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 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
Proportioning is an important part of sintering,as it affects the cost of sintering and the quality of sintered ore.To address the problems posed by the complex raw material information and numerous constraints in the...Proportioning is an important part of sintering,as it affects the cost of sintering and the quality of sintered ore.To address the problems posed by the complex raw material information and numerous constraints in the sintering process,a multi-objective optimisation model for sintering proportioning was established,which takes the proportioning cost and TFe as the optimisation objectives.Additionally,an improved multi-objective beluga whale optimisation(IMOBWO)algorithm was proposed to solve the nonlinear,multi-constrained multi-objective optimisation problems.The algorithm uses the con-strained non-dominance criterion to deal with the constraint problem in the model.Moreover,the algorithm employs an opposite learning strategy and a population guidance mechanism based on angular competition and two-population competition strategy to enhance convergence and population diversity.The actual proportioning of a steel plant indicates that the IMOBWO algorithm applied to the ore proportioning process has good convergence and obtains the uniformly distributed Pareto front.Meanwhile,compared with the actual proportioning scheme,the proportioning cost is reduced by 4.3361¥/t,and the TFe content in the mixture is increased by 0.0367%in the optimal compromise solution.Therefore,the proposed method effectively balances the cost and total iron,facilitating the comprehensive utilisation of sintered iron ore resources while ensuring quality assurance.展开更多
We evaluate an adaptive optimisation methodology,Bayesian optimisation(BO),for designing a minimum weight explosive reactive armour(ERA)for protection against a surrogate medium calibre kinetic energy(KE)long rod proj...We evaluate an adaptive optimisation methodology,Bayesian optimisation(BO),for designing a minimum weight explosive reactive armour(ERA)for protection against a surrogate medium calibre kinetic energy(KE)long rod projectile and surrogate shaped charge(SC)warhead.We perform the optimisation using a conventional BO methodology and compare it with a conventional trial-and-error approach from a human expert.A third approach,utilising a novel human-machine teaming framework for BO is also evaluated.Data for the optimisation is generated using numerical simulations that are demonstrated to provide reasonable qualitative agreement with reference experiments.The human-machine teaming methodology is shown to identify the optimum ERA design in the fewest number of evaluations,outperforming both the stand-alone human and stand-alone BO methodologies.From a design space of almost 1800 configurations the human-machine teaming approach identifies the minimum weight ERA design in 10 samples.展开更多
Decomposition of a complex multi-objective optimisation problem(MOP)to multiple simple subMOPs,known as M2M for short,is an effective approach to multi-objective optimisation.However,M2M facilitates little communicati...Decomposition of a complex multi-objective optimisation problem(MOP)to multiple simple subMOPs,known as M2M for short,is an effective approach to multi-objective optimisation.However,M2M facilitates little communication/collaboration between subMOPs,which limits its use in complex optimisation scenarios.This paper extends the M2M framework to develop a unified algorithm for both multi-objective and manyobjective optimisation.Through bilevel decomposition,an MOP is divided into multiple subMOPs at upper level,each of which is further divided into a number of single-objective subproblems at lower level.Neighbouring subMOPs are allowed to share some subproblems so that the knowledge gained from solving one subMOP can be transferred to another,and eventually to all the subMOPs.The bilevel decomposition is readily combined with some new mating selection and population update strategies,leading to a high-performance algorithm that competes effectively against a number of state-of-the-arts studied in this paper for both multiand many-objective optimisation.Parameter analysis and component analysis have been also carried out to further justify the proposed algorithm.展开更多
In real-world applications, datasets frequently contain outliers, which can hinder the generalization ability of machine learning models. Bayesian classifiers, a popular supervised learning method, rely on accurate pr...In real-world applications, datasets frequently contain outliers, which can hinder the generalization ability of machine learning models. Bayesian classifiers, a popular supervised learning method, rely on accurate probability density estimation for classifying continuous datasets. However, achieving precise density estimation with datasets containing outliers poses a significant challenge. This paper introduces a Bayesian classifier that utilizes optimized robust kernel density estimation to address this issue. Our proposed method enhances the accuracy of probability density distribution estimation by mitigating the impact of outliers on the training sample’s estimated distribution. Unlike the conventional kernel density estimator, our robust estimator can be seen as a weighted kernel mapping summary for each sample. This kernel mapping performs the inner product in the Hilbert space, allowing the kernel density estimation to be considered the average of the samples’ mapping in the Hilbert space using a reproducing kernel. M-estimation techniques are used to obtain accurate mean values and solve the weights. Meanwhile, complete cross-validation is used as the objective function to search for the optimal bandwidth, which impacts the estimator. The Harris Hawks Optimisation optimizes the objective function to improve the estimation accuracy. The experimental results show that it outperforms other optimization algorithms regarding convergence speed and objective function value during the bandwidth search. The optimal robust kernel density estimator achieves better fitness performance than the traditional kernel density estimator when the training data contains outliers. The Naïve Bayesian with optimal robust kernel density estimation improves the generalization in the classification with outliers.展开更多
Cloud computing has rapidly evolved into a critical technology,seamlessly integrating into various aspects of daily life.As user demand for cloud services continues to surge,the need for efficient virtualization and r...Cloud computing has rapidly evolved into a critical technology,seamlessly integrating into various aspects of daily life.As user demand for cloud services continues to surge,the need for efficient virtualization and resource management becomes paramount.At the core of this efficiency lies task scheduling,a complex process that determines how tasks are allocated and executed across cloud resources.While extensive research has been conducted in the area of task scheduling,optimizing multiple objectives simultaneously remains a significant challenge due to the NP(Non-deterministic Polynomial)Complete nature of the problem.This study aims to address these challenges by providing a comprehensive review and experimental analysis of task scheduling approaches,with a particular focus on hybrid techniques that offer promising solutions.Utilizing the CloudSim simulation toolkit,we evaluated the performance of three hybrid algorithms:Estimation of Distribution Algorithm-Genetic Algorithm(EDA-GA),Hybrid Genetic Algorithm-Ant Colony Optimization(HGA-ACO),and Improved Discrete Particle Swarm Optimization(IDPSO).Our experimental results demonstrate that these hybrid methods significantly outperform traditional standalone algorithms in reducing Makespan,which is a critical measure of task completion time.Notably,the IDPSO algorithm exhibited superior performance,achieving a Makespan of just 0.64 milliseconds for a set of 150 tasks.These findings underscore the potential of hybrid algorithms to enhance task scheduling efficiency in cloud computing environments.This paper concludes with a discussion of the implications of our findings and offers recommendations for future research aimed at further improving task scheduling strategies,particularly in the context of increasingly complex and dynamic cloud environments.展开更多
To enhance the rationality of the layout of electric vehicle charging stations,meet the actual needs of users,and optimise the service range and coverage efficiency of charging stations,this paper proposes an optimisa...To enhance the rationality of the layout of electric vehicle charging stations,meet the actual needs of users,and optimise the service range and coverage efficiency of charging stations,this paper proposes an optimisation strategy for the layout of electric vehicle charging stations that integrates Mini Batch K-Means and simulated annealing algorithms.By constructing a circle-like service area model with the charging station as the centre and a certain distance as the radius,the maximum coverage of electric vehicle charging stations in the region and the influence of different regional environments on charging demand are considered.Based on the real data of electric vehicle charging stations in Nanjing,Jiangsu Province,this paper uses the model proposed in this paper to optimise the layout of charging stations in the study area.The results show that the optimisation strategy incorporating Mini Batch K-Means and simulated annealing algorithms outperforms the existing charging station layouts in terms of coverage and the number of stations served,and compared to the original charging station layouts,the optimised charging station layouts have flatter Lorentzian curves and are closer to the average distribution.The proposed optimisation strategy not only improves the service efficiency and user satisfaction of EV(Electric Vehicle)charging stations but also provides a reference for the layout optimisation of EV charging stations in other cities,which has important practical value and promotion potential.展开更多
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.
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.
基金supported by the National Key Research and Development Program of China (2022YFB3304700)Hunan Province Natural Science Foundation (2022JJ50132,2022JCYJ05 and 2022JCYJ09).
文摘Proportioning is an important part of sintering,as it affects the cost of sintering and the quality of sintered ore.To address the problems posed by the complex raw material information and numerous constraints in the sintering process,a multi-objective optimisation model for sintering proportioning was established,which takes the proportioning cost and TFe as the optimisation objectives.Additionally,an improved multi-objective beluga whale optimisation(IMOBWO)algorithm was proposed to solve the nonlinear,multi-constrained multi-objective optimisation problems.The algorithm uses the con-strained non-dominance criterion to deal with the constraint problem in the model.Moreover,the algorithm employs an opposite learning strategy and a population guidance mechanism based on angular competition and two-population competition strategy to enhance convergence and population diversity.The actual proportioning of a steel plant indicates that the IMOBWO algorithm applied to the ore proportioning process has good convergence and obtains the uniformly distributed Pareto front.Meanwhile,compared with the actual proportioning scheme,the proportioning cost is reduced by 4.3361¥/t,and the TFe content in the mixture is increased by 0.0367%in the optimal compromise solution.Therefore,the proposed method effectively balances the cost and total iron,facilitating the comprehensive utilisation of sintered iron ore resources while ensuring quality assurance.
文摘We evaluate an adaptive optimisation methodology,Bayesian optimisation(BO),for designing a minimum weight explosive reactive armour(ERA)for protection against a surrogate medium calibre kinetic energy(KE)long rod projectile and surrogate shaped charge(SC)warhead.We perform the optimisation using a conventional BO methodology and compare it with a conventional trial-and-error approach from a human expert.A third approach,utilising a novel human-machine teaming framework for BO is also evaluated.Data for the optimisation is generated using numerical simulations that are demonstrated to provide reasonable qualitative agreement with reference experiments.The human-machine teaming methodology is shown to identify the optimum ERA design in the fewest number of evaluations,outperforming both the stand-alone human and stand-alone BO methodologies.From a design space of almost 1800 configurations the human-machine teaming approach identifies the minimum weight ERA design in 10 samples.
基金supported in part by the National Natural Science Foundation of China (62376288,U23A20347)the Engineering and Physical Sciences Research Council of UK (EP/X041239/1)the Royal Society International Exchanges Scheme of UK (IEC/NSFC/211404)。
文摘Decomposition of a complex multi-objective optimisation problem(MOP)to multiple simple subMOPs,known as M2M for short,is an effective approach to multi-objective optimisation.However,M2M facilitates little communication/collaboration between subMOPs,which limits its use in complex optimisation scenarios.This paper extends the M2M framework to develop a unified algorithm for both multi-objective and manyobjective optimisation.Through bilevel decomposition,an MOP is divided into multiple subMOPs at upper level,each of which is further divided into a number of single-objective subproblems at lower level.Neighbouring subMOPs are allowed to share some subproblems so that the knowledge gained from solving one subMOP can be transferred to another,and eventually to all the subMOPs.The bilevel decomposition is readily combined with some new mating selection and population update strategies,leading to a high-performance algorithm that competes effectively against a number of state-of-the-arts studied in this paper for both multiand many-objective optimisation.Parameter analysis and component analysis have been also carried out to further justify the proposed algorithm.
文摘In real-world applications, datasets frequently contain outliers, which can hinder the generalization ability of machine learning models. Bayesian classifiers, a popular supervised learning method, rely on accurate probability density estimation for classifying continuous datasets. However, achieving precise density estimation with datasets containing outliers poses a significant challenge. This paper introduces a Bayesian classifier that utilizes optimized robust kernel density estimation to address this issue. Our proposed method enhances the accuracy of probability density distribution estimation by mitigating the impact of outliers on the training sample’s estimated distribution. Unlike the conventional kernel density estimator, our robust estimator can be seen as a weighted kernel mapping summary for each sample. This kernel mapping performs the inner product in the Hilbert space, allowing the kernel density estimation to be considered the average of the samples’ mapping in the Hilbert space using a reproducing kernel. M-estimation techniques are used to obtain accurate mean values and solve the weights. Meanwhile, complete cross-validation is used as the objective function to search for the optimal bandwidth, which impacts the estimator. The Harris Hawks Optimisation optimizes the objective function to improve the estimation accuracy. The experimental results show that it outperforms other optimization algorithms regarding convergence speed and objective function value during the bandwidth search. The optimal robust kernel density estimator achieves better fitness performance than the traditional kernel density estimator when the training data contains outliers. The Naïve Bayesian with optimal robust kernel density estimation improves the generalization in the classification with outliers.
文摘Cloud computing has rapidly evolved into a critical technology,seamlessly integrating into various aspects of daily life.As user demand for cloud services continues to surge,the need for efficient virtualization and resource management becomes paramount.At the core of this efficiency lies task scheduling,a complex process that determines how tasks are allocated and executed across cloud resources.While extensive research has been conducted in the area of task scheduling,optimizing multiple objectives simultaneously remains a significant challenge due to the NP(Non-deterministic Polynomial)Complete nature of the problem.This study aims to address these challenges by providing a comprehensive review and experimental analysis of task scheduling approaches,with a particular focus on hybrid techniques that offer promising solutions.Utilizing the CloudSim simulation toolkit,we evaluated the performance of three hybrid algorithms:Estimation of Distribution Algorithm-Genetic Algorithm(EDA-GA),Hybrid Genetic Algorithm-Ant Colony Optimization(HGA-ACO),and Improved Discrete Particle Swarm Optimization(IDPSO).Our experimental results demonstrate that these hybrid methods significantly outperform traditional standalone algorithms in reducing Makespan,which is a critical measure of task completion time.Notably,the IDPSO algorithm exhibited superior performance,achieving a Makespan of just 0.64 milliseconds for a set of 150 tasks.These findings underscore the potential of hybrid algorithms to enhance task scheduling efficiency in cloud computing environments.This paper concludes with a discussion of the implications of our findings and offers recommendations for future research aimed at further improving task scheduling strategies,particularly in the context of increasingly complex and dynamic cloud environments.
基金supported by the Jiangsu Provincial College Students Innovation andEntrepreneurship Training Plan Project(grant number 202411276037Z)the Nanjing Institute ofTechnology Fund for Research Startup Projects of Introduced Talents(grant number TB202406012).
文摘To enhance the rationality of the layout of electric vehicle charging stations,meet the actual needs of users,and optimise the service range and coverage efficiency of charging stations,this paper proposes an optimisation strategy for the layout of electric vehicle charging stations that integrates Mini Batch K-Means and simulated annealing algorithms.By constructing a circle-like service area model with the charging station as the centre and a certain distance as the radius,the maximum coverage of electric vehicle charging stations in the region and the influence of different regional environments on charging demand are considered.Based on the real data of electric vehicle charging stations in Nanjing,Jiangsu Province,this paper uses the model proposed in this paper to optimise the layout of charging stations in the study area.The results show that the optimisation strategy incorporating Mini Batch K-Means and simulated annealing algorithms outperforms the existing charging station layouts in terms of coverage and the number of stations served,and compared to the original charging station layouts,the optimised charging station layouts have flatter Lorentzian curves and are closer to the average distribution.The proposed optimisation strategy not only improves the service efficiency and user satisfaction of EV(Electric Vehicle)charging stations but also provides a reference for the layout optimisation of EV charging stations in other cities,which has important practical value and promotion potential.
