Federated learning is a distributed framework that trains a centralised model using data from multiple clients without transferring that data to a central server.Despite rapid progress,federated learning still faces s...Federated learning is a distributed framework that trains a centralised model using data from multiple clients without transferring that data to a central server.Despite rapid progress,federated learning still faces several unsolved challenges.Specifically,communication costs and system heterogeneity,such as nonidentical data distribution,hinder federated learning's progress.Several approaches have recently emerged for federated learning involving heterogeneous clients with varying computational capabilities(namely,heterogeneous federated learning).However,heterogeneous federated learning faces two key challenges:optimising model size and determining client selection ratios.Moreover,efficiently aggregating local models from clients with diverse capabilities is crucial for addressing system heterogeneity and communication efficiency.This paper proposes an evolutionary multiobjective optimisation framework for heterogeneous federated learning(MOHFL)to address these issues.Our approach elegantly formulates and solves a biobjective optimisation problem that minimises communication cost and model error rate.The decision variables in this framework comprise model sizes and client selection ratios for each Q client cluster,yielding a total of 2×Q optimisation parameters to be tuned.We develop a partition-based strategy for MOHFL that segregates clients into clusters based on their communication and computation capabilities.Additionally,we implement an adaptive model sizing mechanism that dynamically assigns appropriate subnetwork architectures to clients based on their computational constraints.We also propose a unified aggregation framework to combine models of varying sizes from heterogeneous clients effectively.Extensive experiments on multiple datasets demonstrate the effectiveness and superiority of our proposed method compared to existing approaches.展开更多
The highly efficient electrochemical treatment technology for dye-polluted wastewater is one of hot research topics in industrial wastewater treatment.This study reported a three-dimensional electrochemical treatment ...The highly efficient electrochemical treatment technology for dye-polluted wastewater is one of hot research topics in industrial wastewater treatment.This study reported a three-dimensional electrochemical treatment process integrating graphite intercalation compound(GIC)adsorption,direct anodic oxidation,and·OH oxidation for decolourising Reactive Black 5(RB5)from aqueous solutions.The electrochemical process was optimised using the novel progressive central composite design-response surface methodology(CCD-NPRSM),hybrid artificial neural network-extreme gradient boosting(hybrid ANN-XGBoost),and classification and regression trees(CART).CCD-NPRSM and hybrid ANN-XGBoost were employed to minimise errors in evaluating the electrochemical process involving three manipulated operational parameters:current density,electrolysis(treatment)time,and initial dye concentration.The optimised decolourisation efficiencies were 99.30%,96.63%,and 99.14%for CCD-NPRSM,hybrid ANN-XGBoost,and CART,respectively,compared to the 98.46%RB5 removal rate observed experimentally under optimum conditions:approximately 20 mA/cm^(2) of current density,20 min of electrolysis time,and 65 mg/L of RB5.The optimised mineralisation efficiencies ranged between 89%and 92%for different models based on total organic carbon(TOC).Experimental studies confirmed that the predictive efficiency of optimised models ranked in the descending order of hybrid ANN-XGBoost,CCD-NPRSM,and CART.Model validation using analysis of variance(ANOVA)revealed that hybrid ANN-XGBoost had a mean squared error(MSE)and a coefficient of determination(R^(2))of approximately 0.014 and 0.998,respectively,for the RB5 removal efficiency,outperforming CCD-NPRSM with MSE and R^(2) of 0.518 and 0.998,respectively.Overall,the hybrid ANN-XGBoost approach is the most feasible technique for assessing the electrochemical treatment efficiency in RB5 dye wastewater decolourisation.展开更多
This article presents the design of a microfabricated bio-inspired flapping-wing Nnano Aaerial Vvehicle(NAV),driven by an electromagnetic system.Our approach is based on artificial wings composed of rigid bodies conne...This article presents the design of a microfabricated bio-inspired flapping-wing Nnano Aaerial Vvehicle(NAV),driven by an electromagnetic system.Our approach is based on artificial wings composed of rigid bodies connected by compliant links,which optimise aerodynamic forces though replicating the complex wing kinematics of insects.The originality of this article lies in a new design methodology based on a triple equivalence between a 3D model,a multibody model,and a mass/spring model(0D)which reduces the number of parameters in the problem.This approach facilitates NAV optimisation by using only the mass/spring model,thereby simplifying the design process while maintaining high accuracy.Two wing geometries are studied and optimised in this article to produce large-amplitude wing motions(approximately 40^\circ),and enabling flapping and twisting motion in quadrature.The results are validated thanks to experimental measurements for the large amplitude and through finite element simulations for the combined motion,confirming the effectiveness of this strategy for a NAV weighing less than 40 mg with a wingspan of under 3 cm.展开更多
This paper presents an investigation of the tribological performance of AA2024–B_(4)C composites,with a specific focus on the influence of reinforcement and processing parameters.In this study three input parameters ...This paper presents an investigation of the tribological performance of AA2024–B_(4)C composites,with a specific focus on the influence of reinforcement and processing parameters.In this study three input parameters were varied:B_(4)C weight percentage,milling time,and normal load,to evaluate their effects on two output parameters:wear loss and the coefficient of friction.AA2024 alloy was used as the matrix alloy,while B_(4)C particles were used as reinforcement.Due to the high hardness and wear resistance of B_(4)C,the optimized composite shows strong potential for use in aerospace structural elements and automotive brake components.The optimisation of tribological behaviour was conducted using a Taguchi-Grey Relational Analysis(Taguchi-GRA)and the Technique for Order of Preference by Similarity to Ideal Solution(TOPSIS).A total of 27 combinations of input parameters were analysed,varying the B_(4)C content(0,10,and 15 wt.%),milling time(0,15,and 25 h),and normal load(1,5,and 10 N).Wear loss and the coefficient of friction were numerically evaluated and selected as criteria for optimisation.Artificial Neural Networks(ANNs)were also applied for two outputs simultaneously.TOPSIS identified Alternative 1 as the optimal solution,confirming the results obtained using the Taguchi Grey method.The optimal condition obtained(10 wt.%B_(4)C,25 h milling time,10 N load)resulted in a minimum wear loss of 1.7 mg and a coefficient of friction of 0.176,confirming significant enhancement in tribological behaviour.Based on the results,both the B_(4)C content and the applied processing conditions have a significant impact on wear loss and frictional properties.This approach demonstrates high reliability and confidence,enabling the design of future composite materials with optimal properties for specific applications.展开更多
This review synthesises and assesses the most recent developments in Unmanned Aerial Vehicles(UAVs)and swarm robotics,with a specific emphasis on optimisation strategies,path planning,and formation control.The study i...This review synthesises and assesses the most recent developments in Unmanned Aerial Vehicles(UAVs)and swarm robotics,with a specific emphasis on optimisation strategies,path planning,and formation control.The study identifies key methodologies that are driving progress in the field by conducting a comprehensive analysis of seven critical publications.The following are included:sensor-based platforms that facilitate effective obstacle avoidance,cluster-based hierarchical path planning for efficient navigation,and adaptive hybrid controllers for dynamic environments.The review emphasises the substantial contribution of optimisation techniques,including Max-Min Ant Colony Optimisation(MMACO),to the improvement of convergence rates and the enhancement of path efficiency.The effectiveness of various navigation systems in diverse operational contexts is demonstrated through comparative analysis,which provides valuable insights into the system’s adaptability and performance.The primary findings underscore the strengths and limitations of current methodologies,thereby identifying voids in research and practical applications.This review offers actionable insights for academicians and practitioners who are striving to advance UAV and swarm robotics technology by addressing these challenges.The study concludes with a discussion of future directions,which underscores the potential for innovative solutions to enhance UAV systems in complex,dynamic environments.展开更多
The challenge of optimising multimodal functions within high-dimensional domains constitutes a notable difficulty in evolutionary computation research.Addressing this issue,this study introduces the Deep Backtracking ...The challenge of optimising multimodal functions within high-dimensional domains constitutes a notable difficulty in evolutionary computation research.Addressing this issue,this study introduces the Deep Backtracking Bare-Bones Particle Swarm Optimisation(DBPSO)algorithm,an innovative approach built upon the integration of the Deep Memory Storage Mechanism(DMSM)and the Dynamic Memory Activation Strategy(DMAS).The DMSM enhances the memory retention for the globally optimal particle,promoting interaction between standard particles and their historically optimal counterparts.In parallel,DMAS assures the updated position of the globally optimal particle is appropriately aligned with the deep memory repository.The efficacy of DBPSO was rigorously assessed through a series of simulations employing the CEC2017 benchmark suite.A comparative analysis juxtaposed DBPSO's performance against five contemporary evolutionary algorithms across two experimental conditions:Dimension-50 and Dimension-100.In the 50D trials,DBPSO attained an average ranking of 2.03,whereas in the 100D scenarios,it improved to an average ranking of 1.9.Further examination utilising the CEC2019 benchmark functions revealed DBPSO's robustness,securing four first-place finishes,three second-place standings,and three third-place positions,culminating in an unmatched average ranking of 1.9 across all algorithms.These empirical results corroborate DBPSO's proficiency in delivering precise solutions for complex,high-dimensional optimisation challenges.展开更多
A general and new explicit isogeometric topology optimisation approach with moving morphable voids(MMV)is proposed.In this approach,a novel multiresolution scheme with two distinct discretisation levels is developed t...A general and new explicit isogeometric topology optimisation approach with moving morphable voids(MMV)is proposed.In this approach,a novel multiresolution scheme with two distinct discretisation levels is developed to obtain high-resolution designs with a relatively low computational cost.Ersatz material model based on Greville abscissae collocation scheme is utilised to represent both the Young’s modulus of the material and the density field.Two benchmark examples are tested to illustrate the effectiveness of the proposed method.Numerical results show that high-resolution designs can be obtained with relatively low computational cost,and the optimisation can be significantly improved without introducing additional DOFs.展开更多
This paper presents an optimisatiombased verification process for obstacle avoidance systems of a unicycle-like mobile robot. It is a novel approach for the collision avoidance verification process. Local and global o...This paper presents an optimisatiombased verification process for obstacle avoidance systems of a unicycle-like mobile robot. It is a novel approach for the collision avoidance verification process. Local and global optimisation based verification processes are developed to find the worst-case parameters and the worst-case distance between the robot and an obstacle. The kinematic and dynamic model of the unicycle-like mobile robot is first introduced with force and torque as the inputs. The design of the control system is split into two parts. One is velocity and rotation using the robot dynamics, and the other is the incremental motion planning for robot kinematics. The artificial potential field method is chosen as a path planning and obstacle avoidance candidate technique for verification study as it is simple and widely used. Different optimisation algorithms are applied and compared for the purpose of verification. It is shown that even for a simple case study where only mass and inertia variations are considered, a local optimization based verification method may fail to identify the worst case. Two global optimisation methods have been investigated： genetic algorithms （GAs） and GLOBAL algorithms. Both of these methods successfully find the worst case. The verification process confirms that the obstacle avoidance algorithm functions correctly in the presence of all the possible parameter variations.展开更多
针对快速扩展随机树(Rapidly-Exploring Random Tree,RRT)算法在结合无人船进行路径规划时存在规划时间长、路径冗余大、路径平滑度不符合欠驱动无人船航行要求等问题,提出一种改进RRT的无人船全局路径规划算法。算法中将贝叶斯优化算...针对快速扩展随机树(Rapidly-Exploring Random Tree,RRT)算法在结合无人船进行路径规划时存在规划时间长、路径冗余大、路径平滑度不符合欠驱动无人船航行要求等问题,提出一种改进RRT的无人船全局路径规划算法。算法中将贝叶斯优化算法融入目标采样过程,增强目标点采样导向性;引入动态步长和双向贪心剪枝策略作为重要辅助,进一步提升算法效率和路径质量;得到初始路径后采用动态权重3次B样条曲线进一步平滑处理。最后在3种类型障碍物环境下进行仿真实验并与RRT、RRT^(*)算法进行对比。结果表明,改进RRT算法在规划时长、路径长度以及路径质量等方面有明显优势。改进后算法效率更高,路径平滑度更高,研究成果可为无人船自主航行提供参考。展开更多
基金supported by the National Research Foundation of Korea grant funded by the Korea government(RS-2023-00217116)。
文摘Federated learning is a distributed framework that trains a centralised model using data from multiple clients without transferring that data to a central server.Despite rapid progress,federated learning still faces several unsolved challenges.Specifically,communication costs and system heterogeneity,such as nonidentical data distribution,hinder federated learning's progress.Several approaches have recently emerged for federated learning involving heterogeneous clients with varying computational capabilities(namely,heterogeneous federated learning).However,heterogeneous federated learning faces two key challenges:optimising model size and determining client selection ratios.Moreover,efficiently aggregating local models from clients with diverse capabilities is crucial for addressing system heterogeneity and communication efficiency.This paper proposes an evolutionary multiobjective optimisation framework for heterogeneous federated learning(MOHFL)to address these issues.Our approach elegantly formulates and solves a biobjective optimisation problem that minimises communication cost and model error rate.The decision variables in this framework comprise model sizes and client selection ratios for each Q client cluster,yielding a total of 2×Q optimisation parameters to be tuned.We develop a partition-based strategy for MOHFL that segregates clients into clusters based on their communication and computation capabilities.Additionally,we implement an adaptive model sizing mechanism that dynamically assigns appropriate subnetwork architectures to clients based on their computational constraints.We also propose a unified aggregation framework to combine models of varying sizes from heterogeneous clients effectively.Extensive experiments on multiple datasets demonstrate the effectiveness and superiority of our proposed method compared to existing approaches.
文摘The highly efficient electrochemical treatment technology for dye-polluted wastewater is one of hot research topics in industrial wastewater treatment.This study reported a three-dimensional electrochemical treatment process integrating graphite intercalation compound(GIC)adsorption,direct anodic oxidation,and·OH oxidation for decolourising Reactive Black 5(RB5)from aqueous solutions.The electrochemical process was optimised using the novel progressive central composite design-response surface methodology(CCD-NPRSM),hybrid artificial neural network-extreme gradient boosting(hybrid ANN-XGBoost),and classification and regression trees(CART).CCD-NPRSM and hybrid ANN-XGBoost were employed to minimise errors in evaluating the electrochemical process involving three manipulated operational parameters:current density,electrolysis(treatment)time,and initial dye concentration.The optimised decolourisation efficiencies were 99.30%,96.63%,and 99.14%for CCD-NPRSM,hybrid ANN-XGBoost,and CART,respectively,compared to the 98.46%RB5 removal rate observed experimentally under optimum conditions:approximately 20 mA/cm^(2) of current density,20 min of electrolysis time,and 65 mg/L of RB5.The optimised mineralisation efficiencies ranged between 89%and 92%for different models based on total organic carbon(TOC).Experimental studies confirmed that the predictive efficiency of optimised models ranked in the descending order of hybrid ANN-XGBoost,CCD-NPRSM,and CART.Model validation using analysis of variance(ANOVA)revealed that hybrid ANN-XGBoost had a mean squared error(MSE)and a coefficient of determination(R^(2))of approximately 0.014 and 0.998,respectively,for the RB5 removal efficiency,outperforming CCD-NPRSM with MSE and R^(2) of 0.518 and 0.998,respectively.Overall,the hybrid ANN-XGBoost approach is the most feasible technique for assessing the electrochemical treatment efficiency in RB5 dye wastewater decolourisation.
基金supported by ANR-ASTRID NANOFLY(ANR-19-ASTR-0023)and French AID(Defense Innovation Agency).
文摘This article presents the design of a microfabricated bio-inspired flapping-wing Nnano Aaerial Vvehicle(NAV),driven by an electromagnetic system.Our approach is based on artificial wings composed of rigid bodies connected by compliant links,which optimise aerodynamic forces though replicating the complex wing kinematics of insects.The originality of this article lies in a new design methodology based on a triple equivalence between a 3D model,a multibody model,and a mass/spring model(0D)which reduces the number of parameters in the problem.This approach facilitates NAV optimisation by using only the mass/spring model,thereby simplifying the design process while maintaining high accuracy.Two wing geometries are studied and optimised in this article to produce large-amplitude wing motions(approximately 40^\circ),and enabling flapping and twisting motion in quadrature.The results are validated thanks to experimental measurements for the large amplitude and through finite element simulations for the combined motion,confirming the effectiveness of this strategy for a NAV weighing less than 40 mg with a wingspan of under 3 cm.
文摘This paper presents an investigation of the tribological performance of AA2024–B_(4)C composites,with a specific focus on the influence of reinforcement and processing parameters.In this study three input parameters were varied:B_(4)C weight percentage,milling time,and normal load,to evaluate their effects on two output parameters:wear loss and the coefficient of friction.AA2024 alloy was used as the matrix alloy,while B_(4)C particles were used as reinforcement.Due to the high hardness and wear resistance of B_(4)C,the optimized composite shows strong potential for use in aerospace structural elements and automotive brake components.The optimisation of tribological behaviour was conducted using a Taguchi-Grey Relational Analysis(Taguchi-GRA)and the Technique for Order of Preference by Similarity to Ideal Solution(TOPSIS).A total of 27 combinations of input parameters were analysed,varying the B_(4)C content(0,10,and 15 wt.%),milling time(0,15,and 25 h),and normal load(1,5,and 10 N).Wear loss and the coefficient of friction were numerically evaluated and selected as criteria for optimisation.Artificial Neural Networks(ANNs)were also applied for two outputs simultaneously.TOPSIS identified Alternative 1 as the optimal solution,confirming the results obtained using the Taguchi Grey method.The optimal condition obtained(10 wt.%B_(4)C,25 h milling time,10 N load)resulted in a minimum wear loss of 1.7 mg and a coefficient of friction of 0.176,confirming significant enhancement in tribological behaviour.Based on the results,both the B_(4)C content and the applied processing conditions have a significant impact on wear loss and frictional properties.This approach demonstrates high reliability and confidence,enabling the design of future composite materials with optimal properties for specific applications.
文摘This review synthesises and assesses the most recent developments in Unmanned Aerial Vehicles(UAVs)and swarm robotics,with a specific emphasis on optimisation strategies,path planning,and formation control.The study identifies key methodologies that are driving progress in the field by conducting a comprehensive analysis of seven critical publications.The following are included:sensor-based platforms that facilitate effective obstacle avoidance,cluster-based hierarchical path planning for efficient navigation,and adaptive hybrid controllers for dynamic environments.The review emphasises the substantial contribution of optimisation techniques,including Max-Min Ant Colony Optimisation(MMACO),to the improvement of convergence rates and the enhancement of path efficiency.The effectiveness of various navigation systems in diverse operational contexts is demonstrated through comparative analysis,which provides valuable insights into the system’s adaptability and performance.The primary findings underscore the strengths and limitations of current methodologies,thereby identifying voids in research and practical applications.This review offers actionable insights for academicians and practitioners who are striving to advance UAV and swarm robotics technology by addressing these challenges.The study concludes with a discussion of future directions,which underscores the potential for innovative solutions to enhance UAV systems in complex,dynamic environments.
基金supported by the Artificial Intelligence Innovation Project of Wuhan Science and Technology Bureau,2023010402040016the Natural Science Foundation of Hubei Province of China,2022CFB076,JSPS KAKENHI,JP25K15279,Natural Science Foundation of Hubei Province,2023AFB003+1 种基金the National Natural Science Foundation of China,52201363the Education Department Scientific Research Programme Project of Hubei Province of China,Q20222208.
文摘The challenge of optimising multimodal functions within high-dimensional domains constitutes a notable difficulty in evolutionary computation research.Addressing this issue,this study introduces the Deep Backtracking Bare-Bones Particle Swarm Optimisation(DBPSO)algorithm,an innovative approach built upon the integration of the Deep Memory Storage Mechanism(DMSM)and the Dynamic Memory Activation Strategy(DMAS).The DMSM enhances the memory retention for the globally optimal particle,promoting interaction between standard particles and their historically optimal counterparts.In parallel,DMAS assures the updated position of the globally optimal particle is appropriately aligned with the deep memory repository.The efficacy of DBPSO was rigorously assessed through a series of simulations employing the CEC2017 benchmark suite.A comparative analysis juxtaposed DBPSO's performance against five contemporary evolutionary algorithms across two experimental conditions:Dimension-50 and Dimension-100.In the 50D trials,DBPSO attained an average ranking of 2.03,whereas in the 100D scenarios,it improved to an average ranking of 1.9.Further examination utilising the CEC2019 benchmark functions revealed DBPSO's robustness,securing four first-place finishes,three second-place standings,and three third-place positions,culminating in an unmatched average ranking of 1.9 across all algorithms.These empirical results corroborate DBPSO's proficiency in delivering precise solutions for complex,high-dimensional optimisation challenges.
基金National Natural Science Foundation of China under Grant Nos.51675525 and 11725211.
文摘A general and new explicit isogeometric topology optimisation approach with moving morphable voids(MMV)is proposed.In this approach,a novel multiresolution scheme with two distinct discretisation levels is developed to obtain high-resolution designs with a relatively low computational cost.Ersatz material model based on Greville abscissae collocation scheme is utilised to represent both the Young’s modulus of the material and the density field.Two benchmark examples are tested to illustrate the effectiveness of the proposed method.Numerical results show that high-resolution designs can be obtained with relatively low computational cost,and the optimisation can be significantly improved without introducing additional DOFs.
文摘This paper presents an optimisatiombased verification process for obstacle avoidance systems of a unicycle-like mobile robot. It is a novel approach for the collision avoidance verification process. Local and global optimisation based verification processes are developed to find the worst-case parameters and the worst-case distance between the robot and an obstacle. The kinematic and dynamic model of the unicycle-like mobile robot is first introduced with force and torque as the inputs. The design of the control system is split into two parts. One is velocity and rotation using the robot dynamics, and the other is the incremental motion planning for robot kinematics. The artificial potential field method is chosen as a path planning and obstacle avoidance candidate technique for verification study as it is simple and widely used. Different optimisation algorithms are applied and compared for the purpose of verification. It is shown that even for a simple case study where only mass and inertia variations are considered, a local optimization based verification method may fail to identify the worst case. Two global optimisation methods have been investigated： genetic algorithms （GAs） and GLOBAL algorithms. Both of these methods successfully find the worst case. The verification process confirms that the obstacle avoidance algorithm functions correctly in the presence of all the possible parameter variations.
文摘针对快速扩展随机树(Rapidly-Exploring Random Tree,RRT)算法在结合无人船进行路径规划时存在规划时间长、路径冗余大、路径平滑度不符合欠驱动无人船航行要求等问题,提出一种改进RRT的无人船全局路径规划算法。算法中将贝叶斯优化算法融入目标采样过程,增强目标点采样导向性;引入动态步长和双向贪心剪枝策略作为重要辅助,进一步提升算法效率和路径质量;得到初始路径后采用动态权重3次B样条曲线进一步平滑处理。最后在3种类型障碍物环境下进行仿真实验并与RRT、RRT^(*)算法进行对比。结果表明,改进RRT算法在规划时长、路径长度以及路径质量等方面有明显优势。改进后算法效率更高,路径平滑度更高,研究成果可为无人船自主航行提供参考。
