The dynamic traveling salesman problem(DTSP)is significant in logistics distribution in real-world applications in smart cities,but it is uncertain and difficult to solve.This paper proposes a scheme library-based ant...The dynamic traveling salesman problem(DTSP)is significant in logistics distribution in real-world applications in smart cities,but it is uncertain and difficult to solve.This paper proposes a scheme library-based ant colony optimization(ACO)with a two-optimization(2-opt)strategy to solve the DTSP efficiently.The work is novel and contributes to three aspects:problemmodel,optimization framework,and algorithmdesign.Firstly,in the problem model,traditional DTSP models often consider the change of travel distance between two nodes over time,while this paper focuses on a special DTSP model in that the node locations change dynamically over time.Secondly,in the optimization framework,the ACO algorithm is carried out in an offline optimization and online application framework to efficiently reuse the historical information to help fast respond to the dynamic environment.The framework of offline optimization and online application is proposed due to the fact that the environmental change inDTSPis caused by the change of node location,and therefore the newenvironment is somehowsimilar to certain previous environments.This way,in the offline optimization,the solutions for possible environmental changes are optimized in advance,and are stored in a mode scheme library.In the online application,when an environmental change is detected,the candidate solutions stored in the mode scheme library are reused via ACO to improve search efficiency and reduce computational complexity.Thirdly,in the algorithm design,the ACO cooperates with the 2-opt strategy to enhance search efficiency.To evaluate the performance of ACO with 2-opt,we design two challenging DTSP cases with up to 200 and 1379 nodes and compare them with other ACO and genetic algorithms.The experimental results show that ACO with 2-opt can solve the DTSPs effectively.展开更多
Morphing aircraft can meet requirements of multi-mission during the whole flight due to changing the aerodynamic shape,so it is necessary to study itsmorphing rules along the trajectory.However,trajectory planning con...Morphing aircraft can meet requirements of multi-mission during the whole flight due to changing the aerodynamic shape,so it is necessary to study itsmorphing rules along the trajectory.However,trajectory planning considering morphing variables requires a huge number of expensive CFD computations due to the morphing in view of aerodynamic performance.Under the given missions and trajectory,to alleviate computational cost and improve trajectory-planning efficiency formorphing aircraft,an offline optimizationmethod is proposed based onMulti-Fidelity Kriging(MFK)modeling.The angle of attack,Mach number,sweep angle and axial position of the morphing wing are defined as variables for generating training data for building the MFK models,in which many inviscid aerodynamic solutions are used as low-fidelity data,while the less high-fidelity data are obtained by solving viscous flow.Then the built MFK models of the lift,drag and pressure centre at the different angles of attack andMach numbers are used to predict the aerodynamic performance of the morphing aircraft,which keeps the optimal sweep angle and axial position of the wing during trajectory planning.Hence,themorphing rules can be correspondingly acquired along the trajectory,aswell as keep the aircraftwith the best aerodynamic performance during thewhole task.The trajectory planning of amorphing aircraft was performed with the optimal aerodynamic performance based on the MFK models,built by only using 240 low-fidelity data and 110 high-fidelity data.The results indicate that a complex trajectory can take advantage of morphing rules in keeping good aerodynamic performance,and the proposed method is more efficient than trajectory optimization by reducing 86%of the computing time.展开更多
A risk assessment based adaptive ultra-short-term wind power prediction(USTWPP)method is proposed in this paper.In this method,features are first extracted from the historical data,and then each wind power time series...A risk assessment based adaptive ultra-short-term wind power prediction(USTWPP)method is proposed in this paper.In this method,features are first extracted from the historical data,and then each wind power time series(WPTS)is split into several subsets defined by their stationary patterns.A WPTS that does not match any of the stationary patterns is then included in a subset of non-stationary patterns.Each WPTS subset is then related to a USTWPP model that is specially selected and optimized offline based on the proposed risk assessment index.For online applications,the pattern of the last short WPTS is first recognized,and the relevant prediction model is then applied for USTWPP.Experimental results confirm the efficacy of the proposed method.展开更多
基金supported in part by the National Research Foundation of Korea (NRF-2021H1D3A2A01082705).
文摘The dynamic traveling salesman problem(DTSP)is significant in logistics distribution in real-world applications in smart cities,but it is uncertain and difficult to solve.This paper proposes a scheme library-based ant colony optimization(ACO)with a two-optimization(2-opt)strategy to solve the DTSP efficiently.The work is novel and contributes to three aspects:problemmodel,optimization framework,and algorithmdesign.Firstly,in the problem model,traditional DTSP models often consider the change of travel distance between two nodes over time,while this paper focuses on a special DTSP model in that the node locations change dynamically over time.Secondly,in the optimization framework,the ACO algorithm is carried out in an offline optimization and online application framework to efficiently reuse the historical information to help fast respond to the dynamic environment.The framework of offline optimization and online application is proposed due to the fact that the environmental change inDTSPis caused by the change of node location,and therefore the newenvironment is somehowsimilar to certain previous environments.This way,in the offline optimization,the solutions for possible environmental changes are optimized in advance,and are stored in a mode scheme library.In the online application,when an environmental change is detected,the candidate solutions stored in the mode scheme library are reused via ACO to improve search efficiency and reduce computational complexity.Thirdly,in the algorithm design,the ACO cooperates with the 2-opt strategy to enhance search efficiency.To evaluate the performance of ACO with 2-opt,we design two challenging DTSP cases with up to 200 and 1379 nodes and compare them with other ACO and genetic algorithms.The experimental results show that ACO with 2-opt can solve the DTSPs effectively.
基金This study was co-supported by the National Defense Fundamental Research Funds of China(No.JCKY2016204B102 and JCKY2016208C001).
文摘Morphing aircraft can meet requirements of multi-mission during the whole flight due to changing the aerodynamic shape,so it is necessary to study itsmorphing rules along the trajectory.However,trajectory planning considering morphing variables requires a huge number of expensive CFD computations due to the morphing in view of aerodynamic performance.Under the given missions and trajectory,to alleviate computational cost and improve trajectory-planning efficiency formorphing aircraft,an offline optimizationmethod is proposed based onMulti-Fidelity Kriging(MFK)modeling.The angle of attack,Mach number,sweep angle and axial position of the morphing wing are defined as variables for generating training data for building the MFK models,in which many inviscid aerodynamic solutions are used as low-fidelity data,while the less high-fidelity data are obtained by solving viscous flow.Then the built MFK models of the lift,drag and pressure centre at the different angles of attack andMach numbers are used to predict the aerodynamic performance of the morphing aircraft,which keeps the optimal sweep angle and axial position of the wing during trajectory planning.Hence,themorphing rules can be correspondingly acquired along the trajectory,aswell as keep the aircraftwith the best aerodynamic performance during thewhole task.The trajectory planning of amorphing aircraft was performed with the optimal aerodynamic performance based on the MFK models,built by only using 240 low-fidelity data and 110 high-fidelity data.The results indicate that a complex trajectory can take advantage of morphing rules in keeping good aerodynamic performance,and the proposed method is more efficient than trajectory optimization by reducing 86%of the computing time.
基金supported in part by Special Fund of the National Basic Research Program of China(2013CB228204)NSFCNRCT Collaborative Project(No.51561145011)+1 种基金Australian Research Council Project(DP120101345)State Grid Corporation of China.
文摘A risk assessment based adaptive ultra-short-term wind power prediction(USTWPP)method is proposed in this paper.In this method,features are first extracted from the historical data,and then each wind power time series(WPTS)is split into several subsets defined by their stationary patterns.A WPTS that does not match any of the stationary patterns is then included in a subset of non-stationary patterns.Each WPTS subset is then related to a USTWPP model that is specially selected and optimized offline based on the proposed risk assessment index.For online applications,the pattern of the last short WPTS is first recognized,and the relevant prediction model is then applied for USTWPP.Experimental results confirm the efficacy of the proposed method.
