In the calibration of hydrological models, evaluation criteria are explicitly and quantitatively defined as single-or multi-objective functions when utilizing automatic calibration approaches.In most previous studies,...In the calibration of hydrological models, evaluation criteria are explicitly and quantitatively defined as single-or multi-objective functions when utilizing automatic calibration approaches.In most previous studies, there is a general opinion that no single-objective function can represent all important characteristics of even one specific hydrological variable(e.g., streamflow).Thus hydrologists must turn to multi-objective calibration.In this study, we demonstrated that an optimized single-objective function can compromise multi-response modes(i.e., multi-objective functions) of the hydrograph, which is defined as summation of a power function of the absolute error between observed and simulated streamflow with the exponent of power function optimized for specific watersheds.The new objective function was applied to 196 model parameter estimation experiment(MOPEX) watersheds across the eastern United States using the semi-distributed Xinanjiang hydrological model.The optimized exponent value for each watershed was obtained by targeting four popular objective functions focusing on peak flows, low flows, water balance, and flashiness, respectively.Results showed that the optimized single-objective function can achieve a better hydrograph simulation compared to the traditional single-objective function Nash-Sutcliffe efficiency coefficient for most watersheds, and balance high flow part and low flow part of the hydrograph without substantial differences compared to multi-objective calibration.The proposed optimal single-objective function can be practically adopted in the hydrological modeling if the optimal exponent value could be determined a priori according to hydrological/climatic/landscape characteristics in a specific watershed.展开更多
In this paper, we present an algorithm to solve the inequality constrained multi-objective programming (MP) by using a penalty function with objective parameters and constraint penalty parameter. First, the penalty fu...In this paper, we present an algorithm to solve the inequality constrained multi-objective programming (MP) by using a penalty function with objective parameters and constraint penalty parameter. First, the penalty function with objective parameters and constraint penalty parameter for MP and the corresponding unconstraint penalty optimization problem (UPOP) is defined. Under some conditions, a Pareto efficient solution (or a weakly-efficient solution) to UPOP is proved to be a Pareto efficient solution (or a weakly-efficient solution) to MP. The penalty function is proved to be exact under a stable condition. Then, we design an algorithm to solve MP and prove its convergence. Finally, numerical examples show that the algorithm may help decision makers to find a satisfactory solution to MP.展开更多
This paper deals with the optimality conditions and dual theory of multi-objective programming problems involving generalized convexity. New classes of generalized type-I functions are introduced for arcwise connected...This paper deals with the optimality conditions and dual theory of multi-objective programming problems involving generalized convexity. New classes of generalized type-I functions are introduced for arcwise connected functions, and examples are given to show the existence of these functions. By utilizing the new concepts, several sufficient optimality conditions and Mond-Weir type duality results are proposed for non-differentiable multi-objective programming problem.展开更多
To assist readers to have a comprehensive understanding, the classical and intelligent methods roundly based on precursory research achievements are summarized in this paper. First, basic conception and description ab...To assist readers to have a comprehensive understanding, the classical and intelligent methods roundly based on precursory research achievements are summarized in this paper. First, basic conception and description about multi-objective (MO) optimization are introduced. Then some definitions and related terminologies are given. Furthermore several MO optimization methods including classical and current intelligent methods are discussed one by one succinctly. Finally evaluations on advantages and disadvantages about these methods are made at the end of the paper.展开更多
This paper states a new metaheuristic based on Deterministic Finite Automata （DFA） for the multi - objective optimization of combinatorial problems. First, a new DFA named Multi - Objective Deterministic Finite Auto...This paper states a new metaheuristic based on Deterministic Finite Automata （DFA） for the multi - objective optimization of combinatorial problems. First, a new DFA named Multi - Objective Deterministic Finite Automata （MDFA） is defined. MDFA allows the representation of the feasible solutions space of combinatorial problems. Second, it is defined and implemented a metaheuritic based on MDFA theory. It is named Metaheuristic of Deterministic Swapping （MODS）. MODS is a local search strategy that works using a MDFA. Due to this, MODS never take into account unfeasible solutions. Hence, it is not necessary to verify the problem constraints for a new solution found. Lastly, MODS is tested using well know instances of the Bi-Objective Traveling Salesman Problem （TSP） from TSPLIB. Its results were compared with eight Ant Colony inspired algorithms and two Genetic algorithms taken from the specialized literature. The comparison was made using metrics such as Spacing, Generational Distance, Inverse Generational Distance and No-Dominated Generation Vectors. In every case, the MODS results on the metrics were always better and in some of those cases, the superiority was 100%.展开更多
Considering research on multi-objective optimization for reliability and performance suffering cost constraints in digital circuits,an improved multi-objective optimization algorithm based on performance and reliabili...Considering research on multi-objective optimization for reliability and performance suffering cost constraints in digital circuits,an improved multi-objective optimization algorithm based on performance and reliability was proposed to solve the problem of discrete system resources configuration in this paper. This algorithm used the particle-swarm optimization( PSO) to evaluate the tradeoffs configuration of the system resources between reliability and performance and proved the feasibility through the simulation.Finally, the information of resources configuration from optimization algorithm was used to effectively guide the system design so as to mitigate soft errors caused by single event effect( SEE).展开更多
Dear Editor,This letter investigates predefined-time optimization problems(OPs) of multi-agent systems(MASs), where the agent of MASs is subject to inequality constraints, and the team objective function accounts for ...Dear Editor,This letter investigates predefined-time optimization problems(OPs) of multi-agent systems(MASs), where the agent of MASs is subject to inequality constraints, and the team objective function accounts for impulse effects. Firstly, to address the inequality constraints,the penalty method is introduced. Then, a novel optimization strategy is developed, which only requires that the team objective function be strongly convex.展开更多
In the video-based surveillance application, moving shadows can affect the correct localization and detection of moving objects. This paper aims to present a method for shadow detection and suppression used for moving...In the video-based surveillance application, moving shadows can affect the correct localization and detection of moving objects. This paper aims to present a method for shadow detection and suppression used for moving visual object detection. The major novelty of the shadow suppression is the integration of several features including photometric invariant color feature, motion edge feature, and spatial feature etc. By modifying process for false shadow detected, the averaging detection rate of moving object reaches above 90% in the test of Hall-Monitor sequence.展开更多
To meet the development trend of multi-bar warp knit-ting machine towards high-speed,advanced technologyand computer control and the requirements of variousproducts with small quantity,there are many researcheson the ...To meet the development trend of multi-bar warp knit-ting machine towards high-speed,advanced technologyand computer control and the requirements of variousproducts with small quantity,there are many researcheson the computer-aided pattern design of multi-barwarp knitted fabrics.In terms of the special propertiesof the computer-aided pattern design of multi-barwarp knitted fabrics,the Object Oriented Program(OOP)programming-Object Windows class Library(OWL)programming is selected.According to thecharacters of the OWL programming,various functionsare defined.Pattern design and technical parameters canbe output,which offers a great convenience for the fac-tory.展开更多
Interval prediction of wind power,which features the upper and lower limits of wind power at a given confidence level,plays a significant role in accurate prediction and stability of the power grid integrated with win...Interval prediction of wind power,which features the upper and lower limits of wind power at a given confidence level,plays a significant role in accurate prediction and stability of the power grid integrated with wind power.However,the conventional methods of interval prediction are commonly based on a hypothetic probability distribution function,which neglects the correlations among various variables,leading to the decrease of prediction accuracy.Therefore,we improve the multi-objective interval prediction based on the conditional copula function,through which we can fully utilize the correlations among variables to improve prediction accuracy without an assumed probability distribution function.We use the multi-objective optimization method of nondominated sorting genetic algorithm-II(NSGA-II)to obtain the optimal solution set.The particular best solution is weighted by the prediction interval average width(PIAW)and prediction interval coverage probability(PICP)to pick the optimized solution in practical examples.Finally,we apply the proposed method to three wind power plants in different cities in China as examples forvalidation and obtain higher prediction accuracy compared with other methods,i.e.,relevance vector machine(RVM),artificial neural network(ANN),and particle swarm optimization kernel extreme learning machine(PSO-KELM).These results demonstrate the superiority and practicability of this method in interval prediction of wind power.展开更多
基金Under the auspices of National Key Research and Development Program of China(No.2016YFC0402701)National Natural Science Foundation of China(No.51825902)
文摘In the calibration of hydrological models, evaluation criteria are explicitly and quantitatively defined as single-or multi-objective functions when utilizing automatic calibration approaches.In most previous studies, there is a general opinion that no single-objective function can represent all important characteristics of even one specific hydrological variable(e.g., streamflow).Thus hydrologists must turn to multi-objective calibration.In this study, we demonstrated that an optimized single-objective function can compromise multi-response modes(i.e., multi-objective functions) of the hydrograph, which is defined as summation of a power function of the absolute error between observed and simulated streamflow with the exponent of power function optimized for specific watersheds.The new objective function was applied to 196 model parameter estimation experiment(MOPEX) watersheds across the eastern United States using the semi-distributed Xinanjiang hydrological model.The optimized exponent value for each watershed was obtained by targeting four popular objective functions focusing on peak flows, low flows, water balance, and flashiness, respectively.Results showed that the optimized single-objective function can achieve a better hydrograph simulation compared to the traditional single-objective function Nash-Sutcliffe efficiency coefficient for most watersheds, and balance high flow part and low flow part of the hydrograph without substantial differences compared to multi-objective calibration.The proposed optimal single-objective function can be practically adopted in the hydrological modeling if the optimal exponent value could be determined a priori according to hydrological/climatic/landscape characteristics in a specific watershed.
文摘In this paper, we present an algorithm to solve the inequality constrained multi-objective programming (MP) by using a penalty function with objective parameters and constraint penalty parameter. First, the penalty function with objective parameters and constraint penalty parameter for MP and the corresponding unconstraint penalty optimization problem (UPOP) is defined. Under some conditions, a Pareto efficient solution (or a weakly-efficient solution) to UPOP is proved to be a Pareto efficient solution (or a weakly-efficient solution) to MP. The penalty function is proved to be exact under a stable condition. Then, we design an algorithm to solve MP and prove its convergence. Finally, numerical examples show that the algorithm may help decision makers to find a satisfactory solution to MP.
文摘This paper deals with the optimality conditions and dual theory of multi-objective programming problems involving generalized convexity. New classes of generalized type-I functions are introduced for arcwise connected functions, and examples are given to show the existence of these functions. By utilizing the new concepts, several sufficient optimality conditions and Mond-Weir type duality results are proposed for non-differentiable multi-objective programming problem.
文摘To assist readers to have a comprehensive understanding, the classical and intelligent methods roundly based on precursory research achievements are summarized in this paper. First, basic conception and description about multi-objective (MO) optimization are introduced. Then some definitions and related terminologies are given. Furthermore several MO optimization methods including classical and current intelligent methods are discussed one by one succinctly. Finally evaluations on advantages and disadvantages about these methods are made at the end of the paper.
文摘This paper states a new metaheuristic based on Deterministic Finite Automata （DFA） for the multi - objective optimization of combinatorial problems. First, a new DFA named Multi - Objective Deterministic Finite Automata （MDFA） is defined. MDFA allows the representation of the feasible solutions space of combinatorial problems. Second, it is defined and implemented a metaheuritic based on MDFA theory. It is named Metaheuristic of Deterministic Swapping （MODS）. MODS is a local search strategy that works using a MDFA. Due to this, MODS never take into account unfeasible solutions. Hence, it is not necessary to verify the problem constraints for a new solution found. Lastly, MODS is tested using well know instances of the Bi-Objective Traveling Salesman Problem （TSP） from TSPLIB. Its results were compared with eight Ant Colony inspired algorithms and two Genetic algorithms taken from the specialized literature. The comparison was made using metrics such as Spacing, Generational Distance, Inverse Generational Distance and No-Dominated Generation Vectors. In every case, the MODS results on the metrics were always better and in some of those cases, the superiority was 100%.
文摘Considering research on multi-objective optimization for reliability and performance suffering cost constraints in digital circuits,an improved multi-objective optimization algorithm based on performance and reliability was proposed to solve the problem of discrete system resources configuration in this paper. This algorithm used the particle-swarm optimization( PSO) to evaluate the tradeoffs configuration of the system resources between reliability and performance and proved the feasibility through the simulation.Finally, the information of resources configuration from optimization algorithm was used to effectively guide the system design so as to mitigate soft errors caused by single event effect( SEE).
基金supported in part by the National Natural Science Foundation of China(62276119)the Natural Science Foundation of Jiangsu Province(BK20241764)the Postgraduate Research & Practice Innovation Program of Jiangsu Province(KYCX22_2860)
文摘Dear Editor,This letter investigates predefined-time optimization problems(OPs) of multi-agent systems(MASs), where the agent of MASs is subject to inequality constraints, and the team objective function accounts for impulse effects. Firstly, to address the inequality constraints,the penalty method is introduced. Then, a novel optimization strategy is developed, which only requires that the team objective function be strongly convex.
文摘In the video-based surveillance application, moving shadows can affect the correct localization and detection of moving objects. This paper aims to present a method for shadow detection and suppression used for moving visual object detection. The major novelty of the shadow suppression is the integration of several features including photometric invariant color feature, motion edge feature, and spatial feature etc. By modifying process for false shadow detected, the averaging detection rate of moving object reaches above 90% in the test of Hall-Monitor sequence.
文摘To meet the development trend of multi-bar warp knit-ting machine towards high-speed,advanced technologyand computer control and the requirements of variousproducts with small quantity,there are many researcheson the computer-aided pattern design of multi-barwarp knitted fabrics.In terms of the special propertiesof the computer-aided pattern design of multi-barwarp knitted fabrics,the Object Oriented Program(OOP)programming-Object Windows class Library(OWL)programming is selected.According to thecharacters of the OWL programming,various functionsare defined.Pattern design and technical parameters canbe output,which offers a great convenience for the fac-tory.
基金supported by the National Natural Science Foundation of China(No.51507141)Key research and development plan of Shaanxi Province(No.2018ZDCXL-GY-10-04)+1 种基金the National Key Research and Development Program of China(No.2016YFC0401409)the Shaanxi provincial education office fund(No.17JK0547).
文摘Interval prediction of wind power,which features the upper and lower limits of wind power at a given confidence level,plays a significant role in accurate prediction and stability of the power grid integrated with wind power.However,the conventional methods of interval prediction are commonly based on a hypothetic probability distribution function,which neglects the correlations among various variables,leading to the decrease of prediction accuracy.Therefore,we improve the multi-objective interval prediction based on the conditional copula function,through which we can fully utilize the correlations among variables to improve prediction accuracy without an assumed probability distribution function.We use the multi-objective optimization method of nondominated sorting genetic algorithm-II(NSGA-II)to obtain the optimal solution set.The particular best solution is weighted by the prediction interval average width(PIAW)and prediction interval coverage probability(PICP)to pick the optimized solution in practical examples.Finally,we apply the proposed method to three wind power plants in different cities in China as examples forvalidation and obtain higher prediction accuracy compared with other methods,i.e.,relevance vector machine(RVM),artificial neural network(ANN),and particle swarm optimization kernel extreme learning machine(PSO-KELM).These results demonstrate the superiority and practicability of this method in interval prediction of wind power.
