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
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
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.
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.
