Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently...Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently,enhancing the robustness of scale-free networks has become a pressing issue.To address this problem,this paper proposes a Multi-Granularity Integration Algorithm(MGIA),which aims to improve the robustness of scale-free networks while keeping the initial degree of each node unchanged,ensuring network connectivity and avoiding the generation of multiple edges.The algorithm generates a multi-granularity structure from the initial network to be optimized,then uses different optimization strategies to optimize the networks at various granular layers in this structure,and finally realizes the information exchange between different granular layers,thereby further enhancing the optimization effect.We propose new network refresh,crossover,and mutation operators to ensure that the optimized network satisfies the given constraints.Meanwhile,we propose new network similarity and network dissimilarity evaluation metrics to improve the effectiveness of the optimization operators in the algorithm.In the experiments,the MGIA enhances the robustness of the scale-free network by 67.6%.This improvement is approximately 17.2%higher than the optimization effects achieved by eight currently existing complex network robustness optimization algorithms.展开更多
The advent of microgrids in modern energy systems heralds a promising era of resilience,sustainability,and efficiency.Within the realm of grid-tied microgrids,the selection of an optimal optimization algorithm is crit...The advent of microgrids in modern energy systems heralds a promising era of resilience,sustainability,and efficiency.Within the realm of grid-tied microgrids,the selection of an optimal optimization algorithm is critical for effective energy management,particularly in economic dispatching.This study compares the performance of Particle Swarm Optimization(PSO)and Genetic Algorithms(GA)in microgrid energy management systems,implemented using MATLAB tools.Through a comprehensive review of the literature and sim-ulations conducted in MATLAB,the study analyzes performance metrics,convergence speed,and the overall efficacy of GA and PSO,with a focus on economic dispatching tasks.Notably,a significant distinction emerges between the cost curves generated by the two algo-rithms for microgrid operation,with the PSO algorithm consistently resulting in lower costs due to its effective economic dispatching capabilities.Specifically,the utilization of the PSO approach could potentially lead to substantial savings on the power bill,amounting to approximately$15.30 in this evaluation.Thefindings provide insights into the strengths and limitations of each algorithm within the complex dynamics of grid-tied microgrids,thereby assisting stakeholders and researchers in arriving at informed decisions.This study contributes to the discourse on sustainable energy management by offering actionable guidance for the advancement of grid-tied micro-grid technologies through MATLAB-implemented optimization algorithms.展开更多
The objective of this study is to develop an advanced approach to variogram modelling by integrating genetic algorithms(GA)with machine learning-based linear regression,aiming to improve the accuracy and efficiency of...The objective of this study is to develop an advanced approach to variogram modelling by integrating genetic algorithms(GA)with machine learning-based linear regression,aiming to improve the accuracy and efficiency of geostatistical analysis,particularly in mineral exploration.The study combines GA and machine learning to optimise variogram parameters,including range,sill,and nugget,by minimising the root mean square error(RMSE)and maximising the coefficient of determination(R^(2)).The experimental variograms were computed and modelled using theoretical models,followed by optimisation via evolutionary algorithms.The method was applied to gravity data from the Ngoura-Batouri-Kette mining district in Eastern Cameroon,covering 141 data points.Sequential Gaussian Simulations(SGS)were employed for predictive mapping to validate simulated results against true values.Key findings show variograms with ranges between 24.71 km and 49.77 km,opti-mised RMSE and R^(2) values of 11.21 mGal^(2) and 0.969,respectively,after 42 generations of GA optimisation.Predictive mapping using SGS demonstrated that simulated values closely matched true values,with the simu-lated mean at 21.75 mGal compared to the true mean of 25.16 mGal,and variances of 465.70 mGal^(2) and 555.28 mGal^(2),respectively.The results confirmed spatial variability and anisotropies in the N170-N210 directions,consistent with prior studies.This work presents a novel integration of GA and machine learning for variogram modelling,offering an automated,efficient approach to parameter estimation.The methodology significantly enhances predictive geostatistical models,contributing to the advancement of mineral exploration and improving the precision and speed of decision-making in the petroleum and mining industries.展开更多
The liquid cooling system(LCS)of fuel cells is challenged by significant time delays,model uncertainties,pump and fan coupling,and frequent disturbances,leading to overshoot and control oscillations that degrade tempe...The liquid cooling system(LCS)of fuel cells is challenged by significant time delays,model uncertainties,pump and fan coupling,and frequent disturbances,leading to overshoot and control oscillations that degrade temperature regulation performance.To address these challenges,we propose a composite control scheme combining fuzzy logic and a variable-gain generalized supertwisting algorithm(VG-GSTA).Firstly,a one-dimensional(1D)fuzzy logic controler(FLC)for the pump ensures stable coolant flow,while a two-dimensional(2D)FLC for the fan regulates the stack temperature near the reference value.The VG-GSTA is then introduced to eliminate steady-state errors,offering resistance to disturbances and minimizing control oscillations.The equilibrium optimizer is used to fine-tune VG-GSTA parameters.Co-simulation verifies the effectiveness of our method,demonstrating its advantages in terms of disturbance immunity,overshoot suppression,tracking accuracy and response speed.展开更多
Internet of things networks often suffer from early node failures and short lifespan due to energy limits.Traditional routing methods are not enough.This work proposes a new hybrid algorithm called ACOGA.It combines A...Internet of things networks often suffer from early node failures and short lifespan due to energy limits.Traditional routing methods are not enough.This work proposes a new hybrid algorithm called ACOGA.It combines Ant Colony Optimization(ACO)and the Greedy Algorithm(GA).ACO finds smart paths while Greedy makes quick decisions.This improves energy use and performance.ACOGA outperforms Hybrid Energy-Efficient(HEE)and Adaptive Lossless Data Compression(ALDC)algorithms.After 500 rounds,only 5%of ACOGA’s nodes are dead,compared to 15%for HEE and 20%for ALDC.The network using ACOGA runs for 1200 rounds before the first nodes fail.HEE lasts 900 rounds and ALDC only 850.ACOGA saves at least 15%more energy by better distributing the load.It also achieves a 98%packet delivery rate.The method works well in mixed IoT networks like Smart Water Management Systems(SWMS).These systems have different power levels and communication ranges.The simulation of proposed model has been done in MATLAB simulator.The results show that that the proposed model outperform then the existing models.展开更多
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
针对在现场可编程门阵列(Field Programmable Gate Array,FPGA)上实现基于极化敏感阵列的多重信号分类(Multiple Signal Classification,MUSIC)算法进行二维波达方向(Direction of Arrival,DOA)和二维极化参数联合估计时,硬件资源占用...针对在现场可编程门阵列(Field Programmable Gate Array,FPGA)上实现基于极化敏感阵列的多重信号分类(Multiple Signal Classification,MUSIC)算法进行二维波达方向(Direction of Arrival,DOA)和二维极化参数联合估计时,硬件资源占用大、运行时间长的问题,提出了一种基于极化MUSIC算法的四维参数联合估计FPGA实现架构。该架构包括信号协方差矩阵计算模块、Jacobi旋转模块、噪声子空间提取模块、两级空间谱搜索模块和极化参数计算模块。Jacobi旋转模块被拆分为多个可复用模块,并采用查找表模块生成旋转矩阵。一级空间谱搜索模块通过二维DOA搜索初步确定信源的角度信息。二级空间谱搜索模块根据一级搜索的角度结果确定二级搜索区域各点的极化信息,并计算该区域的四维空间谱,区域内最小值对应的四维参数信息即为最终估计的信源方向角、俯仰角、极化辅助角和极化相位角。仿真结果表明,与传统极化MUSIC算法的四维搜索算法相比,该架构避免了大量四维空间谱计算,同时保证了四维参数估计的精度,显著减少了运行时间和硬件资源消耗。展开更多
Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion...Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion algorithm took advantage of the fast optimization ability of PSO to optimize the population screening link of GA.The Simulink simulation results showed that the convergence of the fitness function of the fusion algorithm was accelerated,the system response adjustment time was reduced,and the overshoot was almost zero.Then the algorithm was applied to the steering test of agricultural robot in various scenes.After modeling the steering system of agricultural robot,the steering test results in the unloaded suspended state showed that the PID control based on fusion algorithm reduced the rise time,response adjustment time and overshoot of the system,and improved the response speed and stability of the system,compared with the artificial trial and error PID control and the PID control based on GA.The actual road steering test results showed that the PID control response rise time based on the fusion algorithm was the shortest,about 4.43 s.When the target pulse number was set to 100,the actual mean value in the steady-state regulation stage was about 102.9,which was the closest to the target value among the three control methods,and the overshoot was reduced at the same time.The steering test results under various scene states showed that the PID control based on the proposed fusion algorithm had good anti-interference ability,it can adapt to the changes of environment and load and improve the performance of the control system.It was effective in the steering control of agricultural robot.This method can provide a reference for the precise steering control of other robots.展开更多
Game theory-based models and design tools have gained substantial prominence for controlling and optimizing behavior within distributed engineering systems due to the inherent distribution of decisions among individua...Game theory-based models and design tools have gained substantial prominence for controlling and optimizing behavior within distributed engineering systems due to the inherent distribution of decisions among individuals.In non-cooperative settings,aggregative games serve as a mathematical framework model for the interdependent optimal decision-making problem among a group of non-cooperative players.In such scenarios,each player's decision is influenced by an aggregation of all players'decisions.Nash equilibrium(NE)seeking in aggregative games has emerged as a vibrant topic driven by applications that harness the aggregation property.This paper presents a comprehensive overview of the current research on aggregative games with a focus on communication topology.A systematic classification is conducted on distributed algorithm research based on communication topologies such as undirected networks,directed networks,and time-varying networks.Furthermore,it sorts out the challenges and compares the algorithms'convergence performance.It also delves into real-world applications of distributed optimization techniques grounded in aggregative games.Finally,it proposes several challenges that can guide future research directions.展开更多
We propose a robust earthquake clustering method:the Bayesian Gaussian mixture model with nearest-neighbor distance(BGMM-NND)algorithm.Unlike the conventional nearest neighbor distance method,the BGMM-NND algorithm el...We propose a robust earthquake clustering method:the Bayesian Gaussian mixture model with nearest-neighbor distance(BGMM-NND)algorithm.Unlike the conventional nearest neighbor distance method,the BGMM-NND algorithm eliminates the need for hyperparameter tuning or reliance on fixed thresholds,offering enhanced flexibility for clustering across varied seismic scales.By integrating cumulative probability and BGMM with principal component analysis(PCA),the BGMM-NND algorithm effectively distinguishes between background and triggered earthquakes while maintaining the magnitude component and resolving the issue of excessively large spatial cluster domains.We apply the BGMM-NND algorithm to the Sichuan–Yunnan seismic catalog from 1971 to 2024,revealing notable variations in earthquake frequency,triggering characteristics,and recurrence patterns across different fault zones.Distinct clustering and triggering behaviors are identified along different segments of the Longmenshan Fault.Multiple seismic modes,namely,the short-distance mode,the medium-distance mode,the repeating-like mode,the uniform background mode,and the Wenchuan mode,are uncovered.The algorithm's flexibility and robust performance in earthquake clustering makes it a valuable tool for exploring seismicity characteristics,offering new insights into earthquake clustering and the spatiotemporal patterns of seismic activity.展开更多
This work proposes an optimization method for gas storage operation parameters under multi-factor coupled constraints to improve the peak-shaving capacity of gas storage reservoirs while ensuring operational safety.Pr...This work proposes an optimization method for gas storage operation parameters under multi-factor coupled constraints to improve the peak-shaving capacity of gas storage reservoirs while ensuring operational safety.Previous research primarily focused on integrating reservoir,wellbore,and surface facility constraints,often resulting in broad constraint ranges and slow model convergence.To solve this problem,the present study introduces additional constraints on maximum withdrawal rates by combining binomial deliverability equations with material balance equations for closed gas reservoirs,while considering extreme peak-shaving demands.This approach effectively narrows the constraint range.Subsequently,a collaborative optimization model with maximum gas production as the objective function is established,and the model employs a joint solution strategy combining genetic algorithms and numerical simulation techniques.Finally,this methodology was applied to optimize operational parameters for Gas Storage T.The results demonstrate:(1)The convergence of the model was achieved after 6 iterations,which significantly improved the convergence speed of the model;(2)The maximum working gas volume reached 11.605×10^(8) m^(3),which increased by 13.78%compared with the traditional optimization method;(3)This method greatly improves the operation safety and the ultimate peak load balancing capability.The research provides important technical support for the intelligent decision of injection and production parameters of gas storage and improving peak load balancing ability.展开更多
When dealing with expensive multiobjective optimization problems,majority of existing surrogate-assisted evolutionary algorithms(SAEAs)generate solutions in decision space and screen candidate solutions mostly by usin...When dealing with expensive multiobjective optimization problems,majority of existing surrogate-assisted evolutionary algorithms(SAEAs)generate solutions in decision space and screen candidate solutions mostly by using designed surrogate models.The generated solutions exhibit excessive randomness,which tends to reduce the likelihood of generating good-quality solutions and cause a long evolution to the optima.To improve SAEAs greatly,this work proposes an evolutionary algorithm based on surrogate and inverse surrogate models by 1)Employing a surrogate model in lieu of expensive(true)function evaluations;and 2)Proposing and using an inverse surrogate model to generate new solutions.By using the same training data but with its inputs and outputs being reversed,the latter is simple to train.It is then used to generate new vectors in objective space,which are mapped into decision space to obtain their corresponding solutions.Using a particular example,this work shows its advantages over existing SAEAs.The results of comparing it with state-of-the-art algorithms on expensive optimization problems show that it is highly competitive in both solution performance and efficiency.展开更多
To extract and display the significant information of combat systems,this paper introduces the methodology of functional cartography into combat networks and proposes an integrated framework named“functional cartogra...To extract and display the significant information of combat systems,this paper introduces the methodology of functional cartography into combat networks and proposes an integrated framework named“functional cartography of heterogeneous combat networks based on the operational chain”(FCBOC).In this framework,a functional module detection algorithm named operational chain-based label propagation algorithm(OCLPA),which considers the cooperation and interactions among combat entities and can thus naturally tackle network heterogeneity,is proposed to identify the functional modules of the network.Then,the nodes and their modules are classified into different roles according to their properties.A case study shows that FCBOC can provide a simplified description of disorderly information of combat networks and enable us to identify their functional and structural network characteristics.The results provide useful information to help commanders make precise and accurate decisions regarding the protection,disintegration or optimization of combat networks.Three algorithms are also compared with OCLPA to show that FCBOC can most effectively find functional modules with practical meaning.展开更多
Finding an optimal isolator arrangement for asymmetric structures using traditional conceptual design methods that can significantly minimize torsional response while ensuring efficient horizontal seismic isolation is...Finding an optimal isolator arrangement for asymmetric structures using traditional conceptual design methods that can significantly minimize torsional response while ensuring efficient horizontal seismic isolation is cumbersome and inefficient.Thus,this work develops a multi-objective optimization method to enhance the torsional resistance of asymmetric base-isolated structures.The primary objective is to simultaneously minimize the interstory rotation of the superstructure,the rotation of the isolation layer,and the interstory displacement of the superstructure without exceeding the isolator displacement limits.A fast non-dominated sorting genetic algorithm(NSGA-Ⅱ)is employed to satisfy this optimization objective.Subsequently,the isolator arrangement,encompassing both positions and categories,is optimized according to this multi-objective optimization method.Additionally,an optimization design platform is developed to streamline the design operation.This platform integrates the input of optimization parameters,the output of optimization results,the finite element analysis,and the multi-objective optimization method proposed herein.Finally,the application of this multi-objective optimization method and its associated platform are demonstrated on two asymmetric base-isolated structures of varying heights and plan configurations.The results indicate that the optimal isolator arrangement derived from the optimization method can further improve the control over the lateral and torsional responses of asymmetric base-isolated structures compared to conventional conceptual design methods.Notably,the interstory rotation of the optimal base-isolated structure is significantly reduced,constituting only approximately 33.7%of that observed in the original base-isolated structure.The proposed platform facilitates the automatic generation of the optimal design scheme for the isolators of asymmetric base-isolated structures,offering valuable insights and guidance for the burgeoning field of intelligent civil engineering design.展开更多
This paper proposes a separated trajectory tracking controller for fishing ships at sea state level 6 to solve the trajectory tracking problem of a fishing ship in a 6-level sea state,and to adapt to different working...This paper proposes a separated trajectory tracking controller for fishing ships at sea state level 6 to solve the trajectory tracking problem of a fishing ship in a 6-level sea state,and to adapt to different working environments and safety requirements.The nonlinear feedback method is used to improve the closed-loop gain shaping algorithm.By introducing the sine function,the problem of excessive control energy of the system can be effectively solved.Moreover,an integral separation design is used to solve the influence of the integral term in conventional PID controllers on the transient performance of the system.In this paper,a common 32.98 m large fiberglass reinforced plastic(FRP)trawler is adopted for simulation research at the winds scale of Beaufort No.7.The results show that the track error is smaller than 3.5 m.The method is safe,feasible,concise and effective and has popularization value in the direction of fishing ship trajectory tracking control.This method can be used to improve the level of informatization and intelligence of fishing ships.展开更多
基金National Natural Science Foundation of China(11971211,12171388).
文摘Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently,enhancing the robustness of scale-free networks has become a pressing issue.To address this problem,this paper proposes a Multi-Granularity Integration Algorithm(MGIA),which aims to improve the robustness of scale-free networks while keeping the initial degree of each node unchanged,ensuring network connectivity and avoiding the generation of multiple edges.The algorithm generates a multi-granularity structure from the initial network to be optimized,then uses different optimization strategies to optimize the networks at various granular layers in this structure,and finally realizes the information exchange between different granular layers,thereby further enhancing the optimization effect.We propose new network refresh,crossover,and mutation operators to ensure that the optimized network satisfies the given constraints.Meanwhile,we propose new network similarity and network dissimilarity evaluation metrics to improve the effectiveness of the optimization operators in the algorithm.In the experiments,the MGIA enhances the robustness of the scale-free network by 67.6%.This improvement is approximately 17.2%higher than the optimization effects achieved by eight currently existing complex network robustness optimization algorithms.
文摘The advent of microgrids in modern energy systems heralds a promising era of resilience,sustainability,and efficiency.Within the realm of grid-tied microgrids,the selection of an optimal optimization algorithm is critical for effective energy management,particularly in economic dispatching.This study compares the performance of Particle Swarm Optimization(PSO)and Genetic Algorithms(GA)in microgrid energy management systems,implemented using MATLAB tools.Through a comprehensive review of the literature and sim-ulations conducted in MATLAB,the study analyzes performance metrics,convergence speed,and the overall efficacy of GA and PSO,with a focus on economic dispatching tasks.Notably,a significant distinction emerges between the cost curves generated by the two algo-rithms for microgrid operation,with the PSO algorithm consistently resulting in lower costs due to its effective economic dispatching capabilities.Specifically,the utilization of the PSO approach could potentially lead to substantial savings on the power bill,amounting to approximately$15.30 in this evaluation.Thefindings provide insights into the strengths and limitations of each algorithm within the complex dynamics of grid-tied microgrids,thereby assisting stakeholders and researchers in arriving at informed decisions.This study contributes to the discourse on sustainable energy management by offering actionable guidance for the advancement of grid-tied micro-grid technologies through MATLAB-implemented optimization algorithms.
文摘The objective of this study is to develop an advanced approach to variogram modelling by integrating genetic algorithms(GA)with machine learning-based linear regression,aiming to improve the accuracy and efficiency of geostatistical analysis,particularly in mineral exploration.The study combines GA and machine learning to optimise variogram parameters,including range,sill,and nugget,by minimising the root mean square error(RMSE)and maximising the coefficient of determination(R^(2)).The experimental variograms were computed and modelled using theoretical models,followed by optimisation via evolutionary algorithms.The method was applied to gravity data from the Ngoura-Batouri-Kette mining district in Eastern Cameroon,covering 141 data points.Sequential Gaussian Simulations(SGS)were employed for predictive mapping to validate simulated results against true values.Key findings show variograms with ranges between 24.71 km and 49.77 km,opti-mised RMSE and R^(2) values of 11.21 mGal^(2) and 0.969,respectively,after 42 generations of GA optimisation.Predictive mapping using SGS demonstrated that simulated values closely matched true values,with the simu-lated mean at 21.75 mGal compared to the true mean of 25.16 mGal,and variances of 465.70 mGal^(2) and 555.28 mGal^(2),respectively.The results confirmed spatial variability and anisotropies in the N170-N210 directions,consistent with prior studies.This work presents a novel integration of GA and machine learning for variogram modelling,offering an automated,efficient approach to parameter estimation.The methodology significantly enhances predictive geostatistical models,contributing to the advancement of mineral exploration and improving the precision and speed of decision-making in the petroleum and mining industries.
基金Supported by the Major Science and Technology Project of Jilin Province(20220301010GX)the International Scientific and Technological Cooperation(20240402071GH).
文摘The liquid cooling system(LCS)of fuel cells is challenged by significant time delays,model uncertainties,pump and fan coupling,and frequent disturbances,leading to overshoot and control oscillations that degrade temperature regulation performance.To address these challenges,we propose a composite control scheme combining fuzzy logic and a variable-gain generalized supertwisting algorithm(VG-GSTA).Firstly,a one-dimensional(1D)fuzzy logic controler(FLC)for the pump ensures stable coolant flow,while a two-dimensional(2D)FLC for the fan regulates the stack temperature near the reference value.The VG-GSTA is then introduced to eliminate steady-state errors,offering resistance to disturbances and minimizing control oscillations.The equilibrium optimizer is used to fine-tune VG-GSTA parameters.Co-simulation verifies the effectiveness of our method,demonstrating its advantages in terms of disturbance immunity,overshoot suppression,tracking accuracy and response speed.
文摘Internet of things networks often suffer from early node failures and short lifespan due to energy limits.Traditional routing methods are not enough.This work proposes a new hybrid algorithm called ACOGA.It combines Ant Colony Optimization(ACO)and the Greedy Algorithm(GA).ACO finds smart paths while Greedy makes quick decisions.This improves energy use and performance.ACOGA outperforms Hybrid Energy-Efficient(HEE)and Adaptive Lossless Data Compression(ALDC)algorithms.After 500 rounds,only 5%of ACOGA’s nodes are dead,compared to 15%for HEE and 20%for ALDC.The network using ACOGA runs for 1200 rounds before the first nodes fail.HEE lasts 900 rounds and ALDC only 850.ACOGA saves at least 15%more energy by better distributing the load.It also achieves a 98%packet delivery rate.The method works well in mixed IoT networks like Smart Water Management Systems(SWMS).These systems have different power levels and communication ranges.The simulation of proposed model has been done in MATLAB simulator.The results show that that the proposed model outperform then the existing models.
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.
文摘针对在现场可编程门阵列(Field Programmable Gate Array,FPGA)上实现基于极化敏感阵列的多重信号分类(Multiple Signal Classification,MUSIC)算法进行二维波达方向(Direction of Arrival,DOA)和二维极化参数联合估计时,硬件资源占用大、运行时间长的问题,提出了一种基于极化MUSIC算法的四维参数联合估计FPGA实现架构。该架构包括信号协方差矩阵计算模块、Jacobi旋转模块、噪声子空间提取模块、两级空间谱搜索模块和极化参数计算模块。Jacobi旋转模块被拆分为多个可复用模块,并采用查找表模块生成旋转矩阵。一级空间谱搜索模块通过二维DOA搜索初步确定信源的角度信息。二级空间谱搜索模块根据一级搜索的角度结果确定二级搜索区域各点的极化信息,并计算该区域的四维空间谱,区域内最小值对应的四维参数信息即为最终估计的信源方向角、俯仰角、极化辅助角和极化相位角。仿真结果表明,与传统极化MUSIC算法的四维搜索算法相比,该架构避免了大量四维空间谱计算,同时保证了四维参数估计的精度,显著减少了运行时间和硬件资源消耗。
文摘Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion algorithm took advantage of the fast optimization ability of PSO to optimize the population screening link of GA.The Simulink simulation results showed that the convergence of the fitness function of the fusion algorithm was accelerated,the system response adjustment time was reduced,and the overshoot was almost zero.Then the algorithm was applied to the steering test of agricultural robot in various scenes.After modeling the steering system of agricultural robot,the steering test results in the unloaded suspended state showed that the PID control based on fusion algorithm reduced the rise time,response adjustment time and overshoot of the system,and improved the response speed and stability of the system,compared with the artificial trial and error PID control and the PID control based on GA.The actual road steering test results showed that the PID control response rise time based on the fusion algorithm was the shortest,about 4.43 s.When the target pulse number was set to 100,the actual mean value in the steady-state regulation stage was about 102.9,which was the closest to the target value among the three control methods,and the overshoot was reduced at the same time.The steering test results under various scene states showed that the PID control based on the proposed fusion algorithm had good anti-interference ability,it can adapt to the changes of environment and load and improve the performance of the control system.It was effective in the steering control of agricultural robot.This method can provide a reference for the precise steering control of other robots.
基金supported in part by the Fundamental Research Funds for the Central Universities(SWU-XDJH202312)the National Natural Science Foundation of China(62173278)the Chongqing Science Fund for Distinguished Young Scholars(2024NSCQJQX0103).
文摘Game theory-based models and design tools have gained substantial prominence for controlling and optimizing behavior within distributed engineering systems due to the inherent distribution of decisions among individuals.In non-cooperative settings,aggregative games serve as a mathematical framework model for the interdependent optimal decision-making problem among a group of non-cooperative players.In such scenarios,each player's decision is influenced by an aggregation of all players'decisions.Nash equilibrium(NE)seeking in aggregative games has emerged as a vibrant topic driven by applications that harness the aggregation property.This paper presents a comprehensive overview of the current research on aggregative games with a focus on communication topology.A systematic classification is conducted on distributed algorithm research based on communication topologies such as undirected networks,directed networks,and time-varying networks.Furthermore,it sorts out the challenges and compares the algorithms'convergence performance.It also delves into real-world applications of distributed optimization techniques grounded in aggregative games.Finally,it proposes several challenges that can guide future research directions.
基金supported by the National Key Research and Development Program of China(Grant Nos.2021YFC3000705 and 2021YFC3000705-05)the National Natural Science Foundation of China(Grant No.42074049)the Youth Innovation Promotion Association of the Chinese Academy of Sciences(Grant No.2023471).
文摘We propose a robust earthquake clustering method:the Bayesian Gaussian mixture model with nearest-neighbor distance(BGMM-NND)algorithm.Unlike the conventional nearest neighbor distance method,the BGMM-NND algorithm eliminates the need for hyperparameter tuning or reliance on fixed thresholds,offering enhanced flexibility for clustering across varied seismic scales.By integrating cumulative probability and BGMM with principal component analysis(PCA),the BGMM-NND algorithm effectively distinguishes between background and triggered earthquakes while maintaining the magnitude component and resolving the issue of excessively large spatial cluster domains.We apply the BGMM-NND algorithm to the Sichuan–Yunnan seismic catalog from 1971 to 2024,revealing notable variations in earthquake frequency,triggering characteristics,and recurrence patterns across different fault zones.Distinct clustering and triggering behaviors are identified along different segments of the Longmenshan Fault.Multiple seismic modes,namely,the short-distance mode,the medium-distance mode,the repeating-like mode,the uniform background mode,and the Wenchuan mode,are uncovered.The algorithm's flexibility and robust performance in earthquake clustering makes it a valuable tool for exploring seismicity characteristics,offering new insights into earthquake clustering and the spatiotemporal patterns of seismic activity.
基金supported by the Science and Technology Research Program of Chongqing Municipal Education Commission(KJQN202401501,KJZD-M202401501).
文摘This work proposes an optimization method for gas storage operation parameters under multi-factor coupled constraints to improve the peak-shaving capacity of gas storage reservoirs while ensuring operational safety.Previous research primarily focused on integrating reservoir,wellbore,and surface facility constraints,often resulting in broad constraint ranges and slow model convergence.To solve this problem,the present study introduces additional constraints on maximum withdrawal rates by combining binomial deliverability equations with material balance equations for closed gas reservoirs,while considering extreme peak-shaving demands.This approach effectively narrows the constraint range.Subsequently,a collaborative optimization model with maximum gas production as the objective function is established,and the model employs a joint solution strategy combining genetic algorithms and numerical simulation techniques.Finally,this methodology was applied to optimize operational parameters for Gas Storage T.The results demonstrate:(1)The convergence of the model was achieved after 6 iterations,which significantly improved the convergence speed of the model;(2)The maximum working gas volume reached 11.605×10^(8) m^(3),which increased by 13.78%compared with the traditional optimization method;(3)This method greatly improves the operation safety and the ultimate peak load balancing capability.The research provides important technical support for the intelligent decision of injection and production parameters of gas storage and improving peak load balancing ability.
基金supported in part by the National Natural Science Foundation of China(51775385)the Natural Science Foundation of Shanghai(23ZR1466000)+2 种基金the Shanghai Industrial Collaborative Science and Technology Innovation Project(2021-cyxt2-kj10)the Innovation Program of Shanghai Municipal Education Commission(202101070007E00098)Fundo para o Desenvolvimento das Ciencias e da Tecnologia(FDCT)(0147/2024/AFJ).
文摘When dealing with expensive multiobjective optimization problems,majority of existing surrogate-assisted evolutionary algorithms(SAEAs)generate solutions in decision space and screen candidate solutions mostly by using designed surrogate models.The generated solutions exhibit excessive randomness,which tends to reduce the likelihood of generating good-quality solutions and cause a long evolution to the optima.To improve SAEAs greatly,this work proposes an evolutionary algorithm based on surrogate and inverse surrogate models by 1)Employing a surrogate model in lieu of expensive(true)function evaluations;and 2)Proposing and using an inverse surrogate model to generate new solutions.By using the same training data but with its inputs and outputs being reversed,the latter is simple to train.It is then used to generate new vectors in objective space,which are mapped into decision space to obtain their corresponding solutions.Using a particular example,this work shows its advantages over existing SAEAs.The results of comparing it with state-of-the-art algorithms on expensive optimization problems show that it is highly competitive in both solution performance and efficiency.
文摘To extract and display the significant information of combat systems,this paper introduces the methodology of functional cartography into combat networks and proposes an integrated framework named“functional cartography of heterogeneous combat networks based on the operational chain”(FCBOC).In this framework,a functional module detection algorithm named operational chain-based label propagation algorithm(OCLPA),which considers the cooperation and interactions among combat entities and can thus naturally tackle network heterogeneity,is proposed to identify the functional modules of the network.Then,the nodes and their modules are classified into different roles according to their properties.A case study shows that FCBOC can provide a simplified description of disorderly information of combat networks and enable us to identify their functional and structural network characteristics.The results provide useful information to help commanders make precise and accurate decisions regarding the protection,disintegration or optimization of combat networks.Three algorithms are also compared with OCLPA to show that FCBOC can most effectively find functional modules with practical meaning.
基金National Natural Science Foundation of China under Grant No.52278490。
文摘Finding an optimal isolator arrangement for asymmetric structures using traditional conceptual design methods that can significantly minimize torsional response while ensuring efficient horizontal seismic isolation is cumbersome and inefficient.Thus,this work develops a multi-objective optimization method to enhance the torsional resistance of asymmetric base-isolated structures.The primary objective is to simultaneously minimize the interstory rotation of the superstructure,the rotation of the isolation layer,and the interstory displacement of the superstructure without exceeding the isolator displacement limits.A fast non-dominated sorting genetic algorithm(NSGA-Ⅱ)is employed to satisfy this optimization objective.Subsequently,the isolator arrangement,encompassing both positions and categories,is optimized according to this multi-objective optimization method.Additionally,an optimization design platform is developed to streamline the design operation.This platform integrates the input of optimization parameters,the output of optimization results,the finite element analysis,and the multi-objective optimization method proposed herein.Finally,the application of this multi-objective optimization method and its associated platform are demonstrated on two asymmetric base-isolated structures of varying heights and plan configurations.The results indicate that the optimal isolator arrangement derived from the optimization method can further improve the control over the lateral and torsional responses of asymmetric base-isolated structures compared to conventional conceptual design methods.Notably,the interstory rotation of the optimal base-isolated structure is significantly reduced,constituting only approximately 33.7%of that observed in the original base-isolated structure.The proposed platform facilitates the automatic generation of the optimal design scheme for the isolators of asymmetric base-isolated structures,offering valuable insights and guidance for the burgeoning field of intelligent civil engineering design.
基金supported by Liaoning Provincial Department of Education 2023 Basic Research Projects for Universities and Colleges(Grant No.JYTQN2023131)Liaoning Provincial Science and Technology Program:Cooperative Control and Recognition of Unmanned Vessels for Fishing Vessel Operation Scenarios(Grant No.600024003)Liaoning Provincial Department of Education Scientific Research Funding Project(Grant No.LJKZ0726).
文摘This paper proposes a separated trajectory tracking controller for fishing ships at sea state level 6 to solve the trajectory tracking problem of a fishing ship in a 6-level sea state,and to adapt to different working environments and safety requirements.The nonlinear feedback method is used to improve the closed-loop gain shaping algorithm.By introducing the sine function,the problem of excessive control energy of the system can be effectively solved.Moreover,an integral separation design is used to solve the influence of the integral term in conventional PID controllers on the transient performance of the system.In this paper,a common 32.98 m large fiberglass reinforced plastic(FRP)trawler is adopted for simulation research at the winds scale of Beaufort No.7.The results show that the track error is smaller than 3.5 m.The method is safe,feasible,concise and effective and has popularization value in the direction of fishing ship trajectory tracking control.This method can be used to improve the level of informatization and intelligence of fishing ships.
