To obtain a suitable scheduling scheme in an effective time range,the minimum completion time is taken as the objective of Flexible Job Shop scheduling Problems(FJSP)with different scales,and Composite Dispatching Rul...To obtain a suitable scheduling scheme in an effective time range,the minimum completion time is taken as the objective of Flexible Job Shop scheduling Problems(FJSP)with different scales,and Composite Dispatching Rules(CDRs)are applied to generate feasible solutions.Firstly,the binary tree coding method is adopted,and the constructed function set is normalized.Secondly,a CDR mining approach based on an Improved Genetic Programming Algorithm(IGPA)is designed.Two population initialization methods are introduced to enrich the initial population,and a superior and inferior population separation strategy is designed to improve the global search ability of the algorithm.At the same time,two individual mutation methods are introduced to improve the algorithm’s local search ability,to achieve the balance between global search and local search.In addition,the effectiveness of the IGPA and the superiority of CDRs are verified through comparative analysis.Finally,Deep Reinforcement Learning(DRL)is employed to solve the FJSP by incorporating the CDRs as the action set,the selection times are counted to further verify the superiority of CDRs.展开更多
The association between miRNA and disease has attracted more and more attention.Until now,existing methods for identifying miRNA related disease mainly rely on top-ranked association model,which may not provide a full...The association between miRNA and disease has attracted more and more attention.Until now,existing methods for identifying miRNA related disease mainly rely on top-ranked association model,which may not provide a full landscape of association between miRNA and disease.Hence there is strong need of new computational method to identify the associations from miRNA group view.In this paper,we proposed a framework,MDA-TOEPGA,to identify miRNAdisease association based on two-objective evolutionary programming genetic algorithm,which identifies latent miRNAdisease associations from the view of functional module.To understand the miRNA functional module in diseases,the case study is presented.We have been compared MDA-TOEPGA with several state-of-the-art functional module algorithm.Experimental results showed that our method cannot only outperform classical algorithms,such as K-means,IK-means,MCODE,HC-PIN,and ClusterONE,but can also achieve an ideal overall performance in terms of a composite score consisting of f1,Sensitivity,and Accuracy.Altogether,our study showed that MDA-TOEPGA is a promising method to investigate miRNA-disease association from the landscapes of functional module.展开更多
Optimal path planning involves finding a feasible state sequence between a start and a goal that optimizes an objective.This process relies on heuristic functions to guide the search direction.While a robust function ...Optimal path planning involves finding a feasible state sequence between a start and a goal that optimizes an objective.This process relies on heuristic functions to guide the search direction.While a robust function can improve search efficiency and solution quality,current methods often overlook available environmental data and simplify the function structure due to the complexity of information relationships.This study introduces Genetic Informed Trees(GIT^(*)),which improves upon Effort Informed Trees(EIT^(*))by integrating a wider array of environmental data,such as repulsive forces from obstacles and the dynamic importance of vertices,to refine heuristic functions for better guidance.Furthermore,we integrated reinforced genetic programming(RGP),which combines genetic programming with reward system feedback to mutate genotype-generative heuristic functions for GIT^(*).RGP leverages a multitude of data types,thereby improving computational efficiency and solution quality within a set timeframe.Comparative analyses demonstrate that GIT^(*)surpasses existing single-query.sampling-based planners in problems ranging from R^(4)to R^(16)and was tested on a real-world mobile manipulation task.展开更多
This research develops a comprehensive method to solve a combinatorial problem consisting of distribution system reconfiguration, capacitor allocation, and renewable energy resources sizing and siting simultaneously a...This research develops a comprehensive method to solve a combinatorial problem consisting of distribution system reconfiguration, capacitor allocation, and renewable energy resources sizing and siting simultaneously and to improve power system's accountability and system performance parameters. Due to finding solution which is closer to realistic characteristics, load forecasting, market price errors and the uncertainties related to the variable output power of wind based DG units are put in consideration. This work employs NSGA-II accompanied by the fuzzy set theory to solve the aforementioned multi-objective problem. The proposed scheme finally leads to a solution with a minimum voltage deviation, a maximum voltage stability, lower amount of pollutant and lower cost. The cost includes the installation costs of new equipment, reconfiguration costs, power loss cost, reliability cost, cost of energy purchased from power market, upgrade costs of lines and operation and maintenance costs of DGs. Therefore, the proposed methodology improves power quality, reliability and security in lower costs besides its preserve, with the operational indices of power distribution networks in acceptable level. To validate the proposed methodology's usefulness, it was applied on the IEEE 33-bus distribution system then the outcomes were compared with initial configuration.展开更多
The primary focus of this paper is to design a progressive restoration plan for an enterprise data center environment following a partial or full disruption. Repairing and restoring disrupted components in an enterpri...The primary focus of this paper is to design a progressive restoration plan for an enterprise data center environment following a partial or full disruption. Repairing and restoring disrupted components in an enterprise data center requires a significant amount of time and human effort. Following a major disruption, the recovery process involves multiple stages, and during each stage, the partially recovered infrastructures can provide limited services to users at some degraded service level. However, how fast and efficiently an enterprise infrastructure can be recovered de- pends on how the recovery mechanism restores the disrupted components, considering the inter-dependencies between services, along with the limitations of expert human operators. The entire problem turns out to be NP- hard and rather complex, and we devise an efficient meta-heuristic to solve the problem. By considering some real-world examples, we show that the proposed meta-heuristic provides very accurate results, and still runs 600-2800 times faster than the optimal solution obtained from a general purpose mathematical solver [1].展开更多
In recent years, energy-retrofitting is becoming an imperative aim for existing buildings worldwide and increased interest has focused on the development of nanoparticle blended concretes with adequate mechanical...In recent years, energy-retrofitting is becoming an imperative aim for existing buildings worldwide and increased interest has focused on the development of nanoparticle blended concretes with adequate mechanical properties and durability performance, through the optimization of concrete permeability and the incorporation of the proper nanoparticle type in the concrete matrix. In order to investigate the potential use of nanocomposites as dense barriers against the permeation of liquids into the concrete, three types of nanoparticles including Zinc Oxide (ZnO), Magnesium Oxide (MgO), and composite nanoparticles were used in the present study as partial replacement of cement. Besides, the effect of adding these nanoparticles on both pore structure and mechanical strengths of the concrete at different ages was determined, and scanning electron microscopy (SEM) images were then used to illustrate the uniformity dispersion of nanoparticles in cement paste. It was demonstrated that the addition of a small number of nanoparticles effectively enhances the mechanical properties of concrete and consequently reduces the extent of the water permeation front. Finally, the behavioral models using Genetic Algorithm (GA) programming were developed to describe the time-dependent behavioral characteristics of nanoparticle blended concrete samples in various compressive and tensile stress states at different ages.展开更多
This paper proposes a hybrid forecasting method to forecast container throughput of Qingdao Port.To eliminate the influence of outliers,local outlier factor(lof) is extended to detect outliers in time series,and then ...This paper proposes a hybrid forecasting method to forecast container throughput of Qingdao Port.To eliminate the influence of outliers,local outlier factor(lof) is extended to detect outliers in time series,and then different dummy variables are constructed to capture the effect of outliers based on domain knowledge.Next,a hybrid forecasting model combining projection pursuit regression(PPR) and genetic programming(GP) algorithm is proposed.Finally,the hybrid model is applied to forecasting container throughput of Qingdao Port and the results show that the proposed method significantly outperforms ANN,SARIMA,and PPR models.展开更多
Accurate estimation of the drag forces generated by vegetation stems is crucial for the comprehensive assessment of the impact of aquatic vegetation on hydrodynamic processes in aquatic environments.The coupling relat...Accurate estimation of the drag forces generated by vegetation stems is crucial for the comprehensive assessment of the impact of aquatic vegetation on hydrodynamic processes in aquatic environments.The coupling relationship between vegetation layer flow velocity and vegetation drag makes precise prediction of submerged vegetation drag forces particularly challenging.The present study utilized published data on submerged vegetation drag force measurements and employed a genetic programming(GP)algorithm,a machine learning technique,to establish the connection between submerged vegetation drag forces and flow and vegetation parameters.When using the bulk velocity,U,as the reference velocity scale to define the drag coefficient,C_(d),and stem Reynolds number,the GP runs revealed that the drag coefficient of submerged vegetation is related to submergence ratio(H^(*)),aspect ratio(d^(*)),blockage ratio(ψ^(*)),and vegetation density(λ).The relation between vegetation stem drag forces and flow velocity is implicitly embedded in the definition of C_(d).Comparisons with experimental drag force measurements indicate that using the bulk velocity as the reference velocity,as opposed to using the vegetation layer average velocity,U_(v),eliminates the need for complex iterative processes to estimate U_(v)and avoids introducing additional errors associated with U_(v)estimation.This approach significantly enhances the model’s predictive capabilities and results in a simpler and more user-friendly formula expression.展开更多
基金supported by the National Natural Science Foundation of China(Nos.51805152 and 52075401)the Green Industry Technology Leading Program of Hubei University of Technology(No.XJ2021005001)+1 种基金the Scientific Research Foundation for High-level Talents of Hubei University of Technology(No.GCRC2020009)the Natural Science Foundation of Hubei Province(No.2022CFB445).
文摘To obtain a suitable scheduling scheme in an effective time range,the minimum completion time is taken as the objective of Flexible Job Shop scheduling Problems(FJSP)with different scales,and Composite Dispatching Rules(CDRs)are applied to generate feasible solutions.Firstly,the binary tree coding method is adopted,and the constructed function set is normalized.Secondly,a CDR mining approach based on an Improved Genetic Programming Algorithm(IGPA)is designed.Two population initialization methods are introduced to enrich the initial population,and a superior and inferior population separation strategy is designed to improve the global search ability of the algorithm.At the same time,two individual mutation methods are introduced to improve the algorithm’s local search ability,to achieve the balance between global search and local search.In addition,the effectiveness of the IGPA and the superiority of CDRs are verified through comparative analysis.Finally,Deep Reinforcement Learning(DRL)is employed to solve the FJSP by incorporating the CDRs as the action set,the selection times are counted to further verify the superiority of CDRs.
基金This work was supported in part by the National Natural Science Foundation of China under Grants 61873089,62032007the Key Project of the Education Department of Hunan Province under Grant 20A087the Innovation Platform Open Fund Project of Hunan Provincial Education Department under Grant 20K025.
文摘The association between miRNA and disease has attracted more and more attention.Until now,existing methods for identifying miRNA related disease mainly rely on top-ranked association model,which may not provide a full landscape of association between miRNA and disease.Hence there is strong need of new computational method to identify the associations from miRNA group view.In this paper,we proposed a framework,MDA-TOEPGA,to identify miRNAdisease association based on two-objective evolutionary programming genetic algorithm,which identifies latent miRNAdisease associations from the view of functional module.To understand the miRNA functional module in diseases,the case study is presented.We have been compared MDA-TOEPGA with several state-of-the-art functional module algorithm.Experimental results showed that our method cannot only outperform classical algorithms,such as K-means,IK-means,MCODE,HC-PIN,and ClusterONE,but can also achieve an ideal overall performance in terms of a composite score consisting of f1,Sensitivity,and Accuracy.Altogether,our study showed that MDA-TOEPGA is a promising method to investigate miRNA-disease association from the landscapes of functional module.
文摘Optimal path planning involves finding a feasible state sequence between a start and a goal that optimizes an objective.This process relies on heuristic functions to guide the search direction.While a robust function can improve search efficiency and solution quality,current methods often overlook available environmental data and simplify the function structure due to the complexity of information relationships.This study introduces Genetic Informed Trees(GIT^(*)),which improves upon Effort Informed Trees(EIT^(*))by integrating a wider array of environmental data,such as repulsive forces from obstacles and the dynamic importance of vertices,to refine heuristic functions for better guidance.Furthermore,we integrated reinforced genetic programming(RGP),which combines genetic programming with reward system feedback to mutate genotype-generative heuristic functions for GIT^(*).RGP leverages a multitude of data types,thereby improving computational efficiency and solution quality within a set timeframe.Comparative analyses demonstrate that GIT^(*)surpasses existing single-query.sampling-based planners in problems ranging from R^(4)to R^(16)and was tested on a real-world mobile manipulation task.
文摘This research develops a comprehensive method to solve a combinatorial problem consisting of distribution system reconfiguration, capacitor allocation, and renewable energy resources sizing and siting simultaneously and to improve power system's accountability and system performance parameters. Due to finding solution which is closer to realistic characteristics, load forecasting, market price errors and the uncertainties related to the variable output power of wind based DG units are put in consideration. This work employs NSGA-II accompanied by the fuzzy set theory to solve the aforementioned multi-objective problem. The proposed scheme finally leads to a solution with a minimum voltage deviation, a maximum voltage stability, lower amount of pollutant and lower cost. The cost includes the installation costs of new equipment, reconfiguration costs, power loss cost, reliability cost, cost of energy purchased from power market, upgrade costs of lines and operation and maintenance costs of DGs. Therefore, the proposed methodology improves power quality, reliability and security in lower costs besides its preserve, with the operational indices of power distribution networks in acceptable level. To validate the proposed methodology's usefulness, it was applied on the IEEE 33-bus distribution system then the outcomes were compared with initial configuration.
文摘The primary focus of this paper is to design a progressive restoration plan for an enterprise data center environment following a partial or full disruption. Repairing and restoring disrupted components in an enterprise data center requires a significant amount of time and human effort. Following a major disruption, the recovery process involves multiple stages, and during each stage, the partially recovered infrastructures can provide limited services to users at some degraded service level. However, how fast and efficiently an enterprise infrastructure can be recovered de- pends on how the recovery mechanism restores the disrupted components, considering the inter-dependencies between services, along with the limitations of expert human operators. The entire problem turns out to be NP- hard and rather complex, and we devise an efficient meta-heuristic to solve the problem. By considering some real-world examples, we show that the proposed meta-heuristic provides very accurate results, and still runs 600-2800 times faster than the optimal solution obtained from a general purpose mathematical solver [1].
文摘In recent years, energy-retrofitting is becoming an imperative aim for existing buildings worldwide and increased interest has focused on the development of nanoparticle blended concretes with adequate mechanical properties and durability performance, through the optimization of concrete permeability and the incorporation of the proper nanoparticle type in the concrete matrix. In order to investigate the potential use of nanocomposites as dense barriers against the permeation of liquids into the concrete, three types of nanoparticles including Zinc Oxide (ZnO), Magnesium Oxide (MgO), and composite nanoparticles were used in the present study as partial replacement of cement. Besides, the effect of adding these nanoparticles on both pore structure and mechanical strengths of the concrete at different ages was determined, and scanning electron microscopy (SEM) images were then used to illustrate the uniformity dispersion of nanoparticles in cement paste. It was demonstrated that the addition of a small number of nanoparticles effectively enhances the mechanical properties of concrete and consequently reduces the extent of the water permeation front. Finally, the behavioral models using Genetic Algorithm (GA) programming were developed to describe the time-dependent behavioral characteristics of nanoparticle blended concrete samples in various compressive and tensile stress states at different ages.
文摘This paper proposes a hybrid forecasting method to forecast container throughput of Qingdao Port.To eliminate the influence of outliers,local outlier factor(lof) is extended to detect outliers in time series,and then different dummy variables are constructed to capture the effect of outliers based on domain knowledge.Next,a hybrid forecasting model combining projection pursuit regression(PPR) and genetic programming(GP) algorithm is proposed.Finally,the hybrid model is applied to forecasting container throughput of Qingdao Port and the results show that the proposed method significantly outperforms ANN,SARIMA,and PPR models.
基金supported by the National Key Research and Development Program of China(Grant No.2022YFC3202601)the National Natural Science Foundation of China(Grant No.52309088)+1 种基金the China Postdoctoral Science Foundation(Grant No.2023M730932)the Jiangsu Funding Program for Excellent Postdoctoral Talent(Grant No.2023ZB608).
文摘Accurate estimation of the drag forces generated by vegetation stems is crucial for the comprehensive assessment of the impact of aquatic vegetation on hydrodynamic processes in aquatic environments.The coupling relationship between vegetation layer flow velocity and vegetation drag makes precise prediction of submerged vegetation drag forces particularly challenging.The present study utilized published data on submerged vegetation drag force measurements and employed a genetic programming(GP)algorithm,a machine learning technique,to establish the connection between submerged vegetation drag forces and flow and vegetation parameters.When using the bulk velocity,U,as the reference velocity scale to define the drag coefficient,C_(d),and stem Reynolds number,the GP runs revealed that the drag coefficient of submerged vegetation is related to submergence ratio(H^(*)),aspect ratio(d^(*)),blockage ratio(ψ^(*)),and vegetation density(λ).The relation between vegetation stem drag forces and flow velocity is implicitly embedded in the definition of C_(d).Comparisons with experimental drag force measurements indicate that using the bulk velocity as the reference velocity,as opposed to using the vegetation layer average velocity,U_(v),eliminates the need for complex iterative processes to estimate U_(v)and avoids introducing additional errors associated with U_(v)estimation.This approach significantly enhances the model’s predictive capabilities and results in a simpler and more user-friendly formula expression.
