWOA).The proposed model designs an edge computing framework,transferring a large share of data processing and storage tasks to the network edge.This architecture effectively reduces transmission costs by minimizing data travel time.In addition,the model considers demand response strategies and builds an objective function based on the minimization of the sum of electricity purchase cost and operation cost.The IWOA enhances the optimization process by utilizing adaptive weight adjustments and an optimal neighborhood perturbation strategy,preventing the algorithm from converging to suboptimal solutions.Experimental results demonstrate that the proposed scheduling model maximizes the flexibility of the energy storage plant,facilitating efficient charging and discharging.It successfully achieves peak shaving and valley filling for both electrical and heat loads,promoting the effective utilization of renewable energy sources.The edge-computing framework significantly reduces transmission delays between energy devices.Furthermore,IWOA outperforms traditional algorithms in optimizing the objective function.展开更多
Hydrocracking is one of the most important petroleum refining processes that converts heavy oils into gases,naphtha,diesel,and other products through cracking reactions.Multi-objective optimization algorithms can help...Hydrocracking is one of the most important petroleum refining processes that converts heavy oils into gases,naphtha,diesel,and other products through cracking reactions.Multi-objective optimization algorithms can help refining enterprises determine the optimal operating parameters to maximize product quality while ensuring product yield,or to increase product yield while reducing energy consumption.This paper presents a multi-objective optimization scheme for hydrocracking based on an improved SPEA2-PE algorithm,which combines path evolution operator and adaptive step strategy to accelerate the convergence speed and improve the computational accuracy of the algorithm.The reactor model used in this article is simulated based on a twenty-five lumped kinetic model.Through model and test function verification,the proposed optimization scheme exhibits significant advantages in the multiobjective optimization process of hydrocracking.展开更多
Deep learning algorithm is an effective data mining method and has been used in many fields to solve practical problems.However,the deep learning algorithms often contain some hyper-parameters which may be continuous,...Deep learning algorithm is an effective data mining method and has been used in many fields to solve practical problems.However,the deep learning algorithms often contain some hyper-parameters which may be continuous,integer,or mixed,and are often given based on experience but largely affect the effectiveness of activity recognition.In order to adapt to different hyper-parameter optimization problems,our improved Cuckoo Search(CS)algorithm is proposed to optimize the mixed hyper-parameters in deep learning algorithm.The algorithm optimizes the hyper-parameters in the deep learning model robustly,and intelligently selects the combination of integer type and continuous hyper-parameters that make the model optimal.Then,the mixed hyper-parameter in Convolutional Neural Network(CNN),Long-Short-Term Memory(LSTM)and CNN-LSTM are optimized based on the methodology on the smart home activity recognition datasets.Results show that the methodology can improve the performance of the deep learning model and whether we are experienced or not,we can get a better deep learning model using our method.展开更多
The research on optimization methods for constellation launch deployment strategies focused on the consideration of mission interval time constraints at the launch site.Firstly,a dynamic modeling of the constellation ...The research on optimization methods for constellation launch deployment strategies focused on the consideration of mission interval time constraints at the launch site.Firstly,a dynamic modeling of the constellation deployment process was established,and the relationship between the deployment window and the phase difference of the orbit insertion point,as well as the cost of phase adjustment after orbit insertion,was derived.Then,the combination of the constellation deployment position sequence was treated as a parameter,together with the sequence of satellite deployment intervals,as optimization variables,simplifying a highdimensional search problem within a wide range of dates to a finite-dimensional integer programming problem.An improved genetic algorithm with local search on deployment dates was introduced to optimize the launch deployment strategy.With the new description of the optimization variables,the total number of elements in the solution space was reduced by N orders of magnitude.Numerical simulation confirms that the proposed optimization method accelerates the convergence speed from hours to minutes.展开更多
In response to the deficiencies of commonly used optimization methods for assembly lines,a production demand-oriented optimization method for assembly lines is proposed.Taking a certain compressor assembly line as an ...In response to the deficiencies of commonly used optimization methods for assembly lines,a production demand-oriented optimization method for assembly lines is proposed.Taking a certain compressor assembly line as an example,the production rhythm and the number of workstations are calculated based on production requirements and working systems.With assembly rhythm and smoothing index as optimization goals,an improved particle swarm optimization algorithm is employed for process allocation.Subsequently,Flexsim simulation is used to analyze the assembly line.The final results show that after optimization using the improved particle swarm algorithm,the assembly line balance rate increased from 71.1%to 85.9%,and the assembly line smoothing index decreased from 47.4 to 29.8,significantly enhancing assembly efficiency.This demonstrates the effectiveness of the proposed optimization method for the assembly line and provides a reference for other products in the same industry.展开更多
To address the problem of high lifespan loss and poor state of charge(SOC)balance of electric vehicles(EVs)participating in grid peak shaving,an improved golden eagle optimizer(IGEO)algorithm for EV grouping control s...To address the problem of high lifespan loss and poor state of charge(SOC)balance of electric vehicles(EVs)participating in grid peak shaving,an improved golden eagle optimizer(IGEO)algorithm for EV grouping control strategy is proposed for peak shaving sce-narios.First,considering the difference between peak and valley loads and the operating costs of EVs,a peak shaving model for EVs is constructed.Second,the design of IGEO has improved the global exploration and local development capabilities of the golden eagle optimizer(GEO)algorithm.Subsequently,IGEO is used to solve the peak shaving model and obtain the overall EV grid connected charging and discharging instructions.Next,using the k-means algorithm,EVs are dynamically divided into priority charging groups,backup groups,and priority discharging groups based on SOC differences.Finally,a dual layer power distribution scheme for EVs is designed.The upper layer determines the charging and discharging sequences and instructions for the three groups of EVs,whereas the lower layer allocates the charging and discharging instructions for each group to each EV.The proposed strategy was simulated and verified,and the results showed that the designed IGEO had faster optimization speed and higher optimization accuracy.The pro-posed EV grouping control strategy effectively reduces the peak-valley difference in the power grid,reduces the operational life loss of EVs,and maintains a better SOC balance for EVs.展开更多
The application of multi-material topology optimization affords greater design flexibility compared to traditional single-material methods.However,density-based topology optimization methods encounter three unique cha...The application of multi-material topology optimization affords greater design flexibility compared to traditional single-material methods.However,density-based topology optimization methods encounter three unique challenges when inertial loads become dominant:non-monotonous behavior of the objective function,possible unconstrained characterization of the optimal solution,and parasitic effects.Herein,an improved Guide-Weight approach is introduced,which effectively addresses the structural topology optimization problem when subjected to inertial loads.Smooth and fast convergence of the compliance is achieved by the approach,while also maintaining the effectiveness of the volume constraints.The rational approximation of material properties model and smooth design are utilized to guarantee clear boundaries of the final structure,facilitating its seamless integration into manufacturing processes.The framework provided by the alternating active-phase algorithm is employed to decompose the multi-material topological problem under inertial loading into a set of sub-problems.The optimization of multi-material under inertial loads is accomplished through the effective resolution of these sub-problems using the improved Guide-Weight method.The effectiveness of the proposed approach is demonstrated through numerical examples involving two-phase and multi-phase materials.展开更多
Reliable Cluster Head(CH)selectionbased routing protocols are necessary for increasing the packet transmission efficiency with optimal path discovery that never introduces degradation over the transmission reliability...Reliable Cluster Head(CH)selectionbased routing protocols are necessary for increasing the packet transmission efficiency with optimal path discovery that never introduces degradation over the transmission reliability.In this paper,Hybrid Golden Jackal,and Improved Whale Optimization Algorithm(HGJIWOA)is proposed as an effective and optimal routing protocol that guarantees efficient routing of data packets in the established between the CHs and the movable sink.This HGJIWOA included the phases of Dynamic Lens-Imaging Learning Strategy and Novel Update Rules for determining the reliable route essential for data packets broadcasting attained through fitness measure estimation-based CH selection.The process of CH selection achieved using Golden Jackal Optimization Algorithm(GJOA)completely depends on the factors of maintainability,consistency,trust,delay,and energy.The adopted GJOA algorithm play a dominant role in determining the optimal path of routing depending on the parameter of reduced delay and minimal distance.It further utilized Improved Whale Optimisation Algorithm(IWOA)for forwarding the data from chosen CHs to the BS via optimized route depending on the parameters of energy and distance.It also included a reliable route maintenance process that aids in deciding the selected route through which data need to be transmitted or re-routed.The simulation outcomes of the proposed HGJIWOA mechanism with different sensor nodes confirmed an improved mean throughput of 18.21%,sustained residual energy of 19.64%with minimized end-to-end delay of 21.82%,better than the competitive CH selection approaches.展开更多
Transportation structures such as composite pavements and railway foundations typically consist of multi-layered media designed to withstand high bearing capacity.A theoretical understanding of load transfer mechanism...Transportation structures such as composite pavements and railway foundations typically consist of multi-layered media designed to withstand high bearing capacity.A theoretical understanding of load transfer mechanisms in these multi-layer composites is essential,as it offers intuitive insights into parametric influences and facilitates enhanced structural performance.This paper employs an improved transfer matrix method to address the limitations of existing theoretical approaches for analyzing multi-layer composite structures.By establishing a twodimensional composite pavement model,it investigates load transfer characteristics and validates the accuracy through finite element simulation.The proposed method offers a straightforward analytical approach for examining internal interactions between structural layers.Case studies indicate that the concrete surface layer is the main load-bearing layer for most vertical normal and shear stresses.The soil base layer reduces the overall mechanical response of the substructure,while horizontal actions increase the risk of interfacial slip and cracking.Structural optimization analysis demonstrates that increasing the thickness of the concrete surface layer,enhancing the thickness and stiffness of the soil base layer,or incorporating gradient layers can significantly mitigate these risks of interfacial slip and cracking.The findings of this study can guide the optimization design,parameter analysis,and damage prevention of multi-layer composite structures.展开更多
A decision-making problem of missile-target assignment with a novel particle swarm optimization algorithm is proposed when it comes to a multiple target collaborative combat situation.The threat function is establishe...A decision-making problem of missile-target assignment with a novel particle swarm optimization algorithm is proposed when it comes to a multiple target collaborative combat situation.The threat function is established to describe air combat situation.Optimization function is used to find an optimal missile-target assignment.An improved particle swarm optimization algorithm is utilized to figure out the optimization function with less parameters,which is based on the adaptive random learning approach.According to the coordinated attack tactics,there are some adjustments to the assignment.Simulation example results show that it is an effective algorithm to handle with the decision-making problem of the missile-target assignment(MTA)in air combat.展开更多
Considering that the vehicle routing problem （VRP） with many extended features is widely used in actual life, such as multi-depot, heterogeneous types of vehicles, customer service priority and time windows etc., a ...Considering that the vehicle routing problem （VRP） with many extended features is widely used in actual life, such as multi-depot, heterogeneous types of vehicles, customer service priority and time windows etc., a mathematical model for multi-depot heterogeneous vehicle routing problem with soft time windows （MDHVRPSTW） is established. An improved ant colony optimization （IACO） is proposed for solving this model. First, MDHVRPSTW is transferred into different groups according to the nearest principle, and then the initial route is constructed by the scanning algorithm （SA）. Secondly, genetic operators are introduced, and crossover probability and mutation probability are adaptively adjusted in order to improve the global search ability of the algorithm. Moreover, the smooth mechanism is used to improve the performance of the ant colony optimization （ACO）. Finally, the 3-opt strategy is used to improve the local search ability. The proposed IACO was tested on three new instances that were generated randomly. The experimental results show that IACO is superior to the other three existing algorithms in terms of convergence speed and solution quality. Thus, the proposed method is effective and feasible, and the proposed model is meaningful.展开更多
The improved element-free Galerkin （IEFG） method of elasticity is used to solve the topology optimization problems. In this method, the improved moving least-squares approximation is used to form the shape function....The improved element-free Galerkin （IEFG） method of elasticity is used to solve the topology optimization problems. In this method, the improved moving least-squares approximation is used to form the shape function. In a topology opti- mization process, the entire structure volume is considered as the constraint. From the solid isotropic microstructures with penalization, we select relative node density as a design variable. Then we choose the minimization of compliance to be an objective function, and compute its sensitivity with the adjoint method. The IEFG method in this paper can overcome the disadvantages of the singular matrices that sometimes appear in conventional element-free Galerkin （EFG） method. The central processing unit （CPU） time of each example is given to show that the IEFG method is more efficient than the EFG method under the same precision, and the advantage that the IEFG method does not form singular matrices is also shown.展开更多
The current Whale Optimization Algorithm(WOA)has several drawbacks,such as slow convergence,low solution accuracy and easy to fall into the local optimal solution.To overcome these drawbacks,an improved Whale Optimiza...The current Whale Optimization Algorithm(WOA)has several drawbacks,such as slow convergence,low solution accuracy and easy to fall into the local optimal solution.To overcome these drawbacks,an improved Whale Optimization Algorithm(IWOA)is proposed in this study.IWOA can enhance the global search capability by two measures.First,the crossover and mutation operations in Differential Evolutionary algorithm(DE)are combined with the whale optimization algorithm.Second,the cloud adaptive inertia weight is introduced in the position update phase of WOA to divide the population into two subgroups,so as to balance the global search ability and local development ability.ANSYS and Matlab are used to establish the structure model.To demonstrate the application of the IWOA,truss structural optimizations on 52-bar plane truss and 25-bar space truss were performed,and the results were are compared with that obtained by other optimization algorithm.It is verified that,compared with WOA,the IWOA has higher efficiency,fast convergence speed,better solution accuracy and stability.So IWOA can be used in the optimization design of large truss structures.展开更多
This paper introduced an integrated allocation model for distribution centers (DCs). The facility cost, inventory cost, transportation cost and service quality were considered in the model. An improved genetic algorit...This paper introduced an integrated allocation model for distribution centers (DCs). The facility cost, inventory cost, transportation cost and service quality were considered in the model. An improved genetic algorithm (IGA) was proposed to solve the problem. The improvement of IGA is based on the idea of adjusting crossover probability and mutation probability. The IGA is supplied by heuristic rules too. The simulation results show that the IGA is better than the standard GA(SGA) in search efficiency and equality.展开更多
A novel method of global optimal path planning for mobile robot was proposed based on the improved Dijkstra algorithm and ant system algorithm. This method includes three steps: the first step is adopting the MAKLINK ...A novel method of global optimal path planning for mobile robot was proposed based on the improved Dijkstra algorithm and ant system algorithm. This method includes three steps: the first step is adopting the MAKLINK graph theory to establish the free space model of the mobile robot, the second step is adopting the improved Dijkstra algorithm to find out a sub-optimal collision-free path, and the third step is using the ant system algorithm to adjust and optimize the location of the sub-optimal path so as to generate the global optimal path for the mobile robot. The computer simulation experiment was carried out and the results show that this method is correct and effective. The comparison of the results confirms that the proposed method is better than the hybrid genetic algorithm in the global optimal path planning.展开更多
When the maneuverability of a pursuer is not significantly higher than that of an evader,it will be difficult to intercept the evader with only one pursuer.Therefore,this article adopts a two-to-one differential game ...When the maneuverability of a pursuer is not significantly higher than that of an evader,it will be difficult to intercept the evader with only one pursuer.Therefore,this article adopts a two-to-one differential game strategy,the game of kind is generally considered to be angle-optimized,which allows unlimited turns,but these practices do not take into account the effect of acceleration,which does not correspond to the actual situation,thus,based on the angle-optimized,the acceleration optimization and the acceleration upper bound constraint are added into the game for consideration.A two-to-one differential game problem is proposed in the three-dimensional space,and an improved multi-objective grey wolf optimization(IMOGWO)algorithm is proposed to solve the optimal game point of this problem.With the equations that describe the relative motions between the pursuers and the evader in the three-dimensional space,a multi-objective function with constraints is given as the performance index to design an optimal strategy for the differential game.Then the optimal game point is solved by using the IMOGWO algorithm.It is proved based on Markov chains that with the IMOGWO,the Pareto solution set is the solution of the differential game.Finally,it is verified through simulations that the pursuers can capture the escapee,and via comparative experiments,it is shown that the IMOGWO algorithm performs well in terms of running time and memory usage.展开更多
This paper proposes a modified version of the Dwarf Mongoose Optimization Algorithm (IDMO) for constrained engineering design problems. This optimization technique modifies the base algorithm (DMO) in three simple but...This paper proposes a modified version of the Dwarf Mongoose Optimization Algorithm (IDMO) for constrained engineering design problems. This optimization technique modifies the base algorithm (DMO) in three simple but effective ways. First, the alpha selection in IDMO differs from the DMO, where evaluating the probability value of each fitness is just a computational overhead and contributes nothing to the quality of the alpha or other group members. The fittest dwarf mongoose is selected as the alpha, and a new operator ω is introduced, which controls the alpha movement, thereby enhancing the exploration ability and exploitability of the IDMO. Second, the scout group movements are modified by randomization to introduce diversity in the search process and explore unvisited areas. Finally, the babysitter's exchange criterium is modified such that once the criterium is met, the babysitters that are exchanged interact with the dwarf mongoose exchanging them to gain information about food sources and sleeping mounds, which could result in better-fitted mongooses instead of initializing them afresh as done in DMO, then the counter is reset to zero. The proposed IDMO was used to solve the classical and CEC 2020 benchmark functions and 12 continuous/discrete engineering optimization problems. The performance of the IDMO, using different performance metrics and statistical analysis, is compared with the DMO and eight other existing algorithms. In most cases, the results show that solutions achieved by the IDMO are better than those obtained by the existing algorithms.展开更多
In the non-linear microwave drying process, the incremental improved back-propagation （BP） neural network and response surface methodology （RSM） were used to build a predictive model of the combined effects of ind...In the non-linear microwave drying process, the incremental improved back-propagation （BP） neural network and response surface methodology （RSM） were used to build a predictive model of the combined effects of independent variables （the microwave power, the acting time and the rotational frequency） for microwave drying of selenium-rich slag. The optimum operating conditions obtained from the quadratic form of the RSM are： the microwave power of 14.97 kW, the acting time of 89.58 min, the rotational frequency of 10.94 Hz, and the temperature of 136.407 ℃. The relative dehydration rate of 97.1895% is obtained. Under the optimum operating conditions, the incremental improved BP neural network prediction model can predict the drying process results and different effects on the results of the independent variables. The verification experiments demonstrate the prediction accuracy of the network, and the mean squared error is 0.16. The optimized results indicate that RSM can optimize the experimental conditions within much more broad range by considering the combination of factors and the neural network model can predict the results effectively and provide the theoretical guidance for the follow-up production process.展开更多
基金supported by the National Natural Science Foundation of China [grant numbers 42088101 and 42375048]。
文摘Due to the lack of accurate data and complex parameterization,the prediction of groundwater depth is a chal-lenge for numerical models.Machine learning can effectively solve this issue and has been proven useful in the prediction of groundwater depth in many areas.In this study,two new models are applied to the prediction of groundwater depth in the Ningxia area,China.The two models combine the improved dung beetle optimizer(DBO)algorithm with two deep learning models:The Multi-head Attention-Convolution Neural Network-Long Short Term Memory networks(MH-CNN-LSTM)and the Multi-head Attention-Convolution Neural Network-Gated Recurrent Unit(MH-CNN-GRU).The models with DBO show better prediction performance,with larger R(correlation coefficient),RPD(residual prediction deviation),and lower RMSE(root-mean-square error).Com-pared with the models with the original DBO,the R and RPD of models with the improved DBO increase by over 1.5%,and the RMSE decreases by over 1.8%,indicating better prediction results.In addition,compared with the multiple linear regression model,a traditional statistical model,deep learning models have better prediction performance.
基金supported by the Changzhou Science and Technology Support Project(CE20235045)Open Subject of Jiangsu Province Key Laboratory of Power Transmission and Distribution(2021JSSPD12)+1 种基金Talent Projects of Jiangsu University of Technology(KYY20018)Postgraduate Research&Practice Innovation Program of Jiangsu Province(SJCX23_1633).
文摘Energy storage power plants are critical in balancing power supply and demand.However,the scheduling of these plants faces significant challenges,including high network transmission costs and inefficient inter-device energy utilization.To tackle these challenges,this study proposes an optimal scheduling model for energy storage power plants based on edge computing and the improved whale optimization algorithm(IWOA).The proposed model designs an edge computing framework,transferring a large share of data processing and storage tasks to the network edge.This architecture effectively reduces transmission costs by minimizing data travel time.In addition,the model considers demand response strategies and builds an objective function based on the minimization of the sum of electricity purchase cost and operation cost.The IWOA enhances the optimization process by utilizing adaptive weight adjustments and an optimal neighborhood perturbation strategy,preventing the algorithm from converging to suboptimal solutions.Experimental results demonstrate that the proposed scheduling model maximizes the flexibility of the energy storage plant,facilitating efficient charging and discharging.It successfully achieves peak shaving and valley filling for both electrical and heat loads,promoting the effective utilization of renewable energy sources.The edge-computing framework significantly reduces transmission delays between energy devices.Furthermore,IWOA outperforms traditional algorithms in optimizing the objective function.
基金supported by National Key Research and Development Program of China (2023YFB3307800)National Natural Science Foundation of China (Key Program: 62136003, 62373155)+1 种基金Major Science and Technology Project of Xinjiang (No. 2022A01006-4)the Fundamental Research Funds for the Central Universities。
文摘Hydrocracking is one of the most important petroleum refining processes that converts heavy oils into gases,naphtha,diesel,and other products through cracking reactions.Multi-objective optimization algorithms can help refining enterprises determine the optimal operating parameters to maximize product quality while ensuring product yield,or to increase product yield while reducing energy consumption.This paper presents a multi-objective optimization scheme for hydrocracking based on an improved SPEA2-PE algorithm,which combines path evolution operator and adaptive step strategy to accelerate the convergence speed and improve the computational accuracy of the algorithm.The reactor model used in this article is simulated based on a twenty-five lumped kinetic model.Through model and test function verification,the proposed optimization scheme exhibits significant advantages in the multiobjective optimization process of hydrocracking.
基金Supported by the Anhui Province Sports Health Information Monitoring Technology Engineering Research Center Open Project (KF2023012)。
文摘Deep learning algorithm is an effective data mining method and has been used in many fields to solve practical problems.However,the deep learning algorithms often contain some hyper-parameters which may be continuous,integer,or mixed,and are often given based on experience but largely affect the effectiveness of activity recognition.In order to adapt to different hyper-parameter optimization problems,our improved Cuckoo Search(CS)algorithm is proposed to optimize the mixed hyper-parameters in deep learning algorithm.The algorithm optimizes the hyper-parameters in the deep learning model robustly,and intelligently selects the combination of integer type and continuous hyper-parameters that make the model optimal.Then,the mixed hyper-parameter in Convolutional Neural Network(CNN),Long-Short-Term Memory(LSTM)and CNN-LSTM are optimized based on the methodology on the smart home activity recognition datasets.Results show that the methodology can improve the performance of the deep learning model and whether we are experienced or not,we can get a better deep learning model using our method.
文摘The research on optimization methods for constellation launch deployment strategies focused on the consideration of mission interval time constraints at the launch site.Firstly,a dynamic modeling of the constellation deployment process was established,and the relationship between the deployment window and the phase difference of the orbit insertion point,as well as the cost of phase adjustment after orbit insertion,was derived.Then,the combination of the constellation deployment position sequence was treated as a parameter,together with the sequence of satellite deployment intervals,as optimization variables,simplifying a highdimensional search problem within a wide range of dates to a finite-dimensional integer programming problem.An improved genetic algorithm with local search on deployment dates was introduced to optimize the launch deployment strategy.With the new description of the optimization variables,the total number of elements in the solution space was reduced by N orders of magnitude.Numerical simulation confirms that the proposed optimization method accelerates the convergence speed from hours to minutes.
文摘In response to the deficiencies of commonly used optimization methods for assembly lines,a production demand-oriented optimization method for assembly lines is proposed.Taking a certain compressor assembly line as an example,the production rhythm and the number of workstations are calculated based on production requirements and working systems.With assembly rhythm and smoothing index as optimization goals,an improved particle swarm optimization algorithm is employed for process allocation.Subsequently,Flexsim simulation is used to analyze the assembly line.The final results show that after optimization using the improved particle swarm algorithm,the assembly line balance rate increased from 71.1%to 85.9%,and the assembly line smoothing index decreased from 47.4 to 29.8,significantly enhancing assembly efficiency.This demonstrates the effectiveness of the proposed optimization method for the assembly line and provides a reference for other products in the same industry.
基金supported by the National Natural Science Foundation of China(52077078)China Southern Power Grid Company Limited 036000KK52220004(GDKJXM20220147).
文摘To address the problem of high lifespan loss and poor state of charge(SOC)balance of electric vehicles(EVs)participating in grid peak shaving,an improved golden eagle optimizer(IGEO)algorithm for EV grouping control strategy is proposed for peak shaving sce-narios.First,considering the difference between peak and valley loads and the operating costs of EVs,a peak shaving model for EVs is constructed.Second,the design of IGEO has improved the global exploration and local development capabilities of the golden eagle optimizer(GEO)algorithm.Subsequently,IGEO is used to solve the peak shaving model and obtain the overall EV grid connected charging and discharging instructions.Next,using the k-means algorithm,EVs are dynamically divided into priority charging groups,backup groups,and priority discharging groups based on SOC differences.Finally,a dual layer power distribution scheme for EVs is designed.The upper layer determines the charging and discharging sequences and instructions for the three groups of EVs,whereas the lower layer allocates the charging and discharging instructions for each group to each EV.The proposed strategy was simulated and verified,and the results showed that the designed IGEO had faster optimization speed and higher optimization accuracy.The pro-posed EV grouping control strategy effectively reduces the peak-valley difference in the power grid,reduces the operational life loss of EVs,and maintains a better SOC balance for EVs.
基金supported by the National Natural Science Foundation of China(Grant No.52172356)the Hunan Provincial Natural Science Foundation of China(Grant No.2022JJ10012).
文摘The application of multi-material topology optimization affords greater design flexibility compared to traditional single-material methods.However,density-based topology optimization methods encounter three unique challenges when inertial loads become dominant:non-monotonous behavior of the objective function,possible unconstrained characterization of the optimal solution,and parasitic effects.Herein,an improved Guide-Weight approach is introduced,which effectively addresses the structural topology optimization problem when subjected to inertial loads.Smooth and fast convergence of the compliance is achieved by the approach,while also maintaining the effectiveness of the volume constraints.The rational approximation of material properties model and smooth design are utilized to guarantee clear boundaries of the final structure,facilitating its seamless integration into manufacturing processes.The framework provided by the alternating active-phase algorithm is employed to decompose the multi-material topological problem under inertial loading into a set of sub-problems.The optimization of multi-material under inertial loads is accomplished through the effective resolution of these sub-problems using the improved Guide-Weight method.The effectiveness of the proposed approach is demonstrated through numerical examples involving two-phase and multi-phase materials.
文摘Reliable Cluster Head(CH)selectionbased routing protocols are necessary for increasing the packet transmission efficiency with optimal path discovery that never introduces degradation over the transmission reliability.In this paper,Hybrid Golden Jackal,and Improved Whale Optimization Algorithm(HGJIWOA)is proposed as an effective and optimal routing protocol that guarantees efficient routing of data packets in the established between the CHs and the movable sink.This HGJIWOA included the phases of Dynamic Lens-Imaging Learning Strategy and Novel Update Rules for determining the reliable route essential for data packets broadcasting attained through fitness measure estimation-based CH selection.The process of CH selection achieved using Golden Jackal Optimization Algorithm(GJOA)completely depends on the factors of maintainability,consistency,trust,delay,and energy.The adopted GJOA algorithm play a dominant role in determining the optimal path of routing depending on the parameter of reduced delay and minimal distance.It further utilized Improved Whale Optimisation Algorithm(IWOA)for forwarding the data from chosen CHs to the BS via optimized route depending on the parameters of energy and distance.It also included a reliable route maintenance process that aids in deciding the selected route through which data need to be transmitted or re-routed.The simulation outcomes of the proposed HGJIWOA mechanism with different sensor nodes confirmed an improved mean throughput of 18.21%,sustained residual energy of 19.64%with minimized end-to-end delay of 21.82%,better than the competitive CH selection approaches.
基金supported by Fundamental Research Funds for the Central Universities(No.lzujbky-2024-05)Innovation Foundation of Provincial Education Department of Gansu(2024B-005)+2 种基金Scientific Department of Gansu(24CXGA083,24CXGA024,JK2024-28,JK2024-32 and 23CXJA0007)Industrial Support Plan Project of Provincial Education Department of Gansu(2025CYZC-003 and CYZC-2024-10)the Hunan Natural Science Foundation Science and Education Joint Fund Project(2022JJ60109).
文摘Transportation structures such as composite pavements and railway foundations typically consist of multi-layered media designed to withstand high bearing capacity.A theoretical understanding of load transfer mechanisms in these multi-layer composites is essential,as it offers intuitive insights into parametric influences and facilitates enhanced structural performance.This paper employs an improved transfer matrix method to address the limitations of existing theoretical approaches for analyzing multi-layer composite structures.By establishing a twodimensional composite pavement model,it investigates load transfer characteristics and validates the accuracy through finite element simulation.The proposed method offers a straightforward analytical approach for examining internal interactions between structural layers.Case studies indicate that the concrete surface layer is the main load-bearing layer for most vertical normal and shear stresses.The soil base layer reduces the overall mechanical response of the substructure,while horizontal actions increase the risk of interfacial slip and cracking.Structural optimization analysis demonstrates that increasing the thickness of the concrete surface layer,enhancing the thickness and stiffness of the soil base layer,or incorporating gradient layers can significantly mitigate these risks of interfacial slip and cracking.The findings of this study can guide the optimization design,parameter analysis,and damage prevention of multi-layer composite structures.
基金jointly granted by the Science and Technology on Avionics Integration Laboratory and the Aeronautical Science Foundation of China (No. 2016ZC15008)
文摘A decision-making problem of missile-target assignment with a novel particle swarm optimization algorithm is proposed when it comes to a multiple target collaborative combat situation.The threat function is established to describe air combat situation.Optimization function is used to find an optimal missile-target assignment.An improved particle swarm optimization algorithm is utilized to figure out the optimization function with less parameters,which is based on the adaptive random learning approach.According to the coordinated attack tactics,there are some adjustments to the assignment.Simulation example results show that it is an effective algorithm to handle with the decision-making problem of the missile-target assignment(MTA)in air combat.
基金The National Natural Science Foundation of China(No.61074147)the Natural Science Foundation of Guangdong Province(No.S2011010005059)+2 种基金the Foundation of Enterprise-University-Research Institute Cooperation from Guangdong Province and Ministry of Education of China(No.2012B091000171,2011B090400460)the Science and Technology Program of Guangdong Province(No.2012B050600028)the Science and Technology Program of Huadu District,Guangzhou(No.HD14ZD001)
文摘Considering that the vehicle routing problem （VRP） with many extended features is widely used in actual life, such as multi-depot, heterogeneous types of vehicles, customer service priority and time windows etc., a mathematical model for multi-depot heterogeneous vehicle routing problem with soft time windows （MDHVRPSTW） is established. An improved ant colony optimization （IACO） is proposed for solving this model. First, MDHVRPSTW is transferred into different groups according to the nearest principle, and then the initial route is constructed by the scanning algorithm （SA）. Secondly, genetic operators are introduced, and crossover probability and mutation probability are adaptively adjusted in order to improve the global search ability of the algorithm. Moreover, the smooth mechanism is used to improve the performance of the ant colony optimization （ACO）. Finally, the 3-opt strategy is used to improve the local search ability. The proposed IACO was tested on three new instances that were generated randomly. The experimental results show that IACO is superior to the other three existing algorithms in terms of convergence speed and solution quality. Thus, the proposed method is effective and feasible, and the proposed model is meaningful.
基金supported by the National Natural Science Foundation of China(Grant Nos.11571223 and U1433104)
文摘The improved element-free Galerkin （IEFG） method of elasticity is used to solve the topology optimization problems. In this method, the improved moving least-squares approximation is used to form the shape function. In a topology opti- mization process, the entire structure volume is considered as the constraint. From the solid isotropic microstructures with penalization, we select relative node density as a design variable. Then we choose the minimization of compliance to be an objective function, and compute its sensitivity with the adjoint method. The IEFG method in this paper can overcome the disadvantages of the singular matrices that sometimes appear in conventional element-free Galerkin （EFG） method. The central processing unit （CPU） time of each example is given to show that the IEFG method is more efficient than the EFG method under the same precision, and the advantage that the IEFG method does not form singular matrices is also shown.
基金This work was supported by the National Natural Science Foundation of China(Grant No.11872157 and 11532013)the graduate innovative research project of Heilongjiang University of Science and Technology(Grant No.YJSCX2020-214HKD).
文摘The current Whale Optimization Algorithm(WOA)has several drawbacks,such as slow convergence,low solution accuracy and easy to fall into the local optimal solution.To overcome these drawbacks,an improved Whale Optimization Algorithm(IWOA)is proposed in this study.IWOA can enhance the global search capability by two measures.First,the crossover and mutation operations in Differential Evolutionary algorithm(DE)are combined with the whale optimization algorithm.Second,the cloud adaptive inertia weight is introduced in the position update phase of WOA to divide the population into two subgroups,so as to balance the global search ability and local development ability.ANSYS and Matlab are used to establish the structure model.To demonstrate the application of the IWOA,truss structural optimizations on 52-bar plane truss and 25-bar space truss were performed,and the results were are compared with that obtained by other optimization algorithm.It is verified that,compared with WOA,the IWOA has higher efficiency,fast convergence speed,better solution accuracy and stability.So IWOA can be used in the optimization design of large truss structures.
文摘This paper introduced an integrated allocation model for distribution centers (DCs). The facility cost, inventory cost, transportation cost and service quality were considered in the model. An improved genetic algorithm (IGA) was proposed to solve the problem. The improvement of IGA is based on the idea of adjusting crossover probability and mutation probability. The IGA is supplied by heuristic rules too. The simulation results show that the IGA is better than the standard GA(SGA) in search efficiency and equality.
文摘A novel method of global optimal path planning for mobile robot was proposed based on the improved Dijkstra algorithm and ant system algorithm. This method includes three steps: the first step is adopting the MAKLINK graph theory to establish the free space model of the mobile robot, the second step is adopting the improved Dijkstra algorithm to find out a sub-optimal collision-free path, and the third step is using the ant system algorithm to adjust and optimize the location of the sub-optimal path so as to generate the global optimal path for the mobile robot. The computer simulation experiment was carried out and the results show that this method is correct and effective. The comparison of the results confirms that the proposed method is better than the hybrid genetic algorithm in the global optimal path planning.
基金National Natural Science Foundation of China(NSFC61773142,NSFC62303136)。
文摘When the maneuverability of a pursuer is not significantly higher than that of an evader,it will be difficult to intercept the evader with only one pursuer.Therefore,this article adopts a two-to-one differential game strategy,the game of kind is generally considered to be angle-optimized,which allows unlimited turns,but these practices do not take into account the effect of acceleration,which does not correspond to the actual situation,thus,based on the angle-optimized,the acceleration optimization and the acceleration upper bound constraint are added into the game for consideration.A two-to-one differential game problem is proposed in the three-dimensional space,and an improved multi-objective grey wolf optimization(IMOGWO)algorithm is proposed to solve the optimal game point of this problem.With the equations that describe the relative motions between the pursuers and the evader in the three-dimensional space,a multi-objective function with constraints is given as the performance index to design an optimal strategy for the differential game.Then the optimal game point is solved by using the IMOGWO algorithm.It is proved based on Markov chains that with the IMOGWO,the Pareto solution set is the solution of the differential game.Finally,it is verified through simulations that the pursuers can capture the escapee,and via comparative experiments,it is shown that the IMOGWO algorithm performs well in terms of running time and memory usage.
文摘This paper proposes a modified version of the Dwarf Mongoose Optimization Algorithm (IDMO) for constrained engineering design problems. This optimization technique modifies the base algorithm (DMO) in three simple but effective ways. First, the alpha selection in IDMO differs from the DMO, where evaluating the probability value of each fitness is just a computational overhead and contributes nothing to the quality of the alpha or other group members. The fittest dwarf mongoose is selected as the alpha, and a new operator ω is introduced, which controls the alpha movement, thereby enhancing the exploration ability and exploitability of the IDMO. Second, the scout group movements are modified by randomization to introduce diversity in the search process and explore unvisited areas. Finally, the babysitter's exchange criterium is modified such that once the criterium is met, the babysitters that are exchanged interact with the dwarf mongoose exchanging them to gain information about food sources and sleeping mounds, which could result in better-fitted mongooses instead of initializing them afresh as done in DMO, then the counter is reset to zero. The proposed IDMO was used to solve the classical and CEC 2020 benchmark functions and 12 continuous/discrete engineering optimization problems. The performance of the IDMO, using different performance metrics and statistical analysis, is compared with the DMO and eight other existing algorithms. In most cases, the results show that solutions achieved by the IDMO are better than those obtained by the existing algorithms.
基金Project(50734007) supported by the National Natural Science Foundation of China
文摘In the non-linear microwave drying process, the incremental improved back-propagation （BP） neural network and response surface methodology （RSM） were used to build a predictive model of the combined effects of independent variables （the microwave power, the acting time and the rotational frequency） for microwave drying of selenium-rich slag. The optimum operating conditions obtained from the quadratic form of the RSM are： the microwave power of 14.97 kW, the acting time of 89.58 min, the rotational frequency of 10.94 Hz, and the temperature of 136.407 ℃. The relative dehydration rate of 97.1895% is obtained. Under the optimum operating conditions, the incremental improved BP neural network prediction model can predict the drying process results and different effects on the results of the independent variables. The verification experiments demonstrate the prediction accuracy of the network, and the mean squared error is 0.16. The optimized results indicate that RSM can optimize the experimental conditions within much more broad range by considering the combination of factors and the neural network model can predict the results effectively and provide the theoretical guidance for the follow-up production process.
