Due to the atmospheric turbulence and the system noise, images are blurred in the astronomical or space object detection. Wavefront aberrations and system noise make the capability of detecting objects decrease greatl...Due to the atmospheric turbulence and the system noise, images are blurred in the astronomical or space object detection. Wavefront aberrations and system noise make the capability of detecting objects decrease greatly. A two-channel image restoration method based on alternating minimization is proposed to restore the turbulence degraded images. The images at different times are regarded as separate channels, then the object and the point spread function(PSF) are reconstructed in an alternative way. There are two optimization parameters in the algorithm: the object and the PSF. Each optimization step is transformed into a constraint problem by variable splitting and processed by the augmented Lagrangian method. The results of simulation and actual experiment verify that the two-channel image restoration method can always converge rapidly within five iterations, and values of normalized root mean square error(NRMSE) remain below 3% after five iterations. Standard deviation data show that optimized alternating minimization(OAM) has strong stability and adaptability to different turbulent levels and noise levels. Restored images are approximate to the ideal imaging by visual assessment, even though atmospheric turbulence and systemnoise have a strong impact on imaging. Additionally, the method can remove noise effectively during the process of image restoration.展开更多
Multiplicative calculus(MUC)measures the rate of change of function in terms of ratios,which makes the exponential functions significantly linear in the framework of MUC.Therefore,a generally non-linear optimization p...Multiplicative calculus(MUC)measures the rate of change of function in terms of ratios,which makes the exponential functions significantly linear in the framework of MUC.Therefore,a generally non-linear optimization problem containing exponential functions becomes a linear problem in MUC.Taking this as motivation,this paper lays mathematical foundation of well-known classical Gauss-Newton minimization(CGNM)algorithm in the framework of MUC.This paper formulates the mathematical derivation of proposed method named as multiplicative Gauss-Newton minimization(MGNM)method along with its convergence properties.The proposed method is generalized for n number of variables,and all its theoretical concepts are authenticated by simulation results.Two case studies have been conducted incorporating multiplicatively-linear and non-linear exponential functions.From simulation results,it has been observed that proposed MGNM method converges for 12972 points,out of 19600 points considered while optimizing multiplicatively-linear exponential function,whereas CGNM and multiplicative Newton minimization methods converge for only 2111 and 9922 points,respectively.Furthermore,for a given set of initial value,the proposed MGNM converges only after 2 iterations as compared to 5 iterations taken by other methods.A similar pattern is observed for multiplicatively-non-linear exponential function.Therefore,it can be said that proposed method converges faster and for large range of initial values as compared to conventional methods.展开更多
Inverters are power electronic devices that change over DC to sinusoidal AC quantity. Be that as it may, in down to earth, these devices produce non-sinusoidal yield which contains harmonics, so as to blend a close si...Inverters are power electronic devices that change over DC to sinusoidal AC quantity. Be that as it may, in down to earth, these devices produce non-sinusoidal yield which contains harmonics, so as to blend a close sinusoidal component and to lessen the harmonic distortion multilevel inverters developed. Mathematical methods, which were developed, are derivative based and need initial considerations. To overcome this, evolutionary algorithms, which are derivative free and accurate, were developed for obtaining multi levels of output voltage. The proposed work uses two evolutionary algorithms, namely, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm. These algorithms are used to generate the switching angles by satisfying the non linear transcendental equations that govern the low order harmonic components. A seven level cascaded full bridge inverter is designed using MATLAB/Simulink and the results validate the results for switching angles. The Total Harmonic Distortion (THD) value obtained for GA and PSO is 11.81% and 10.84% respectively. The solution obtained from GA algorithm was implemented in hardware using dsPIC controller to validate the simulation results. The THD value obtained for cascaded seven-level multilevel inverter in the hardware prototype is 25.9%.展开更多
Regression testing is a widely used approach to confirm the correct functionality of the software in incremental development.The use of test cases makes it easier to test the ripple effect of changed requirements.Rigo...Regression testing is a widely used approach to confirm the correct functionality of the software in incremental development.The use of test cases makes it easier to test the ripple effect of changed requirements.Rigorous testingmay help in meeting the quality criteria that is based on the conformance to the requirements as given by the intended stakeholders.However,a minimized and prioritized set of test cases may reduce the efforts and time required for testingwhile focusing on the timely delivery of the software application.In this research,a technique named Test Reduce has been presented to get a minimal set of test cases based on high priority to ensure that the web applicationmeets the required quality criteria.A new technique TestReduce is proposed with a blend of genetic algorithm to find an optimized and minimal set of test cases.The ultimate objective associated with this study is to provide a technique that may solve the minimization problem of regression test cases in the case of linked requirements.In this research,the 100-Dollar prioritization approach is used to define the priority of the new requirements.展开更多
Multi-level inverters(MLIs)have become popular in different applications such as industrial power control systems and distributed generations.There are different forms of MLIs.The cascaded MLIs(CMLIs)have some special...Multi-level inverters(MLIs)have become popular in different applications such as industrial power control systems and distributed generations.There are different forms of MLIs.The cascaded MLIs(CMLIs)have some special advantages among them such as more different output voltage levels using the same number of components and higher power quality.In this paper,a 27-level inverter switching algorithm considering total harmonic distortion(THD)minimization is investigated.Switching angles of the inverter switches are achieved by minimizing a THD-based objective function.In order to minimize the THD-based objective function,the hyper-spherical search(HSS)algorithm,as a novel optimization algorithm,is improved and the results of improved HSS(IHSS)are compared with HSS algorithm and other five evolutionary algorithms to show the advantages of IHSS algorithm.展开更多
Abstract Recently a, monotone generalized directional derixrative has been introduced for Lipschitz functions. This concept has been applied to represent and optimize nonsmooth functions. The second a.pplication resul...Abstract Recently a, monotone generalized directional derixrative has been introduced for Lipschitz functions. This concept has been applied to represent and optimize nonsmooth functions. The second a.pplication result,ed relevant for parallel computing, by allowing to define minimization algorithms with high degree of inherent parallelism. The paper presents first the theoretical background, namely the notions of monotone generalized directional derivative and monotone generalized subdifferential. Then it defines the tools for the procedures, that is a necessary optimality condition and a steel>est descent direction. Therefore the minimization algorithms are outlined. Successively the used architectures and the performed numerical expertence are described, by listing and commenting the t.ested functions and the obtained results.展开更多
In [1] the unconstrained minimization problem was considered and presented an algorithm without derivative. But the terminative conditions and convergence proof of the algorithm were not given. In this paper, we prese...In [1] the unconstrained minimization problem was considered and presented an algorithm without derivative. But the terminative conditions and convergence proof of the algorithm were not given. In this paper, we present a revised algorithm and prove its convergence.展开更多
In urban Vehicular Ad hoc Networks(VANETs),high mobility of vehicular environment and frequently changed network topology call for a low delay end-to-end routing algorithm.In this paper,we propose a Multi-Agent Reinfo...In urban Vehicular Ad hoc Networks(VANETs),high mobility of vehicular environment and frequently changed network topology call for a low delay end-to-end routing algorithm.In this paper,we propose a Multi-Agent Reinforcement Learning(MARL)based decentralized routing scheme,where the inherent similarity between the routing problem in VANET and the MARL problem is exploited.The proposed routing scheme models the interaction between vehicles and the environment as a multi-agent problem in which each vehicle autonomously establishes the communication channel with a neighbor device regardless of the global information.Simulation performed in the 3GPP Manhattan mobility model demonstrates that our proposed decentralized routing algorithm achieves less than 45.8 ms average latency and high stability of 0.05%averaging failure rate with varying vehicle capacities.展开更多
In this paper, a new probabilistic analytical approach, the minimal cut-based recursive decomposition algorithm （MCRDA）, is presented to evaluate the seismic reliability of large-scale lifeline systems. Based on the...In this paper, a new probabilistic analytical approach, the minimal cut-based recursive decomposition algorithm （MCRDA）, is presented to evaluate the seismic reliability of large-scale lifeline systems. Based on the minimal cut searching algorithm, the approach calculates the disjoint minimal cuts one by one using the basic procedure of the recursive decomposition method. At the same time, the process obtains the disjoint minimal paths of the system. In order to improve the computation efficiency, probabilistic inequality is used to calculate a solution that satisfies the prescribed error bound. A series of case studies show that MCRDA converges rapidly when the edges of the systems have low reliabilities. Therefore, the approach can be used to evaluate large-scale lifeline systems subjected to strong seismic wave excitation.展开更多
A new problem of degree-constrained Euclidean Steiner minimal tree is discussed, which is quite useful in several fields. Although it is slightly different from the traditional degree-constrained minimal spanning tree...A new problem of degree-constrained Euclidean Steiner minimal tree is discussed, which is quite useful in several fields. Although it is slightly different from the traditional degree-constrained minimal spanning tree, it is also NP-hard. Two intelligent algorithms are proposed in an attempt to solve this difficult problem. Series of numerical examples are tested, which demonstrate that the algorithms also work well in practice.展开更多
Optimal operation of a compressor station is important since it accounts for 25%to 50%of a company’s total operating budget.In short-term management of a compressor station,handling demand uncertainty is important ye...Optimal operation of a compressor station is important since it accounts for 25%to 50%of a company’s total operating budget.In short-term management of a compressor station,handling demand uncertainty is important yet challenging.Previous studies either require precise information about the distribution of uncertain parameters or greatly simplify the compressor model.We build a two-stage robust optimization framework of power cost minimization in a natural gas compressor station with nonidentical compressors.In the first stage,decision variables are the ON/OFF state of each compressor and discharge pressure.The worst-case cost of the second stage is incorporated in the first stage.Firststage decision variables feasibility is discussed and proper feasibility cuts are also proposed for the first stage.We employ a piece-wise approximation and propose accelerate methods.Our numerical results highlight two advantages of robust approach when managing uncertainty in practical settings:(1)the feasibility of first-stage decision can be increased by up to 45%,and(2)the worst-case cost can be reduced by up to 25%compared with stochastic programming models.Furthermore,our numerical experiments show that the designed accelerate algorithm has time improvements of 1518.9%on average(3785.9%at maximum).展开更多
Aiming at the difficulty of accurately constructing the dynamic model of subtropical high, based on the potential height field time series over 500 hPa layer of T106 numerical forecast products, by using EOF（empirica...Aiming at the difficulty of accurately constructing the dynamic model of subtropical high, based on the potential height field time series over 500 hPa layer of T106 numerical forecast products, by using EOF（empirical orthogonal function） temporal-spatial separation technique, the disassembled EOF time coefficients series were regarded as dynamical model variables, and dynamic system retrieval idea as well as genetic algorithm were introduced to make dynamical model parameters optimization search, then, a reasonable non-linear dynamic model of EOF time-coefficients was established. By dynamic model integral and EOF temporal-spatial components assembly, a mid-/long-term forecast of subtropical high was carried out. The experimental results show that the forecast results of dynamic model are superior to that of general numerical model forecast results. A new modeling idea and forecast technique is presented for diagnosing and forecasting such complicated weathers as subtropical high.展开更多
The non-linear inversion of rock mechanics parameters based on genetic algorithm is presented. The principIe and step of genetic algorithm is also given. A brief discussion of this method and an application example is...The non-linear inversion of rock mechanics parameters based on genetic algorithm is presented. The principIe and step of genetic algorithm is also given. A brief discussion of this method and an application example is presented at the end of this paper. From the satisfied result, quick, convenient and practical new approach is developed to solve this kind of problems.展开更多
In this paper, an innovative Genetic Algorithms (GA)-based inexact non-linear programming (GAINLP) problem solving approach has been proposed for solving non-linear programming optimization problems with inexact infor...In this paper, an innovative Genetic Algorithms (GA)-based inexact non-linear programming (GAINLP) problem solving approach has been proposed for solving non-linear programming optimization problems with inexact information (inexact non-linear operation programming). GAINLP was developed based on a GA-based inexact quadratic solving method. The Genetic Algorithm Solver of the Global Optimization Toolbox (GASGOT) developed by MATLABTM was adopted as the implementation environment of this study. GAINLP was applied to a municipality solid waste management case. The results from different scenarios indicated that the proposed GA-based heuristic optimization approach was able to generate a solution for a complicated nonlinear problem, which also involved uncertainty.展开更多
In order to optimize the transitional time during the successive exposure scans for a step-and-scan lithography and improve the productivity in a wafer production process, an investigation of the motion trajectory pla...In order to optimize the transitional time during the successive exposure scans for a step-and-scan lithography and improve the productivity in a wafer production process, an investigation of the motion trajectory planning along the scanning direction for wafer stage was carried out. The motions of wafer stage were divided into two respective logical moves （i. e. step-move and scan-move） and the multi-motionoverlap algorithms （MMOA） were presented for optimizing the transitional time between the successive exposure scans. The conventional motion planning method, the Hazelton method and the MMOA were analyzed theoretically and simulated using MATLAB under four different exposure field sizes. The results show that the total time between two successive scans consumed by MMOA is reduced by 4.82%, 2.62%, 3.06% and 3.96%, compared with those of the conventional motion planning method; and reduced by 2.58%, 0.76%, 1.63% and 2.92%, compared with those of the Hazehon method respectively. The theoretical analyses and simulation results illuminate that the MMOA can effectively minimize the transitional step time between successive exposure scans and therefore increase the wafer fabricating productivity.展开更多
This study examines the multicriteria scheduling problem on a single machine to minimize three criteria: the maximum cost function, denoted by maximum late work (V<sub>max</sub>), maximum tardy job, denote...This study examines the multicriteria scheduling problem on a single machine to minimize three criteria: the maximum cost function, denoted by maximum late work (V<sub>max</sub>), maximum tardy job, denoted by (T<sub>max</sub>), and maximum earliness (E<sub>max</sub>). We propose several algorithms based on types of objectives function to be optimized when dealing with simultaneous minimization problems with and without weight and hierarchical minimization problems. The proposed Algorithm (3) is to find the set of efficient solutions for 1//F (V<sub>max</sub>, T<sub>max</sub>, E<sub>max</sub>) and 1//(V<sub>max</sub> + T<sub>max</sub> + E<sub>max</sub>). The Local Search Heuristic Methods (Descent Method (DM), Simulated Annealing (SA), Genetic Algorithm (GA), and the Tree Type Heuristics Method (TTHM) are applied to solve all suggested problems. Finally, the experimental results of Algorithm (3) are compared with the results of the Branch and Bound (BAB) method for optimal and Pareto optimal solutions for smaller instance sizes and compared to the Local Search Heuristic Methods for large instance sizes. These results ensure the efficiency of Algorithm (3) in a reasonable time.展开更多
Based on the analysis of previous genetic algorithms (GAs) for TSP, a novel method called Ge- GA is proposed. It combines gene pool and GA so as to direct the evolution of the whole population. The core of Ge- GA is t...Based on the analysis of previous genetic algorithms (GAs) for TSP, a novel method called Ge- GA is proposed. It combines gene pool and GA so as to direct the evolution of the whole population. The core of Ge- GA is the construction of gene pool and how to apply it to GA. Different from standard GAs, Ge- GA aims to enhance the ability of exploration and exploitation by incorporating global search with local search. On one hand a local search called Ge- Lo-calSearch operator is proposed to improve the solution quality, on the other hand the modified Inver-Over operator called Ge InverOver is considered as a global search mechanism to expand solution space of local minimal. Both of these operators are based on the gene pool. Our algorithm is applied to 11 well-known traveling salesman problems whose numbers of cities are from 70 to 1577 cities. The experiments results indicate that Ge- GA has great robustness for TSP. For each test instance, the average value of solution quality, found in accepted time, stays within 0. 001% from the optimum.展开更多
基金supported by the National Natural Science Foundation of China(No.11573011)the Six Talent Peaks Project of Jiangsu Province(No.KTHY-058)+1 种基金the’333’Talent’s Project in Jiangsu Province(No.BRA2019244)the Research and Practice Innovation for Postgraduate in Jiangsu Province(No.KYCX20_2961)。
文摘Due to the atmospheric turbulence and the system noise, images are blurred in the astronomical or space object detection. Wavefront aberrations and system noise make the capability of detecting objects decrease greatly. A two-channel image restoration method based on alternating minimization is proposed to restore the turbulence degraded images. The images at different times are regarded as separate channels, then the object and the point spread function(PSF) are reconstructed in an alternative way. There are two optimization parameters in the algorithm: the object and the PSF. Each optimization step is transformed into a constraint problem by variable splitting and processed by the augmented Lagrangian method. The results of simulation and actual experiment verify that the two-channel image restoration method can always converge rapidly within five iterations, and values of normalized root mean square error(NRMSE) remain below 3% after five iterations. Standard deviation data show that optimized alternating minimization(OAM) has strong stability and adaptability to different turbulent levels and noise levels. Restored images are approximate to the ideal imaging by visual assessment, even though atmospheric turbulence and systemnoise have a strong impact on imaging. Additionally, the method can remove noise effectively during the process of image restoration.
文摘Multiplicative calculus(MUC)measures the rate of change of function in terms of ratios,which makes the exponential functions significantly linear in the framework of MUC.Therefore,a generally non-linear optimization problem containing exponential functions becomes a linear problem in MUC.Taking this as motivation,this paper lays mathematical foundation of well-known classical Gauss-Newton minimization(CGNM)algorithm in the framework of MUC.This paper formulates the mathematical derivation of proposed method named as multiplicative Gauss-Newton minimization(MGNM)method along with its convergence properties.The proposed method is generalized for n number of variables,and all its theoretical concepts are authenticated by simulation results.Two case studies have been conducted incorporating multiplicatively-linear and non-linear exponential functions.From simulation results,it has been observed that proposed MGNM method converges for 12972 points,out of 19600 points considered while optimizing multiplicatively-linear exponential function,whereas CGNM and multiplicative Newton minimization methods converge for only 2111 and 9922 points,respectively.Furthermore,for a given set of initial value,the proposed MGNM converges only after 2 iterations as compared to 5 iterations taken by other methods.A similar pattern is observed for multiplicatively-non-linear exponential function.Therefore,it can be said that proposed method converges faster and for large range of initial values as compared to conventional methods.
文摘Inverters are power electronic devices that change over DC to sinusoidal AC quantity. Be that as it may, in down to earth, these devices produce non-sinusoidal yield which contains harmonics, so as to blend a close sinusoidal component and to lessen the harmonic distortion multilevel inverters developed. Mathematical methods, which were developed, are derivative based and need initial considerations. To overcome this, evolutionary algorithms, which are derivative free and accurate, were developed for obtaining multi levels of output voltage. The proposed work uses two evolutionary algorithms, namely, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm. These algorithms are used to generate the switching angles by satisfying the non linear transcendental equations that govern the low order harmonic components. A seven level cascaded full bridge inverter is designed using MATLAB/Simulink and the results validate the results for switching angles. The Total Harmonic Distortion (THD) value obtained for GA and PSO is 11.81% and 10.84% respectively. The solution obtained from GA algorithm was implemented in hardware using dsPIC controller to validate the simulation results. The THD value obtained for cascaded seven-level multilevel inverter in the hardware prototype is 25.9%.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Large Groups,Project under grant number RGP.2/49/43.
文摘Regression testing is a widely used approach to confirm the correct functionality of the software in incremental development.The use of test cases makes it easier to test the ripple effect of changed requirements.Rigorous testingmay help in meeting the quality criteria that is based on the conformance to the requirements as given by the intended stakeholders.However,a minimized and prioritized set of test cases may reduce the efforts and time required for testingwhile focusing on the timely delivery of the software application.In this research,a technique named Test Reduce has been presented to get a minimal set of test cases based on high priority to ensure that the web applicationmeets the required quality criteria.A new technique TestReduce is proposed with a blend of genetic algorithm to find an optimized and minimal set of test cases.The ultimate objective associated with this study is to provide a technique that may solve the minimization problem of regression test cases in the case of linked requirements.In this research,the 100-Dollar prioritization approach is used to define the priority of the new requirements.
文摘Multi-level inverters(MLIs)have become popular in different applications such as industrial power control systems and distributed generations.There are different forms of MLIs.The cascaded MLIs(CMLIs)have some special advantages among them such as more different output voltage levels using the same number of components and higher power quality.In this paper,a 27-level inverter switching algorithm considering total harmonic distortion(THD)minimization is investigated.Switching angles of the inverter switches are achieved by minimizing a THD-based objective function.In order to minimize the THD-based objective function,the hyper-spherical search(HSS)algorithm,as a novel optimization algorithm,is improved and the results of improved HSS(IHSS)are compared with HSS algorithm and other five evolutionary algorithms to show the advantages of IHSS algorithm.
文摘Abstract Recently a, monotone generalized directional derixrative has been introduced for Lipschitz functions. This concept has been applied to represent and optimize nonsmooth functions. The second a.pplication result,ed relevant for parallel computing, by allowing to define minimization algorithms with high degree of inherent parallelism. The paper presents first the theoretical background, namely the notions of monotone generalized directional derivative and monotone generalized subdifferential. Then it defines the tools for the procedures, that is a necessary optimality condition and a steel>est descent direction. Therefore the minimization algorithms are outlined. Successively the used architectures and the performed numerical expertence are described, by listing and commenting the t.ested functions and the obtained results.
文摘In [1] the unconstrained minimization problem was considered and presented an algorithm without derivative. But the terminative conditions and convergence proof of the algorithm were not given. In this paper, we present a revised algorithm and prove its convergence.
基金This work is supported by the National Science Foundation of China under grant No.61901403,61790551,and 61925106,Youth Innovation Fund of Xiamen No.3502Z20206039 and Tsinghua-Foshan Innovation Special Fund(TFISF)No.2020THFS0109.
文摘In urban Vehicular Ad hoc Networks(VANETs),high mobility of vehicular environment and frequently changed network topology call for a low delay end-to-end routing algorithm.In this paper,we propose a Multi-Agent Reinforcement Learning(MARL)based decentralized routing scheme,where the inherent similarity between the routing problem in VANET and the MARL problem is exploited.The proposed routing scheme models the interaction between vehicles and the environment as a multi-agent problem in which each vehicle autonomously establishes the communication channel with a neighbor device regardless of the global information.Simulation performed in the 3GPP Manhattan mobility model demonstrates that our proposed decentralized routing algorithm achieves less than 45.8 ms average latency and high stability of 0.05%averaging failure rate with varying vehicle capacities.
基金the Natural Science Fundation of China for the Innovative Research Group of China Under Grant No. 50621062
文摘In this paper, a new probabilistic analytical approach, the minimal cut-based recursive decomposition algorithm （MCRDA）, is presented to evaluate the seismic reliability of large-scale lifeline systems. Based on the minimal cut searching algorithm, the approach calculates the disjoint minimal cuts one by one using the basic procedure of the recursive decomposition method. At the same time, the process obtains the disjoint minimal paths of the system. In order to improve the computation efficiency, probabilistic inequality is used to calculate a solution that satisfies the prescribed error bound. A series of case studies show that MCRDA converges rapidly when the edges of the systems have low reliabilities. Therefore, the approach can be used to evaluate large-scale lifeline systems subjected to strong seismic wave excitation.
基金the National Natural Science Foundation of China (70471065)the Shanghai Leading Academic Discipline Project (T0502).
文摘A new problem of degree-constrained Euclidean Steiner minimal tree is discussed, which is quite useful in several fields. Although it is slightly different from the traditional degree-constrained minimal spanning tree, it is also NP-hard. Two intelligent algorithms are proposed in an attempt to solve this difficult problem. Series of numerical examples are tested, which demonstrate that the algorithms also work well in practice.
基金the support from the National Science Foundation of China(Grant 71822105)。
文摘Optimal operation of a compressor station is important since it accounts for 25%to 50%of a company’s total operating budget.In short-term management of a compressor station,handling demand uncertainty is important yet challenging.Previous studies either require precise information about the distribution of uncertain parameters or greatly simplify the compressor model.We build a two-stage robust optimization framework of power cost minimization in a natural gas compressor station with nonidentical compressors.In the first stage,decision variables are the ON/OFF state of each compressor and discharge pressure.The worst-case cost of the second stage is incorporated in the first stage.Firststage decision variables feasibility is discussed and proper feasibility cuts are also proposed for the first stage.We employ a piece-wise approximation and propose accelerate methods.Our numerical results highlight two advantages of robust approach when managing uncertainty in practical settings:(1)the feasibility of first-stage decision can be increased by up to 45%,and(2)the worst-case cost can be reduced by up to 25%compared with stochastic programming models.Furthermore,our numerical experiments show that the designed accelerate algorithm has time improvements of 1518.9%on average(3785.9%at maximum).
基金Project supported by the National Natural Science Foundation of China (No.40375019) the Tropical Marine and Meteorology Science Foundation (No.200609) the Jiangsu Key Laboratory of Meteorological Disaster Foundation (No.KLME0507)
文摘Aiming at the difficulty of accurately constructing the dynamic model of subtropical high, based on the potential height field time series over 500 hPa layer of T106 numerical forecast products, by using EOF（empirical orthogonal function） temporal-spatial separation technique, the disassembled EOF time coefficients series were regarded as dynamical model variables, and dynamic system retrieval idea as well as genetic algorithm were introduced to make dynamical model parameters optimization search, then, a reasonable non-linear dynamic model of EOF time-coefficients was established. By dynamic model integral and EOF temporal-spatial components assembly, a mid-/long-term forecast of subtropical high was carried out. The experimental results show that the forecast results of dynamic model are superior to that of general numerical model forecast results. A new modeling idea and forecast technique is presented for diagnosing and forecasting such complicated weathers as subtropical high.
文摘The non-linear inversion of rock mechanics parameters based on genetic algorithm is presented. The principIe and step of genetic algorithm is also given. A brief discussion of this method and an application example is presented at the end of this paper. From the satisfied result, quick, convenient and practical new approach is developed to solve this kind of problems.
文摘In this paper, an innovative Genetic Algorithms (GA)-based inexact non-linear programming (GAINLP) problem solving approach has been proposed for solving non-linear programming optimization problems with inexact information (inexact non-linear operation programming). GAINLP was developed based on a GA-based inexact quadratic solving method. The Genetic Algorithm Solver of the Global Optimization Toolbox (GASGOT) developed by MATLABTM was adopted as the implementation environment of this study. GAINLP was applied to a municipality solid waste management case. The results from different scenarios indicated that the proposed GA-based heuristic optimization approach was able to generate a solution for a complicated nonlinear problem, which also involved uncertainty.
基金the National Basic Research Program of China(No.2003CB716206)the National Natural Science Foundation of China(No.50605025)
文摘In order to optimize the transitional time during the successive exposure scans for a step-and-scan lithography and improve the productivity in a wafer production process, an investigation of the motion trajectory planning along the scanning direction for wafer stage was carried out. The motions of wafer stage were divided into two respective logical moves （i. e. step-move and scan-move） and the multi-motionoverlap algorithms （MMOA） were presented for optimizing the transitional time between the successive exposure scans. The conventional motion planning method, the Hazelton method and the MMOA were analyzed theoretically and simulated using MATLAB under four different exposure field sizes. The results show that the total time between two successive scans consumed by MMOA is reduced by 4.82%, 2.62%, 3.06% and 3.96%, compared with those of the conventional motion planning method; and reduced by 2.58%, 0.76%, 1.63% and 2.92%, compared with those of the Hazehon method respectively. The theoretical analyses and simulation results illuminate that the MMOA can effectively minimize the transitional step time between successive exposure scans and therefore increase the wafer fabricating productivity.
文摘This study examines the multicriteria scheduling problem on a single machine to minimize three criteria: the maximum cost function, denoted by maximum late work (V<sub>max</sub>), maximum tardy job, denoted by (T<sub>max</sub>), and maximum earliness (E<sub>max</sub>). We propose several algorithms based on types of objectives function to be optimized when dealing with simultaneous minimization problems with and without weight and hierarchical minimization problems. The proposed Algorithm (3) is to find the set of efficient solutions for 1//F (V<sub>max</sub>, T<sub>max</sub>, E<sub>max</sub>) and 1//(V<sub>max</sub> + T<sub>max</sub> + E<sub>max</sub>). The Local Search Heuristic Methods (Descent Method (DM), Simulated Annealing (SA), Genetic Algorithm (GA), and the Tree Type Heuristics Method (TTHM) are applied to solve all suggested problems. Finally, the experimental results of Algorithm (3) are compared with the results of the Branch and Bound (BAB) method for optimal and Pareto optimal solutions for smaller instance sizes and compared to the Local Search Heuristic Methods for large instance sizes. These results ensure the efficiency of Algorithm (3) in a reasonable time.
基金Supported by the National Natural Science Foundation of China(70071042,60073043,and 60133010)
文摘Based on the analysis of previous genetic algorithms (GAs) for TSP, a novel method called Ge- GA is proposed. It combines gene pool and GA so as to direct the evolution of the whole population. The core of Ge- GA is the construction of gene pool and how to apply it to GA. Different from standard GAs, Ge- GA aims to enhance the ability of exploration and exploitation by incorporating global search with local search. On one hand a local search called Ge- Lo-calSearch operator is proposed to improve the solution quality, on the other hand the modified Inver-Over operator called Ge InverOver is considered as a global search mechanism to expand solution space of local minimal. Both of these operators are based on the gene pool. Our algorithm is applied to 11 well-known traveling salesman problems whose numbers of cities are from 70 to 1577 cities. The experiments results indicate that Ge- GA has great robustness for TSP. For each test instance, the average value of solution quality, found in accepted time, stays within 0. 001% from the optimum.
