By combing the properties of chaos optimization method and genetic algorithm,an adaptive mutative scale chaos genetic algorithm(AMSCGA) was proposed by using one-dimensional iterative chaotic self-map with infinite co...By combing the properties of chaos optimization method and genetic algorithm,an adaptive mutative scale chaos genetic algorithm(AMSCGA) was proposed by using one-dimensional iterative chaotic self-map with infinite collapses within the finite region of [-1,1].Some measures in the optimization algorithm,such as adjusting the searching space of optimized variables continuously by using adaptive mutative scale method and making the most circle time as its control guideline,were taken to ensure its speediness and veracity in seeking the optimization process.The calculation examples about three testing functions reveal that AMSCGA has both high searching speed and high precision.Furthermore,the average truncated generations,the distribution entropy of truncated generations and the ratio of average inertia generations were used to evaluate the optimization efficiency of AMSCGA quantificationally.It is shown that the optimization efficiency of AMSCGA is higher than that of genetic algorithm.展开更多
Satellite constellation design for space optical systems is essentially a multiple-objective optimization problem. In this work, to tackle this challenge, we first categorize the performance metrics of the space optic...Satellite constellation design for space optical systems is essentially a multiple-objective optimization problem. In this work, to tackle this challenge, we first categorize the performance metrics of the space optical system by taking into account the system tasks（i.e., target detection and tracking）. We then propose a new non-dominated sorting genetic algorithm（NSGA） to maximize the system surveillance performance. Pareto optimal sets are employed to deal with the conflicts due to the presence of multiple cost functions. Simulation results verify the validity and the improved performance of the proposed technique over benchmark methods.展开更多
A hybrid method for synthesizing antenna＇s three dimensional （3D） pattern is proposed to obtain the low sidelobe feature of truncated cone conformal phased arrays. In this method, the elements of truncated cone con...A hybrid method for synthesizing antenna＇s three dimensional （3D） pattern is proposed to obtain the low sidelobe feature of truncated cone conformal phased arrays. In this method, the elements of truncated cone conformal phased arrays are projected to the tangent plane in one generatrix of the truncated cone. Then two dimensional （2D） Chebyshev amplitude distribution optimization is respectively used in two mutual vertical directions of the tangent plane. According to the location of the elements, the excitation current amplitude distribution of each element on the conformal structure is derived reversely, then the excitation current amplitude is further optimized by using the genetic algorithm （GA）. A truncated cone problem with 8x8 elements on it, and a 3D pattern desired side lobe level （SLL） up to 35 dB, is studied. By using the hybrid method, the optimal goal is accomplished with acceptable CPU time, which indicates that this hybrid method for the low sidelobe synthesis is feasible.展开更多
It is significant to combine multiple tasks into an optimal work package in decision-making of aircraft maintenance to reduce cost,so a cost rate model of combinatorial maintenance is an urgent need.However,the optima...It is significant to combine multiple tasks into an optimal work package in decision-making of aircraft maintenance to reduce cost,so a cost rate model of combinatorial maintenance is an urgent need.However,the optimal combination under various constraints not only involves numerical calculations but also is an NP-hard combinatorial problem.To solve the problem,an adaptive genetic algorithm based on cluster search,which is divided into two phases,is put forward.In the first phase,according to the density,all individuals can be homogeneously scattered over the whole solution space through crossover and mutation and better individuals are collected as candidate cluster centres.In the second phase,the search is confined to the neighbourhood of some selected possible solutions to accurately solve with cluster radius decreasing slowly,meanwhile all clusters continuously move to better regions until all the peaks in the question space is searched.This algorithm can efficiently solve the combination problem.Taking the optimization on decision-making of aircraft maintenance by the algorithm for an example,maintenance which combines multiple parts or tasks can significantly enhance economic benefit when the halt cost is rather high.展开更多
Crimping is widely adopted in the production of large-diameter submerged-arc welding pipes. Traditionally, designers obtain the technical parameters for crimping from experience or by trial and error through experimen...Crimping is widely adopted in the production of large-diameter submerged-arc welding pipes. Traditionally, designers obtain the technical parameters for crimping from experience or by trial and error through experiments and the finite element(FE) method. However, it is difficult to achieve ideal crimping quality by these approaches. To resolve this issue, crimping parameter design was investigated by multi-objective optimization. Crimping was simulated using the FE code ABAQUS and the FE model was validated experimentally. A welding pipe made of X80 high-strength pipeline steel was considered as a target object and the optimization problem for its crimping was formulated as a mathematical model and crimping was optimized. A response surface method based on the radial basis function was used to construct a surrogate model; the genetic algorithm NSGA-II was adopted to search for Pareto solutions; grey relational analysis was used to determine the most satisfactory solution from the Pareto solutions. The obtained optimal design of parameters shows good agreement with the initial design and remarkably improves the crimping quality. Thus, the results provide an effective approach for improving crimping quality and reducing design times.展开更多
In order to reduce both the weight of vehicles and the damage of occupants in a crash event simultaneously, it is necessary to perform a multi-objective optimal design of the automotive energy absorbing components. Mo...In order to reduce both the weight of vehicles and the damage of occupants in a crash event simultaneously, it is necessary to perform a multi-objective optimal design of the automotive energy absorbing components. Modified non-dominated sorting genetic algorithm II(NSGA II) was used for multi-objective optimization of automotive S-rail considering absorbed energy(E), peak crushing force(Fmax) and mass of the structure(W) as three conflicting objective functions. In the multi-objective optimization problem(MOP), E and Fmax are defined by polynomial models extracted using the software GEvo M based on train and test data obtained from numerical simulation of quasi-static crushing of the S-rail using ABAQUS. Finally, the nearest to ideal point(NIP)method and technique for ordering preferences by similarity to ideal solution(TOPSIS) method are used to find the some trade-off optimum design points from all non-dominated optimum design points represented by the Pareto fronts. Results represent that the optimum design point obtained from TOPSIS method exhibits better trade-off in comparison with that of optimum design point obtained from NIP method.展开更多
基金Project(60874114) supported by the National Natural Science Foundation of China
文摘By combing the properties of chaos optimization method and genetic algorithm,an adaptive mutative scale chaos genetic algorithm(AMSCGA) was proposed by using one-dimensional iterative chaotic self-map with infinite collapses within the finite region of [-1,1].Some measures in the optimization algorithm,such as adjusting the searching space of optimized variables continuously by using adaptive mutative scale method and making the most circle time as its control guideline,were taken to ensure its speediness and veracity in seeking the optimization process.The calculation examples about three testing functions reveal that AMSCGA has both high searching speed and high precision.Furthermore,the average truncated generations,the distribution entropy of truncated generations and the ratio of average inertia generations were used to evaluate the optimization efficiency of AMSCGA quantificationally.It is shown that the optimization efficiency of AMSCGA is higher than that of genetic algorithm.
文摘Satellite constellation design for space optical systems is essentially a multiple-objective optimization problem. In this work, to tackle this challenge, we first categorize the performance metrics of the space optical system by taking into account the system tasks（i.e., target detection and tracking）. We then propose a new non-dominated sorting genetic algorithm（NSGA） to maximize the system surveillance performance. Pareto optimal sets are employed to deal with the conflicts due to the presence of multiple cost functions. Simulation results verify the validity and the improved performance of the proposed technique over benchmark methods.
基金supported by the Fundamental Research Funds for the Central Universities(YWF-13D2-XX-13)the National High-tech Research and Development Program(863 Program)(2008AA121802)
文摘A hybrid method for synthesizing antenna＇s three dimensional （3D） pattern is proposed to obtain the low sidelobe feature of truncated cone conformal phased arrays. In this method, the elements of truncated cone conformal phased arrays are projected to the tangent plane in one generatrix of the truncated cone. Then two dimensional （2D） Chebyshev amplitude distribution optimization is respectively used in two mutual vertical directions of the tangent plane. According to the location of the elements, the excitation current amplitude distribution of each element on the conformal structure is derived reversely, then the excitation current amplitude is further optimized by using the genetic algorithm （GA）. A truncated cone problem with 8x8 elements on it, and a 3D pattern desired side lobe level （SLL） up to 35 dB, is studied. By using the hybrid method, the optimal goal is accomplished with acceptable CPU time, which indicates that this hybrid method for the low sidelobe synthesis is feasible.
基金supported by the National Natural Science Foundation of China(6107901361079014+4 种基金61403198)the National Natural Science Funds and Civil Aviaiton Mutual Funds(U1533128U1233114)the Programs of Natural Science Foundation of China and China Civil Aviation Joint Fund(60939003)the Natural Science Foundation of Jiangsu Province in China(BK2011737)
文摘It is significant to combine multiple tasks into an optimal work package in decision-making of aircraft maintenance to reduce cost,so a cost rate model of combinatorial maintenance is an urgent need.However,the optimal combination under various constraints not only involves numerical calculations but also is an NP-hard combinatorial problem.To solve the problem,an adaptive genetic algorithm based on cluster search,which is divided into two phases,is put forward.In the first phase,according to the density,all individuals can be homogeneously scattered over the whole solution space through crossover and mutation and better individuals are collected as candidate cluster centres.In the second phase,the search is confined to the neighbourhood of some selected possible solutions to accurately solve with cluster radius decreasing slowly,meanwhile all clusters continuously move to better regions until all the peaks in the question space is searched.This algorithm can efficiently solve the combination problem.Taking the optimization on decision-making of aircraft maintenance by the algorithm for an example,maintenance which combines multiple parts or tasks can significantly enhance economic benefit when the halt cost is rather high.
基金Project(Y2012035)supported by the Natural Science Foundation of Hebei Provincial Education Department,ChinaProject(12211014)supported by the Natural Science Foundation of Hebei Provincial Technology Department,China+2 种基金Project(NJZY14006)supported by the Inner Mongolia Higher School Science and Technology Research Program,ChinaProject(2014BS0502)supported by the Natural Science Foundation of Inner Mongolia,ChinaProject(135143)supported by the Program of Higher-level Talents Fund of Inner Mongolia University,China
文摘Crimping is widely adopted in the production of large-diameter submerged-arc welding pipes. Traditionally, designers obtain the technical parameters for crimping from experience or by trial and error through experiments and the finite element(FE) method. However, it is difficult to achieve ideal crimping quality by these approaches. To resolve this issue, crimping parameter design was investigated by multi-objective optimization. Crimping was simulated using the FE code ABAQUS and the FE model was validated experimentally. A welding pipe made of X80 high-strength pipeline steel was considered as a target object and the optimization problem for its crimping was formulated as a mathematical model and crimping was optimized. A response surface method based on the radial basis function was used to construct a surrogate model; the genetic algorithm NSGA-II was adopted to search for Pareto solutions; grey relational analysis was used to determine the most satisfactory solution from the Pareto solutions. The obtained optimal design of parameters shows good agreement with the initial design and remarkably improves the crimping quality. Thus, the results provide an effective approach for improving crimping quality and reducing design times.
文摘In order to reduce both the weight of vehicles and the damage of occupants in a crash event simultaneously, it is necessary to perform a multi-objective optimal design of the automotive energy absorbing components. Modified non-dominated sorting genetic algorithm II(NSGA II) was used for multi-objective optimization of automotive S-rail considering absorbed energy(E), peak crushing force(Fmax) and mass of the structure(W) as three conflicting objective functions. In the multi-objective optimization problem(MOP), E and Fmax are defined by polynomial models extracted using the software GEvo M based on train and test data obtained from numerical simulation of quasi-static crushing of the S-rail using ABAQUS. Finally, the nearest to ideal point(NIP)method and technique for ordering preferences by similarity to ideal solution(TOPSIS) method are used to find the some trade-off optimum design points from all non-dominated optimum design points represented by the Pareto fronts. Results represent that the optimum design point obtained from TOPSIS method exhibits better trade-off in comparison with that of optimum design point obtained from NIP method.
