Cloud computing has become an essential technology for the management and processing of large datasets,offering scalability,high availability,and fault tolerance.However,optimizing data replication across multiple dat...Cloud computing has become an essential technology for the management and processing of large datasets,offering scalability,high availability,and fault tolerance.However,optimizing data replication across multiple data centers poses a significant challenge,especially when balancing opposing goals such as latency,storage costs,energy consumption,and network efficiency.This study introduces a novel Dynamic Optimization Algorithm called Dynamic Multi-Objective Gannet Optimization(DMGO),designed to enhance data replication efficiency in cloud environments.Unlike traditional static replication systems,DMGO adapts dynamically to variations in network conditions,system demand,and resource availability.The approach utilizes multi-objective optimization approaches to efficiently balance data access latency,storage efficiency,and operational costs.DMGO consistently evaluates data center performance and adjusts replication algorithms in real time to guarantee optimal system efficiency.Experimental evaluations conducted in a simulated cloud environment demonstrate that DMGO significantly outperforms conventional static algorithms,achieving faster data access,lower storage overhead,reduced energy consumption,and improved scalability.The proposed methodology offers a robust and adaptable solution for modern cloud systems,ensuring efficient resource consumption while maintaining high performance.展开更多
Airborne pulse radar and communication systems are essential for precise detection and collision avoidance,ensuring that aircraft operate safely and efficiently.A major challenge in spectrum sharing is the allocation ...Airborne pulse radar and communication systems are essential for precise detection and collision avoidance,ensuring that aircraft operate safely and efficiently.A major challenge in spectrum sharing is the allocation of resources in both the time and frequency domains,aiming to minimize inter-system interference as the available spectrum fluctuates over time.In this paper,regarding maximization of detection probability and spectrum utilization efficiency as two fundamental objectives,a novel Dynamic Spectrum and Power Allocation based on Genetic Algorithm(GA-DSPA)model is proposed,which dynamically allocates communication channel frequency and power under the constraints of pulse radar detection probability and signal-to-interferenceplus-noise ratio of communication.To solve this bi-objective model,a non-dominated sortingbased multi-objective genetic algorithm is developed.A novel environment perception strategy and offspring sorting technique based on radar echoes are integrated into the optimization framework.Simulation results indicate that by integrating environmental monitoring mechanisms and dynamic adaptation strategies,the proposed method effectively tracks the evolving Paretooptimal Fronts(Po Fs),thereby ensuring optimal performance for both co-located pulse radar and communication systems.Hardware test results confirm that within the GA-DSPA framework,the pulse radar achieves higher detection probabilities under identical conditions,while the communication system realizes increased average throughput.展开更多
Structural Reliability-Based Topology Optimization(RBTO),as an efficient design methodology,serves as a crucial means to ensure the development ofmodern engineering structures towards high performance,long service lif...Structural Reliability-Based Topology Optimization(RBTO),as an efficient design methodology,serves as a crucial means to ensure the development ofmodern engineering structures towards high performance,long service life,and high reliability.However,in practical design processes,topology optimization must not only account for the static performance of structures but also consider the impacts of various responses and uncertainties under complex dynamic conditions,which traditional methods often struggle accommodate.Therefore,this study proposes an RBTO framework based on a Kriging-assisted level set function and a novel Dynamic Hybrid Particle Swarm Optimization(DHPSO)algorithm.By leveraging the Kriging model as a surrogate,the high cost associated with repeatedly running finite element analysis processes is reduced,addressing the issue of minimizing structural compliance.Meanwhile,the DHPSO algorithm enables a better balance between the population’s developmental and exploratory capabilities,significantly accelerating convergence speed and enhancing global convergence performance.Finally,the proposed method is validated through three different structural examples,demonstrating its superior performance.Observed that the computational that,compared to the traditional Solid Isotropic Material with Penalization(SIMP)method,the proposed approach reduces the upper bound of structural compliance by approximately 30%.Additionally,the optimized results exhibit clear material interfaces without grayscale elements,and the stress concentration factor is reduced by approximately 42%.Consequently,the computational results fromdifferent examples verify the effectiveness and superiority of this study across various fields,achieving the goal of providing more precise optimization results within a shorter timeframe.展开更多
The dynamic optimal interpolation(DOI)method is a technique based on quasi-geostrophic dynamics for merging multi-satellite altimeter along-track observations to generate gridded absolute dynamic topography(ADT).Compa...The dynamic optimal interpolation(DOI)method is a technique based on quasi-geostrophic dynamics for merging multi-satellite altimeter along-track observations to generate gridded absolute dynamic topography(ADT).Compared with the linear optimal interpolation(LOI)method,the DOI method can improve the accuracy of gridded ADT locally but with low computational efficiency.Consequently,considering both computational efficiency and accuracy,the DOI method is more suitable to be used only for regional applications.In this study,we propose to evaluate the suitable region for applying the DOI method based on the correlation between the absolute value of the Jacobian operator of the geostrophic stream function and the improvement achieved by the DOI method.After verifying the LOI and DOI methods,the suitable region was investigated in three typical areas:the Gulf Stream(25°N-50°N,55°W-80°W),the Japanese Kuroshio(25°N-45°N,135°E-155°E),and the South China Sea(5°N-25°N,100°E-125°E).We propose to use the DOI method only in regions outside the equatorial region and where the absolute value of the Jacobian operator of the geostrophic stream function is higher than1×10^(-11).展开更多
Considering the self-excited and forced vibrations in high-speed milling processes, a novel method for dynamic optimization of system stability is used to determine the cutting parameters and structural parameters by ...Considering the self-excited and forced vibrations in high-speed milling processes, a novel method for dynamic optimization of system stability is used to determine the cutting parameters and structural parameters by increasing the chatter free material removal rate （CF-MRR） and surface finish. The method is hased on the theory of the chatter stability and the semi-bandwidth of the resonant region. The objective function of the method is material removal rate（MRR）,the constraints are chatter stability and surface finish, and the optimizing variables are cutting and structural parameters. The optimization procedure is stated. The method is applied to a milling system and CF-MRR is increased 18.86%. It is shown that the influences of the chatter stability and the resonance are simultaneously considered in the dynamic optimization of the milling system for increasing CF-MRR and the surface finish.展开更多
Aiming at the problems in current cam profile optimization processes, such as simple dynamics models, limited geometric accuracy and low design automatization level, a new dynamic optimization mode is put forward. Bas...Aiming at the problems in current cam profile optimization processes, such as simple dynamics models, limited geometric accuracy and low design automatization level, a new dynamic optimization mode is put forward. Based on the parameterization modeling technique of MSC. ADAMS platform, the different steps in current mode are reorganized, thus obtaining an upgraded mode called the ＂parameterized-prototype-based cam profile dynamic optimization mode＂. A parameterized prototype（PP） of valve mechanism is constructed in the course of dynamic optimization for cam profiles. Practically, by utilizing PP and considering the flexibility of the parts in valve mechanism, geometric accuracy and design automatization are improved.展开更多
Considering the uncertainty of grid connection of electric vehicle charging stations and the uncertainty of new energy and residential electricity load,a spatio-temporal decoupling strategy of dynamic reactive power o...Considering the uncertainty of grid connection of electric vehicle charging stations and the uncertainty of new energy and residential electricity load,a spatio-temporal decoupling strategy of dynamic reactive power optimization based on clustering-local relaxation-correction is proposed.Firstly,the k-medoids clustering algorithm is used to divide the reduced power scene into periods.Then,the discrete variables and continuous variables are optimized in the same period of time.Finally,the number of input groups of parallel capacitor banks(CB)in multiple periods is fixed,and then the secondary static reactive power optimization correction is carried out by using the continuous reactive power output device based on the static reactive power compensation device(SVC),the new energy grid-connected inverter,and the electric vehicle charging station.According to the characteristics of the model,a hybrid optimization algorithm with a cross-feedback mechanism is used to solve different types of variables,and an improved artificial hummingbird algorithm based on tent chaotic mapping and adaptive mutation is proposed to improve the solution efficiency.The simulation results show that the proposed decoupling strategy can obtain satisfactory optimization resultswhile strictly guaranteeing the dynamic constraints of discrete variables,and the hybrid algorithm can effectively solve the mixed integer nonlinear optimization problem.展开更多
The unmanned aerial vehicle(UAV)-assisted mobile edge computing(MEC)has been deemed a promising solution for energy-constrained devices to run smart applications with computationintensive and latency-sensitive require...The unmanned aerial vehicle(UAV)-assisted mobile edge computing(MEC)has been deemed a promising solution for energy-constrained devices to run smart applications with computationintensive and latency-sensitive requirements,especially in some infrastructure-limited areas or some emergency scenarios.However,the multi-UAVassisted MEC network remains largely unexplored.In this paper,the dynamic trajectory optimization and computation offloading are studied in a multi-UAVassisted MEC system where multiple UAVs fly over a target area with different trajectories to serve ground users.By considering the dynamic channel condition and random task arrival and jointly optimizing UAVs'trajectories,user association,and subchannel assignment,the average long-term sum of the user energy consumption minimization problem is formulated.To address the problem involving both discrete and continuous variables,a hybrid decision deep reinforcement learning(DRL)-based intelligent energyefficient resource allocation and trajectory optimization algorithm is proposed,named HDRT algorithm,where deep Q network(DQN)and deep deterministic policy gradient(DDPG)are invoked to process discrete and continuous variables,respectively.Simulation results show that the proposed HDRT algorithm converges fast and outperforms other benchmarks in the aspect of user energy consumption and latency.展开更多
With the rapid advancement of robotics and Artificial Intelligence(AI),aerobics training companion robots now support eco-friendly fitness by reducing reliance on nonrenewable energy.This study presents a solar-powere...With the rapid advancement of robotics and Artificial Intelligence(AI),aerobics training companion robots now support eco-friendly fitness by reducing reliance on nonrenewable energy.This study presents a solar-powered aerobics training robot featuring an adaptive energy management system designed for sustainability and efficiency.The robot integrates machine vision with an enhanced Dynamic Cheetah Optimizer and Bayesian Neural Network(DynCO-BNN)to enable precise exercise monitoring and real-time feedback.Solar tracking technology ensures optimal energy absorption,while a microcontroller-based regulator manages power distribution and robotic movement.Dual-battery switching ensures uninterrupted operation,aided by light and I/V sensors for energy optimization.Using the INSIGHT-LME IMU dataset,which includes motion data from 76 individuals performing Local Muscular Endurance(LME)exercises,the system detects activities,counts repetitions,and recognizes human movements.To minimize energy use during data processing,Min-Max normalization and two-dimensional Discrete Fourier Transform(2D-DFT)are applied,boosting computational efficiency.The robot accurately identifies upper and lower limb movements,delivering effective exercise guidance.The DynCO-BNN model achieved a high tracking accuracy of 96.8%.Results confirm improved solar utilization,ecological sustainability,and reduced dependence on fossil fuels—positioning the robot as a smart,energy-efficient solution for next-generation fitness technology.展开更多
Dynamic optimization problems are a kind of optimization problems that involve changes over time. They pose a serious challenge to traditional optimization methods as well as conventional genetic algorithms since the ...Dynamic optimization problems are a kind of optimization problems that involve changes over time. They pose a serious challenge to traditional optimization methods as well as conventional genetic algorithms since the goal is no longer to search for the optimal solution（s） of a fixed problem but to track the moving optimum over time. Dynamic optimization problems have attracted a growing interest from the genetic algorithm community in recent years. Several approaches have been developed to enhance the performance of genetic algorithms in dynamic environments. One approach is to maintain the diversity of the population via random immigrants. This paper proposes a hybrid immigrants scheme that combines the concepts of elitism, dualism and random immigrants for genetic algorithms to address dynamic optimization problems. In this hybrid scheme, the best individual, i.e., the elite, from the previous generation and its dual individual are retrieved as the bases to create immigrants via traditional mutation scheme. These elitism-based and dualism-based immigrants together with some random immigrants are substituted into the current population, replacing the worst individuals in the population. These three kinds of immigrants aim to address environmental changes of slight, medium and significant degrees respectively and hence efficiently adapt genetic algorithms to dynamic environments that are subject to different severities of changes. Based on a series of systematically constructed dynamic test problems, experiments are carried out to investigate the performance of genetic algorithms with the hybrid immigrants scheme and traditional random immigrants scheme. Experimental results validate the efficiency of the proposed hybrid immigrants scheme for improving the performance of genetic algorithms in dynamic environments.展开更多
To solve dynamic optimization problem of chemical process (CPDOP), a hybrid differential evolution algorithm, which is integrated with Alopex and named as Alopex-DE, was proposed. In Alopex-DE, each original individua...To solve dynamic optimization problem of chemical process (CPDOP), a hybrid differential evolution algorithm, which is integrated with Alopex and named as Alopex-DE, was proposed. In Alopex-DE, each original individual has its own symbiotic individual, which consists of control parameters. Differential evolution operator is applied for the original individuals to search the global optimization solution. Alopex algorithm is used to co-evolve the symbiotic individuals during the original individual evolution and enhance the fitness of the original individuals. Thus, control parameters are self-adaptively adjusted by Alopex to obtain the real-time optimum values for the original population. To illustrate the whole performance of Alopex-DE, several varietal DEs were applied to optimize 13 benchmark functions. The results show that the whole performance of Alopex-DE is the best. Further, Alopex-DE was applied to solve 4 typical CPDOPs, and the effect of the discrete time degree on the optimization solution was analyzed. The satisfactory result is obtained.展开更多
The dynamic dexterity is an important issue for manipulator design, some indices were proposed for analyzing dynamic dexterity, but they can evaluate the dynamic performance just at one pose in the workspaee of the ma...The dynamic dexterity is an important issue for manipulator design, some indices were proposed for analyzing dynamic dexterity, but they can evaluate the dynamic performance just at one pose in the workspaee of the manipulator, and can＇t be applied to dynamic design expediently. Much work has been done in the kinematic optimization, but the work in the dynamic optimization is much less. A global dynamic condition number index is proposed and applied to the dynamic optimization design the parallel manipulator. This paper deals with the dynamic manipulability and dynamic optimization of a two degree-of-freedom （DOF） parallel manipulator. The particular velocity and particular angular velocity matrices of each moving part about the part＇s pivot point are derived fi＇om the kinematic formulation of the manipulator, and the inertial force and inertial movement are obtained utilizing Newton-Euler formulation, then the inverse dynamic model of the parallel manipulator is proposed based on the virtual work principle. The general inertial ellipsoid and dynamic manipulability ellipsoid are applied to evaluate the dynamic performance of the manipulator, a global dynamic condition number index based on the condition number of general inertial matrix in the workspace is proposed, and then the link lengths of the manipulator is redesigned to optimize the dynamic manipulability by this index. The dynamic manipulability of the origin mechanism and the optimized mechanism are compared, the result shows that the optimized one is much better. The global dynamic condition number index has good effect in evaluating the dynamic dexterity of the whole workspace, and is efficient in the dynamic optimal design of the parallel manipulator.展开更多
A design and optimization approach of dynamic and control performance for a two-DOF planar manipulator was proposed.After the kinematic and dynamic analysis,several advantages of the mechanism were illustrated,which m...A design and optimization approach of dynamic and control performance for a two-DOF planar manipulator was proposed.After the kinematic and dynamic analysis,several advantages of the mechanism were illustrated,which made it possible to obtain good dynamic and control performances just through mechanism optimization.Based on the idea of design for control(DFC),a novel kind of multi-objective optimization model was proposed.There were three optimization objectives:the index of inertia,the index describing the dynamic coupling effects and the global condition number.Other indexes to characterize the designing requirements such as the velocity of end-effector,the workspace size,and the first mode natural frequency were regarded as the constraints.The cross-section area and length of the linkages were chosen as the design variables.NSGA-II algorithm was introduced to solve this complex multi-objective optimization problem.Additional criteria from engineering experience were incorporated into the selecting of final parameters among the obtained Pareto solution sets.Finally,experiments were performed to validate the linear dynamic structure and control performances of the optimized mechanisms.A new expression for measuring the dynamic coupling degree with clear physical meaning was proposed.The results show that the optimized mechanism has an approximate decoupled dynamics structure,and each active joint can be regarded as a linear SISO system.The control performances of the linear and nonlinear controllers were also compared.It can be concluded that the optimized mechanism can achieve good control performance only using a linear controller.展开更多
The solutions of dynamic optimization problems are usually very difficult due to their highly nonlinear and multidimensional nature. 13enetic algorithm （GA） has been proved to be a teasibte method when the gradient ...The solutions of dynamic optimization problems are usually very difficult due to their highly nonlinear and multidimensional nature. 13enetic algorithm （GA） has been proved to be a teasibte method when the gradient is difficult to calculate. Its advantage is that the control profiles at all time stages are optimized simultaneously, but its convergence is very slow in the later period of evolution and it is easily trapped in the local optimum. In this study, a hybrid improved genetic algorithm （HIGA） for solving dynamic optimization problems is proposed to overcome these defects. Simplex method （SM） is used to perform the local search in the neighborhood of the optimal solution. By using SM, the ideal searching direction of global optimal solution could be found as soon as possible and the convergence speed of the algorithm is improved. The hybrid algorithm presents some improvements, such as protecting the best individual, accepting immigrations, as well as employing adaptive crossover and Ganssian mutation operators. The efficiency of the proposed algorithm is demonstrated by solving several dynamic optimization problems. At last, HIGA is applied to the optimal production of secreted protein in a fed batch reactor and the optimal feed-rate found by HIGA is effective and relatively stable.展开更多
Various nodes,logistics,capital flows,and information flows are required to make systematic decisions concerning the operation of an integrated coal supply system. We describe a quantitative analysis of such a system....Various nodes,logistics,capital flows,and information flows are required to make systematic decisions concerning the operation of an integrated coal supply system. We describe a quantitative analysis of such a system. A dynamic optimization model of the supply chain is developed. It has achieved optimal system profit under conditions guaranteeing a certain level of customer satisfaction. Applying this model to coal production of the Xuzhou coal mines allows recommendations for a more systematic use of washing and processing,transportation and sale resources for commercial coal production to be made. The results show that this model,which is scientific and effective,has an important value for making reasonable decisions related to complex coal enterprises.展开更多
Dynamic optimization of electromechanical coupling system is a significant engineering problem in the field of mechatronics. The performance improvement of electromechanical equipment depends on the system design para...Dynamic optimization of electromechanical coupling system is a significant engineering problem in the field of mechatronics. The performance improvement of electromechanical equipment depends on the system design parameters. Aiming at the spindle unit of refitted machine tool for solid rocket, the vibration acceleration of tool is taken as objective function, and the electromechanical system design parameters are appointed as design variables. Dynamic optimization model is set up by adopting Lagrange-Maxwell equations, Park transform and electromechanical system energy equations. In the procedure of seeking high efficient optimization method, exponential function is adopted to be the weight function of particle swarm optimization algorithm. Exponential inertia weight particle swarm algorithm（EPSA）, is formed and applied to solve the dynamic optimization problem of electromechanical system. The probability density function of EPSA is presented and used to perform convergence analysis. After calculation, the optimized design parameters of the spindle unit are obtained in limited time period. The vibration acceleration of the tool has been decreased greatly by the optimized design parameters. The research job in the paper reveals that the problem of dynamic optimization of electromechanical system can be solved by the method of combining system dynamic analysis with reformed swarm particle optimizati on. Such kind of method can be applied in the design of robots, NC machine, and other electromechanical equipments.展开更多
Work on dynamic topology optimization of engineering structures for vibration suppression has mainly addressed the maximization of eigenfrequencies and gaps between consecutive eigenfrequencies of free vibration, mini...Work on dynamic topology optimization of engineering structures for vibration suppression has mainly addressed the maximization of eigenfrequencies and gaps between consecutive eigenfrequencies of free vibration, minimization of the dynamic compliance subject to forced vibration, and minimization of the structural frequency response. A dynamic topology optimization method of bi-material plate structures is presented based on power flow analysis. Topology optimization problems formulated directly with the design objective of minimizing the power flow response are dealt with. In comparison to the displacement or velocity response, the power flow response takes not only the amplitude of force and velocity into account, but also the phase relationship of the two vector quantities. The complex expression of power flow response is derived based on time-harmonic external mechanical loading and Rayleigh damping. The mathematical formulation of topology optimization is established based on power flow response and bi-material solid isotropic material with penalization(SIMP) model. Computational optimization procedure is developed by using adjoint design sensitivity analysis and the method of moving asymptotes(MMA). Several numerical examples are presented for bi-material plate structures with different loading frequencies, which verify the feasibility and effectiveness of this method. Additionally, optimum results between topological design of minimum power flow response and minimum dynamic compliance are compared, showing that the present method has strong adaptability for structural dynamic topology optimization problems. The proposed research provides a more accurate and effective approach for dynamic topology optimization of vibrating structures.展开更多
This paper puts forward a design idea for blended wing body（BWB）.The idea is described as that cruise point,maximum lift to drag point and pitch trim point are in the same flight attitude.According to this design id...This paper puts forward a design idea for blended wing body（BWB）.The idea is described as that cruise point,maximum lift to drag point and pitch trim point are in the same flight attitude.According to this design idea,design objectives and constraints are defined.By applying low and high fidelity aerodynamic analysis tools,BWB aerodynamic design methodology is established by the combination of optimization design and inverse design methods.High lift to drag ratio,pitch trim and acceptable buffet margin can be achieved by this design methodology.For 300-passenger BWB configuration based on static stability design,as compared with initial configuration,the maximum lift to drag ratio and pitch trim are achieved at cruise condition,zero lift pitching moment is positive,and buffet characteristics is well.Fuel burn of 300-passenger BWB configuration is also significantly reduced as compared with conventional civil transports.Because aerodynamic design is carried out under the constraints of BWB design requirements,the design configuration fulfills the demands for interior layout and provides a solid foundation for continuous work.展开更多
A comparison of arithmetic operations of two dynamic process optimization approaches called quasi-sequential approach and reduced Sequential Quadratic Programming(rSQP)simultaneous approach with respect to equality co...A comparison of arithmetic operations of two dynamic process optimization approaches called quasi-sequential approach and reduced Sequential Quadratic Programming(rSQP)simultaneous approach with respect to equality constrained optimization problems is presented.Through the detail comparison of arithmetic operations,it is concluded that the average iteration number within differential algebraic equations(DAEs)integration of quasi-sequential approach could be regarded as a criterion.One formula is given to calculate the threshold value of average iteration number.If the average iteration number is less than the threshold value,quasi-sequential approach takes advantage of rSQP simultaneous approach which is more suitable contrarily.Two optimal control problems are given to demonstrate the usage of threshold value.For optimal control problems whose objective is to stay near desired operating point,the iteration number is usually small.Therefore,quasi-sequential approach seems more suitable for such problems.展开更多
In dynamic environments,it is important to track changing optimal solutions over time.Univariate marginal distribution algorithm（UMDA） which is a class algorithm of estimation of distribution algorithms attracts mor...In dynamic environments,it is important to track changing optimal solutions over time.Univariate marginal distribution algorithm（UMDA） which is a class algorithm of estimation of distribution algorithms attracts more and more attention in recent years.In this paper a new multi-population and diffusion UMDA（MDUMDA） is proposed for dynamic multimodal problems.The multi-population approach is used to locate multiple local optima which are useful to find the global optimal solution quickly to dynamic multimodal problems.The diffusion model is used to increase the diversity in a guided fashion,which makes the neighbor individuals of previous optimal solutions move gradually from the previous optimal solutions and enlarge the search space.This approach uses both the information of current population and the part history information of the optimal solutions.Finally experimental studies on the moving peaks benchmark are carried out to evaluate the proposed algorithm and compare the performance of MDUMDA and multi-population quantum swarm optimization（MQSO） from the literature.The experimental results show that the MDUMDA is effective for the function with moving optimum and can adapt to the dynamic environments rapidly.展开更多
文摘Cloud computing has become an essential technology for the management and processing of large datasets,offering scalability,high availability,and fault tolerance.However,optimizing data replication across multiple data centers poses a significant challenge,especially when balancing opposing goals such as latency,storage costs,energy consumption,and network efficiency.This study introduces a novel Dynamic Optimization Algorithm called Dynamic Multi-Objective Gannet Optimization(DMGO),designed to enhance data replication efficiency in cloud environments.Unlike traditional static replication systems,DMGO adapts dynamically to variations in network conditions,system demand,and resource availability.The approach utilizes multi-objective optimization approaches to efficiently balance data access latency,storage efficiency,and operational costs.DMGO consistently evaluates data center performance and adjusts replication algorithms in real time to guarantee optimal system efficiency.Experimental evaluations conducted in a simulated cloud environment demonstrate that DMGO significantly outperforms conventional static algorithms,achieving faster data access,lower storage overhead,reduced energy consumption,and improved scalability.The proposed methodology offers a robust and adaptable solution for modern cloud systems,ensuring efficient resource consumption while maintaining high performance.
基金co-supported by the National Natural Science Foundation of China(No.62293495)the National Key Research and Development Program of China(No.2023YFB3306900)the Academic Excellence Foundation of BUAA for ph.D Students,China。
文摘Airborne pulse radar and communication systems are essential for precise detection and collision avoidance,ensuring that aircraft operate safely and efficiently.A major challenge in spectrum sharing is the allocation of resources in both the time and frequency domains,aiming to minimize inter-system interference as the available spectrum fluctuates over time.In this paper,regarding maximization of detection probability and spectrum utilization efficiency as two fundamental objectives,a novel Dynamic Spectrum and Power Allocation based on Genetic Algorithm(GA-DSPA)model is proposed,which dynamically allocates communication channel frequency and power under the constraints of pulse radar detection probability and signal-to-interferenceplus-noise ratio of communication.To solve this bi-objective model,a non-dominated sortingbased multi-objective genetic algorithm is developed.A novel environment perception strategy and offspring sorting technique based on radar echoes are integrated into the optimization framework.Simulation results indicate that by integrating environmental monitoring mechanisms and dynamic adaptation strategies,the proposed method effectively tracks the evolving Paretooptimal Fronts(Po Fs),thereby ensuring optimal performance for both co-located pulse radar and communication systems.Hardware test results confirm that within the GA-DSPA framework,the pulse radar achieves higher detection probabilities under identical conditions,while the communication system realizes increased average throughput.
基金fundings supported by Sichuan Science and Technology Program(2025YFHZ0065).
文摘Structural Reliability-Based Topology Optimization(RBTO),as an efficient design methodology,serves as a crucial means to ensure the development ofmodern engineering structures towards high performance,long service life,and high reliability.However,in practical design processes,topology optimization must not only account for the static performance of structures but also consider the impacts of various responses and uncertainties under complex dynamic conditions,which traditional methods often struggle accommodate.Therefore,this study proposes an RBTO framework based on a Kriging-assisted level set function and a novel Dynamic Hybrid Particle Swarm Optimization(DHPSO)algorithm.By leveraging the Kriging model as a surrogate,the high cost associated with repeatedly running finite element analysis processes is reduced,addressing the issue of minimizing structural compliance.Meanwhile,the DHPSO algorithm enables a better balance between the population’s developmental and exploratory capabilities,significantly accelerating convergence speed and enhancing global convergence performance.Finally,the proposed method is validated through three different structural examples,demonstrating its superior performance.Observed that the computational that,compared to the traditional Solid Isotropic Material with Penalization(SIMP)method,the proposed approach reduces the upper bound of structural compliance by approximately 30%.Additionally,the optimized results exhibit clear material interfaces without grayscale elements,and the stress concentration factor is reduced by approximately 42%.Consequently,the computational results fromdifferent examples verify the effectiveness and superiority of this study across various fields,achieving the goal of providing more precise optimization results within a shorter timeframe.
基金supported by National Natural Science Foundation of China under Grants 42192531 and 42192534the Special Fund of Hubei Luojia Laboratory(China)under Grant 220100001the Natural Science Foundation of Hubei Province for Distinguished Young Scholars(China)under Grant 2022CFA090。
文摘The dynamic optimal interpolation(DOI)method is a technique based on quasi-geostrophic dynamics for merging multi-satellite altimeter along-track observations to generate gridded absolute dynamic topography(ADT).Compared with the linear optimal interpolation(LOI)method,the DOI method can improve the accuracy of gridded ADT locally but with low computational efficiency.Consequently,considering both computational efficiency and accuracy,the DOI method is more suitable to be used only for regional applications.In this study,we propose to evaluate the suitable region for applying the DOI method based on the correlation between the absolute value of the Jacobian operator of the geostrophic stream function and the improvement achieved by the DOI method.After verifying the LOI and DOI methods,the suitable region was investigated in three typical areas:the Gulf Stream(25°N-50°N,55°W-80°W),the Japanese Kuroshio(25°N-45°N,135°E-155°E),and the South China Sea(5°N-25°N,100°E-125°E).We propose to use the DOI method only in regions outside the equatorial region and where the absolute value of the Jacobian operator of the geostrophic stream function is higher than1×10^(-11).
基金Supported by the National Key Basic Research Program of China("973"Project)(2009CB724401)the China Postdoctoral Science Foundation(20070420208)the Postdoctoral Innovation Foundation of Shandong Province(200702023)~~
文摘Considering the self-excited and forced vibrations in high-speed milling processes, a novel method for dynamic optimization of system stability is used to determine the cutting parameters and structural parameters by increasing the chatter free material removal rate （CF-MRR） and surface finish. The method is hased on the theory of the chatter stability and the semi-bandwidth of the resonant region. The objective function of the method is material removal rate（MRR）,the constraints are chatter stability and surface finish, and the optimizing variables are cutting and structural parameters. The optimization procedure is stated. The method is applied to a milling system and CF-MRR is increased 18.86%. It is shown that the influences of the chatter stability and the resonance are simultaneously considered in the dynamic optimization of the milling system for increasing CF-MRR and the surface finish.
文摘Aiming at the problems in current cam profile optimization processes, such as simple dynamics models, limited geometric accuracy and low design automatization level, a new dynamic optimization mode is put forward. Based on the parameterization modeling technique of MSC. ADAMS platform, the different steps in current mode are reorganized, thus obtaining an upgraded mode called the ＂parameterized-prototype-based cam profile dynamic optimization mode＂. A parameterized prototype（PP） of valve mechanism is constructed in the course of dynamic optimization for cam profiles. Practically, by utilizing PP and considering the flexibility of the parts in valve mechanism, geometric accuracy and design automatization are improved.
基金funded by the“Research and Application Project of Collaborative Optimization Control Technology for Distribution Station Area for High Proportion Distributed PV Consumption(4000-202318079A-1-1-ZN)”of the Headquarters of the State Grid Corporation.
文摘Considering the uncertainty of grid connection of electric vehicle charging stations and the uncertainty of new energy and residential electricity load,a spatio-temporal decoupling strategy of dynamic reactive power optimization based on clustering-local relaxation-correction is proposed.Firstly,the k-medoids clustering algorithm is used to divide the reduced power scene into periods.Then,the discrete variables and continuous variables are optimized in the same period of time.Finally,the number of input groups of parallel capacitor banks(CB)in multiple periods is fixed,and then the secondary static reactive power optimization correction is carried out by using the continuous reactive power output device based on the static reactive power compensation device(SVC),the new energy grid-connected inverter,and the electric vehicle charging station.According to the characteristics of the model,a hybrid optimization algorithm with a cross-feedback mechanism is used to solve different types of variables,and an improved artificial hummingbird algorithm based on tent chaotic mapping and adaptive mutation is proposed to improve the solution efficiency.The simulation results show that the proposed decoupling strategy can obtain satisfactory optimization resultswhile strictly guaranteeing the dynamic constraints of discrete variables,and the hybrid algorithm can effectively solve the mixed integer nonlinear optimization problem.
基金supported by National Natural Science Foundation of China(No.62471254)National Natural Science Foundation of China(No.92367302)。
文摘The unmanned aerial vehicle(UAV)-assisted mobile edge computing(MEC)has been deemed a promising solution for energy-constrained devices to run smart applications with computationintensive and latency-sensitive requirements,especially in some infrastructure-limited areas or some emergency scenarios.However,the multi-UAVassisted MEC network remains largely unexplored.In this paper,the dynamic trajectory optimization and computation offloading are studied in a multi-UAVassisted MEC system where multiple UAVs fly over a target area with different trajectories to serve ground users.By considering the dynamic channel condition and random task arrival and jointly optimizing UAVs'trajectories,user association,and subchannel assignment,the average long-term sum of the user energy consumption minimization problem is formulated.To address the problem involving both discrete and continuous variables,a hybrid decision deep reinforcement learning(DRL)-based intelligent energyefficient resource allocation and trajectory optimization algorithm is proposed,named HDRT algorithm,where deep Q network(DQN)and deep deterministic policy gradient(DDPG)are invoked to process discrete and continuous variables,respectively.Simulation results show that the proposed HDRT algorithm converges fast and outperforms other benchmarks in the aspect of user energy consumption and latency.
文摘With the rapid advancement of robotics and Artificial Intelligence(AI),aerobics training companion robots now support eco-friendly fitness by reducing reliance on nonrenewable energy.This study presents a solar-powered aerobics training robot featuring an adaptive energy management system designed for sustainability and efficiency.The robot integrates machine vision with an enhanced Dynamic Cheetah Optimizer and Bayesian Neural Network(DynCO-BNN)to enable precise exercise monitoring and real-time feedback.Solar tracking technology ensures optimal energy absorption,while a microcontroller-based regulator manages power distribution and robotic movement.Dual-battery switching ensures uninterrupted operation,aided by light and I/V sensors for energy optimization.Using the INSIGHT-LME IMU dataset,which includes motion data from 76 individuals performing Local Muscular Endurance(LME)exercises,the system detects activities,counts repetitions,and recognizes human movements.To minimize energy use during data processing,Min-Max normalization and two-dimensional Discrete Fourier Transform(2D-DFT)are applied,boosting computational efficiency.The robot accurately identifies upper and lower limb movements,delivering effective exercise guidance.The DynCO-BNN model achieved a high tracking accuracy of 96.8%.Results confirm improved solar utilization,ecological sustainability,and reduced dependence on fossil fuels—positioning the robot as a smart,energy-efficient solution for next-generation fitness technology.
基金This work was supported by UK EPSRC(No.EP/E060722/01)Broil FAPESP(Proc.04/04289-6).
文摘Dynamic optimization problems are a kind of optimization problems that involve changes over time. They pose a serious challenge to traditional optimization methods as well as conventional genetic algorithms since the goal is no longer to search for the optimal solution（s） of a fixed problem but to track the moving optimum over time. Dynamic optimization problems have attracted a growing interest from the genetic algorithm community in recent years. Several approaches have been developed to enhance the performance of genetic algorithms in dynamic environments. One approach is to maintain the diversity of the population via random immigrants. This paper proposes a hybrid immigrants scheme that combines the concepts of elitism, dualism and random immigrants for genetic algorithms to address dynamic optimization problems. In this hybrid scheme, the best individual, i.e., the elite, from the previous generation and its dual individual are retrieved as the bases to create immigrants via traditional mutation scheme. These elitism-based and dualism-based immigrants together with some random immigrants are substituted into the current population, replacing the worst individuals in the population. These three kinds of immigrants aim to address environmental changes of slight, medium and significant degrees respectively and hence efficiently adapt genetic algorithms to dynamic environments that are subject to different severities of changes. Based on a series of systematically constructed dynamic test problems, experiments are carried out to investigate the performance of genetic algorithms with the hybrid immigrants scheme and traditional random immigrants scheme. Experimental results validate the efficiency of the proposed hybrid immigrants scheme for improving the performance of genetic algorithms in dynamic environments.
基金Project(2013CB733600) supported by the National Basic Research Program of ChinaProject(21176073) supported by the National Natural Science Foundation of China+2 种基金Project(20090074110005) supported by Doctoral Fund of Ministry of Education of ChinaProject(NCET-09-0346) supported by Program for New Century Excellent Talents in University of ChinaProject(09SG29) supported by "Shu Guang", China
文摘To solve dynamic optimization problem of chemical process (CPDOP), a hybrid differential evolution algorithm, which is integrated with Alopex and named as Alopex-DE, was proposed. In Alopex-DE, each original individual has its own symbiotic individual, which consists of control parameters. Differential evolution operator is applied for the original individuals to search the global optimization solution. Alopex algorithm is used to co-evolve the symbiotic individuals during the original individual evolution and enhance the fitness of the original individuals. Thus, control parameters are self-adaptively adjusted by Alopex to obtain the real-time optimum values for the original population. To illustrate the whole performance of Alopex-DE, several varietal DEs were applied to optimize 13 benchmark functions. The results show that the whole performance of Alopex-DE is the best. Further, Alopex-DE was applied to solve 4 typical CPDOPs, and the effect of the discrete time degree on the optimization solution was analyzed. The satisfactory result is obtained.
基金supported by National Natural Science Foundation of China (Grant No. 50605041, No. 50775125)National Basic Research Program of China (973 Program, Grant No. 2006CB705400)
文摘The dynamic dexterity is an important issue for manipulator design, some indices were proposed for analyzing dynamic dexterity, but they can evaluate the dynamic performance just at one pose in the workspaee of the manipulator, and can＇t be applied to dynamic design expediently. Much work has been done in the kinematic optimization, but the work in the dynamic optimization is much less. A global dynamic condition number index is proposed and applied to the dynamic optimization design the parallel manipulator. This paper deals with the dynamic manipulability and dynamic optimization of a two degree-of-freedom （DOF） parallel manipulator. The particular velocity and particular angular velocity matrices of each moving part about the part＇s pivot point are derived fi＇om the kinematic formulation of the manipulator, and the inertial force and inertial movement are obtained utilizing Newton-Euler formulation, then the inverse dynamic model of the parallel manipulator is proposed based on the virtual work principle. The general inertial ellipsoid and dynamic manipulability ellipsoid are applied to evaluate the dynamic performance of the manipulator, a global dynamic condition number index based on the condition number of general inertial matrix in the workspace is proposed, and then the link lengths of the manipulator is redesigned to optimize the dynamic manipulability by this index. The dynamic manipulability of the origin mechanism and the optimized mechanism are compared, the result shows that the optimized one is much better. The global dynamic condition number index has good effect in evaluating the dynamic dexterity of the whole workspace, and is efficient in the dynamic optimal design of the parallel manipulator.
基金Project(2009AA04Z216) supported in part by the National High Technology Research and Development Program of ChinaProject(2009ZX04013-011) supported by the National Science and Technology Major Program of ChinaProject(20092302120068) supported by the Doctoral Program of Higher Education of China
文摘A design and optimization approach of dynamic and control performance for a two-DOF planar manipulator was proposed.After the kinematic and dynamic analysis,several advantages of the mechanism were illustrated,which made it possible to obtain good dynamic and control performances just through mechanism optimization.Based on the idea of design for control(DFC),a novel kind of multi-objective optimization model was proposed.There were three optimization objectives:the index of inertia,the index describing the dynamic coupling effects and the global condition number.Other indexes to characterize the designing requirements such as the velocity of end-effector,the workspace size,and the first mode natural frequency were regarded as the constraints.The cross-section area and length of the linkages were chosen as the design variables.NSGA-II algorithm was introduced to solve this complex multi-objective optimization problem.Additional criteria from engineering experience were incorporated into the selecting of final parameters among the obtained Pareto solution sets.Finally,experiments were performed to validate the linear dynamic structure and control performances of the optimized mechanisms.A new expression for measuring the dynamic coupling degree with clear physical meaning was proposed.The results show that the optimized mechanism has an approximate decoupled dynamics structure,and each active joint can be regarded as a linear SISO system.The control performances of the linear and nonlinear controllers were also compared.It can be concluded that the optimized mechanism can achieve good control performance only using a linear controller.
基金Supported by Major State Basic Research Development Program of China （2012CB720500）, National Natural Science Foundation of China （Key Program： Ul162202）, National Science Fund for Outstanding Young Scholars （61222303）, National Natural Science Foundation of China （21276078, 21206037） and the Fundamental Research Funds for the Central Universities.
文摘The solutions of dynamic optimization problems are usually very difficult due to their highly nonlinear and multidimensional nature. 13enetic algorithm （GA） has been proved to be a teasibte method when the gradient is difficult to calculate. Its advantage is that the control profiles at all time stages are optimized simultaneously, but its convergence is very slow in the later period of evolution and it is easily trapped in the local optimum. In this study, a hybrid improved genetic algorithm （HIGA） for solving dynamic optimization problems is proposed to overcome these defects. Simplex method （SM） is used to perform the local search in the neighborhood of the optimal solution. By using SM, the ideal searching direction of global optimal solution could be found as soon as possible and the convergence speed of the algorithm is improved. The hybrid algorithm presents some improvements, such as protecting the best individual, accepting immigrations, as well as employing adaptive crossover and Ganssian mutation operators. The efficiency of the proposed algorithm is demonstrated by solving several dynamic optimization problems. At last, HIGA is applied to the optimal production of secreted protein in a fed batch reactor and the optimal feed-rate found by HIGA is effective and relatively stable.
文摘Various nodes,logistics,capital flows,and information flows are required to make systematic decisions concerning the operation of an integrated coal supply system. We describe a quantitative analysis of such a system. A dynamic optimization model of the supply chain is developed. It has achieved optimal system profit under conditions guaranteeing a certain level of customer satisfaction. Applying this model to coal production of the Xuzhou coal mines allows recommendations for a more systematic use of washing and processing,transportation and sale resources for commercial coal production to be made. The results show that this model,which is scientific and effective,has an important value for making reasonable decisions related to complex coal enterprises.
基金supported by National Natural Science Foundation of China (Grant No. 50675095)
文摘Dynamic optimization of electromechanical coupling system is a significant engineering problem in the field of mechatronics. The performance improvement of electromechanical equipment depends on the system design parameters. Aiming at the spindle unit of refitted machine tool for solid rocket, the vibration acceleration of tool is taken as objective function, and the electromechanical system design parameters are appointed as design variables. Dynamic optimization model is set up by adopting Lagrange-Maxwell equations, Park transform and electromechanical system energy equations. In the procedure of seeking high efficient optimization method, exponential function is adopted to be the weight function of particle swarm optimization algorithm. Exponential inertia weight particle swarm algorithm（EPSA）, is formed and applied to solve the dynamic optimization problem of electromechanical system. The probability density function of EPSA is presented and used to perform convergence analysis. After calculation, the optimized design parameters of the spindle unit are obtained in limited time period. The vibration acceleration of the tool has been decreased greatly by the optimized design parameters. The research job in the paper reveals that the problem of dynamic optimization of electromechanical system can be solved by the method of combining system dynamic analysis with reformed swarm particle optimizati on. Such kind of method can be applied in the design of robots, NC machine, and other electromechanical equipments.
基金supported by China Armament Pre-research Foundation(Grant No. 51318010402)UK Engineering and Physical Science Research Council (EPSRC), and China Scholarship Council (Grant No.2010611054)
文摘Work on dynamic topology optimization of engineering structures for vibration suppression has mainly addressed the maximization of eigenfrequencies and gaps between consecutive eigenfrequencies of free vibration, minimization of the dynamic compliance subject to forced vibration, and minimization of the structural frequency response. A dynamic topology optimization method of bi-material plate structures is presented based on power flow analysis. Topology optimization problems formulated directly with the design objective of minimizing the power flow response are dealt with. In comparison to the displacement or velocity response, the power flow response takes not only the amplitude of force and velocity into account, but also the phase relationship of the two vector quantities. The complex expression of power flow response is derived based on time-harmonic external mechanical loading and Rayleigh damping. The mathematical formulation of topology optimization is established based on power flow response and bi-material solid isotropic material with penalization(SIMP) model. Computational optimization procedure is developed by using adjoint design sensitivity analysis and the method of moving asymptotes(MMA). Several numerical examples are presented for bi-material plate structures with different loading frequencies, which verify the feasibility and effectiveness of this method. Additionally, optimum results between topological design of minimum power flow response and minimum dynamic compliance are compared, showing that the present method has strong adaptability for structural dynamic topology optimization problems. The proposed research provides a more accurate and effective approach for dynamic topology optimization of vibrating structures.
文摘This paper puts forward a design idea for blended wing body（BWB）.The idea is described as that cruise point,maximum lift to drag point and pitch trim point are in the same flight attitude.According to this design idea,design objectives and constraints are defined.By applying low and high fidelity aerodynamic analysis tools,BWB aerodynamic design methodology is established by the combination of optimization design and inverse design methods.High lift to drag ratio,pitch trim and acceptable buffet margin can be achieved by this design methodology.For 300-passenger BWB configuration based on static stability design,as compared with initial configuration,the maximum lift to drag ratio and pitch trim are achieved at cruise condition,zero lift pitching moment is positive,and buffet characteristics is well.Fuel burn of 300-passenger BWB configuration is also significantly reduced as compared with conventional civil transports.Because aerodynamic design is carried out under the constraints of BWB design requirements,the design configuration fulfills the demands for interior layout and provides a solid foundation for continuous work.
基金Supported by the National Natural Science Foundation of China（20676117） the National Creative Research Groups Science Foundation of China（60421002）
文摘A comparison of arithmetic operations of two dynamic process optimization approaches called quasi-sequential approach and reduced Sequential Quadratic Programming(rSQP)simultaneous approach with respect to equality constrained optimization problems is presented.Through the detail comparison of arithmetic operations,it is concluded that the average iteration number within differential algebraic equations(DAEs)integration of quasi-sequential approach could be regarded as a criterion.One formula is given to calculate the threshold value of average iteration number.If the average iteration number is less than the threshold value,quasi-sequential approach takes advantage of rSQP simultaneous approach which is more suitable contrarily.Two optimal control problems are given to demonstrate the usage of threshold value.For optimal control problems whose objective is to stay near desired operating point,the iteration number is usually small.Therefore,quasi-sequential approach seems more suitable for such problems.
基金supported by the National Natural Science Foundation of China (6087309960775013)
文摘In dynamic environments,it is important to track changing optimal solutions over time.Univariate marginal distribution algorithm（UMDA） which is a class algorithm of estimation of distribution algorithms attracts more and more attention in recent years.In this paper a new multi-population and diffusion UMDA（MDUMDA） is proposed for dynamic multimodal problems.The multi-population approach is used to locate multiple local optima which are useful to find the global optimal solution quickly to dynamic multimodal problems.The diffusion model is used to increase the diversity in a guided fashion,which makes the neighbor individuals of previous optimal solutions move gradually from the previous optimal solutions and enlarge the search space.This approach uses both the information of current population and the part history information of the optimal solutions.Finally experimental studies on the moving peaks benchmark are carried out to evaluate the proposed algorithm and compare the performance of MDUMDA and multi-population quantum swarm optimization（MQSO） from the literature.The experimental results show that the MDUMDA is effective for the function with moving optimum and can adapt to the dynamic environments rapidly.
