This paper proposed a RIME-VMD-BiLSTM surrogate model to rapidly and precisely predict the seismic response of a nonlinear vehicle-track-bridge(VTB)system.The surrogate model employs the RIME algorithm to optimize the...This paper proposed a RIME-VMD-BiLSTM surrogate model to rapidly and precisely predict the seismic response of a nonlinear vehicle-track-bridge(VTB)system.The surrogate model employs the RIME algorithm to optimize the variational mode decomposition(VMD)parameters(k and α)and the architecture and hyperparameter of the bidirectional long-and short-term memory network(BiLSTM).After comparing different combinations and optimization algorithms,the surrogate model was trained and used to analyze a typical 9-span 32-m high-speed railway simply supported bridge system.A series of numerical examples considering the vehicle speed,bridge damping,seismic intensity,and training strategy on the prediction effect of the surrogate model were conducted on the extended OpenSees platform.The results show that the BiLSTM model performed better than the LSTM model,whereas the prediction effects of the single-LSTM and BiLSTM models were relatively poor.With the introduction of the VMD and RIME optimization techniques,the prediction effect of the proposed RIME-VMD-BiLSTM model was excellent.The abovementioned factors had a significant influence on the seismic response of a VTB system but little impact on the prediction effect of the surrogate model.The proposed surrogate model exhibits notable transferability and robustness for predicting the VTB’s nonlinear seismic response.展开更多
In order to optimize the crashworthy characteristic of energy-absorbing structures, the surrogate models of specific energy absorption （SEA） and ratio of SEA to initial peak force （REAF） with respect to the design...In order to optimize the crashworthy characteristic of energy-absorbing structures, the surrogate models of specific energy absorption （SEA） and ratio of SEA to initial peak force （REAF） with respect to the design parameters were respectively constructed based on surrogate model optimization methods （polynomial response surface method （PRSM） and Kriging method （KM））. Firstly, the sample data were prepared through the design of experiment （DOE）. Then, the test data models were set up based on the theory of surrogate model, and the data samples were trained to obtain the response relationship between the SEA ＆amp; REAF and design parameters. At last, the structure optimal parameters were obtained by visual analysis and genetic algorithm （GA）. The results indicate that the KM, where the local interpolation method is used in Gauss correlation function, has the highest fitting accuracy and the structure optimal parameters are obtained as： the SEA of 29.8558 kJ/kg （corresponding toa=70 mm andt= 3.5 mm） and REAF of 0.2896 （corresponding toa=70 mm andt=1.9615 mm）. The basis function of the quartic PRSM with higher order than that of the quadratic PRSM, and the mutual influence of the design variables are considered, so the fitting accuracy of the quartic PRSM is higher than that of the quadratic PRSM.展开更多
Multi-objective optimization(MOO)for the microwave metamaterial absorber(MMA)normally adopts evolutionary algo-rithms,and these optimization algorithms require many objec-tive function evaluations.To remedy this issue...Multi-objective optimization(MOO)for the microwave metamaterial absorber(MMA)normally adopts evolutionary algo-rithms,and these optimization algorithms require many objec-tive function evaluations.To remedy this issue,a surrogate-based MOO algorithm is proposed in this paper where Kriging models are employed to approximate objective functions.An efficient sampling strategy is presented to sequentially capture promising samples in the design region for exact evaluations.Firstly,new sample points are generated by the MOO on surro-gate models.Then,new samples are captured by exploiting each objective function.Furthermore,a weighted sum of the improvement of hypervolume(IHV)and the distance to sampled points is calculated to select the new sample.Compared with two well-known MOO algorithms,the proposed algorithm is vali-dated by benchmark problems.In addition,two broadband MMAs are applied to verify the feasibility and efficiency of the proposed algorithm.展开更多
OBJECTIVE To evaluate if RNA helicase DDX20,highly expressed in triple negative breast cancer(TNBC)cells,could serve as a surrogate marker for simvastatin treatment response.METHODS We first assessed correlation betwe...OBJECTIVE To evaluate if RNA helicase DDX20,highly expressed in triple negative breast cancer(TNBC)cells,could serve as a surrogate marker for simvastatin treatment response.METHODS We first assessed correlation between 17 mevalonate pathway-related genes and expression of DDX20 in a cohort of 1325 breast cancer tumors.TNBC cells,MDA-MB-231,were then treated with simvastatin and mevalonate pathway intermediates to assess the alteration in DDX20 expression.In the mouse model,MDA-MB-231 cells were injected to tail veins of mice,groups of 8mice each were injected intraperioneally with vehicle or simvastatin 25mg·kg-1 3times a week for 6weeks.The number of metastatic colonies formed was quantified and immunohistochemical(IHC)staining of DDX20 was carried out in the lung tissues.RESULTS Among the 17 genes evaluated,positive correlation with DDX20 expression was observed in eight of them,with HMGCR having the highest correlation.Our in vitro experiments show exposure of breast cancer cells to simvastatin lead to a Rho-dependent decrease in gene expression of DDX20,leading to decreased tumor proliferation in a mevalonate pathway-dependent manner.Conversely,ectopic overexpression of DDX20 significantly abrogated the anti-metastatic activity of simvastatin.A similar observation is seen in the mouse model,where simvastatin-injected mice show significantly fewer visible lung metastases compared to placebo-fed mice.IHC staining on these lung tissues showed decreased DDX20 expression in simvastatin-injected group,corroborating our observations in vitro.CONCLUSION DDX20 is a potential surrogate marker for simvastatin treatment response in breast cancer and a long term implication of our findings is the possibility of an effective combinatorial therapeutic intervention using statins(to suppress DDX20 gene expression)and a suitable firstline agent″for the kill″of invasive breast cancer.展开更多
The Infrared Hyperspectral Atmospheric SounderⅡ(HIRAS-Ⅱ)is the key equipment on FengYun-3E(FY-3E)satellite,which can realize vertical atmospheric detection,featuring hyper spectral,high sensitivity and high precisio...The Infrared Hyperspectral Atmospheric SounderⅡ(HIRAS-Ⅱ)is the key equipment on FengYun-3E(FY-3E)satellite,which can realize vertical atmospheric detection,featuring hyper spectral,high sensitivity and high precision.To ensure its accuracy of detection,it is necessary to correlate their thermal models to in-orbit da⁃ta.In this work,an investigation of intelligent correlation method named Intelligent Correlation Platform for Ther⁃mal Model(ICP-TM)was established,the advanced Kriging surrogate model and efficient adaptive region opti⁃mization algorithm were introduced.After the correlation with this method for FY-3E/HIRAS-Ⅱ,the results indi⁃cate that compared with the data in orbit,the error of the thermal model has decreased from 5 K to within±1 K in cold case(10℃).Then,the correlated model is validated in hot case(20℃),and the correlated model exhibits good universality.This correlation precision is also much superiors to the general ones like 3 K in other similar lit⁃erature.Furthermore,the process is finished in 8 days using ICP-TM,the efficiency is much better than 3 months based on manual.The results show that the proposed approach significantly enhances the accuracy and efficiency of thermal model,this contributes to the precise thermal control of subsequent infrared optical payloads.展开更多
Trans-medium flight vehicles can combine high aerial maneuverability and underwater concealment ability,which have attracted much attention recently.As the most crucial procedure,the trajectory design generally determ...Trans-medium flight vehicles can combine high aerial maneuverability and underwater concealment ability,which have attracted much attention recently.As the most crucial procedure,the trajectory design generally determines the trans-medium flight vehicle performance.To quantitatively analyze the flight vehicle performance,an entire aerial-aquatic trajectory model is developed in this paper.Different from modeling a trajectory purely for the water entry process,the constructed entire trajectory model has integrated aerial,water entry,and underwater trajectories together,which can consider the influence of the connected trajectories.As for the aerial and underwater trajectories,explicit dynamic models are established to obtain the trajectory parameters.Due to the complicated fluid force during high-velocity water entry,a computational fluid dynamics model is investigated to analyze this phase.The compu-tational domain size is adaptively refined according to the final aerial trajectory state,where the redundant computational domain is removed.An entire trajectory optimization problem is then formulated to maximize the total flight range via tuning the joint states of different trajectories.Simultaneously,several constraints,i.e.,the max impact load,trajectory height,etc.,are involved in the optimization problem.Rather than directly optimizing by a heuristic algorithm,a multi-surrogate cooperative sampling-based optimization method is proposed to alleviate the computational complexity of the entire trajectory optimization problem.In this method,various surrogates coopera-tively generate infill sample points,thereby preventing the poor approximation.After optimization,the total flight range can be improved by 20%,while all the constraints are satisfied.The result demonstrates the effectiveness and practicability of the developed model and optimization framework.展开更多
Low collateral damage weapons achieve controlled personnel injury through the coupling of shock waves and particle swarms,where the particle swarms arise from the high-explosive dispersion of compacted metal particle ...Low collateral damage weapons achieve controlled personnel injury through the coupling of shock waves and particle swarms,where the particle swarms arise from the high-explosive dispersion of compacted metal particle ring.To investigate the dynamic response of the human target under combined shock waves and particle swarms loading,a physical human surrogate torso model(HSTM)was developed,and the dynamic response test experiment was conducted under the combined loading.The effects of particle size on the loading parameters,the damage patterns of the ballistic plate and HSTM,and the dynamic response parameters of the HSTM with and without protection are mainly analyzed.Our findings revealed that particle swarms can effectively delay the shock wave attenuation,especially the best effect when the particle size was 0.28–0.45 mm.The ballistic plate mainly exhibited dense perforation of the outer fabric and impacted crater damage of ceramic plates,whereas the unprotected HSTM was mainly dominated by high-density and small-size ballistic cavity group damage.The peak values of the dynamic response parameters for the HSTM under combined loading were significantly larger than those under bare charge loading,with multiple peaks observed.Under unprotected conditions,the peak acceleration of skeletons and peak pressure of organs increased with the particle size.Under protected conditions,the particle size,the number of particles hit,and the fit of the ballistic plate to the HSTM together affected the dynamic response parameters of the HSTM.展开更多
Surrogate models have shown to be effective in assisting evolutionary algorithms(EAs)for solving computationally expensive complex optimization problems.However,the effectiveness of the existing surrogate-assisted evo...Surrogate models have shown to be effective in assisting evolutionary algorithms(EAs)for solving computationally expensive complex optimization problems.However,the effectiveness of the existing surrogate-assisted evolutionary algorithms still needs to be improved.A data-driven evolutionary sampling optimization(DESO)framework is proposed,where at each generation it randomly employs one of two evolutionary sampling strategies,surrogate screening and surrogate local search based on historical data,to effectively balance global and local search.In DESO,the radial basis function(RBF)is used as the surrogate model in the sampling strategy,and different degrees of the evolutionary process are used to sample candidate points.The sampled points by sampling strategies are evaluated,and then added into the database for the updating surrogate model and population in the next sampling.To get the insight of DESO,extensive experiments and analysis of DESO have been performed.The proposed algorithm presents superior computational efficiency and robustness compared with five state-of-the-art algorithms on benchmark problems from 20 to 200 dimensions.Besides,DESO is applied to an airfoil design problem to show its effectiveness.展开更多
In order to reduce the computation of complex problems, a new surrogate-assisted estimation of distribution algorithm with Gaussian process was proposed. Coevolution was used in dual populations which evolved in paral...In order to reduce the computation of complex problems, a new surrogate-assisted estimation of distribution algorithm with Gaussian process was proposed. Coevolution was used in dual populations which evolved in parallel. The search space was projected into multiple subspaces and searched by sub-populations. Also, the whole space was exploited by the other population which exchanges information with the sub-populations. In order to make the evolutionary course efficient, multivariate Gaussian model and Gaussian mixture model were used in both populations separately to estimate the distribution of individuals and reproduce new generations. For the surrogate model, Gaussian process was combined with the algorithm which predicted variance of the predictions. The results on six benchmark functions show that the new algorithm performs better than other surrogate-model based algorithms and the computation complexity is only 10% of the original estimation of distribution algorithm.展开更多
For the safety protection of passengers when train crashes occur, special structures are crucially needed as a kind of indispensable energy absorbing device. With the help of the structures, crash kinetic-energy can b...For the safety protection of passengers when train crashes occur, special structures are crucially needed as a kind of indispensable energy absorbing device. With the help of the structures, crash kinetic-energy can be completely absorbed or dissipated for the aim of safety. Two composite structures(circumscribed circle structure and inscribed circle structure) were constructed. In addition, comparison and optimization of the crashworthy characteristic of the two structures were carried out based on the method of explicit finite element analysis(FEA) and Kriging surrogate model. According to the result of Kriging surrogate model, conclusions can be safely drawn that the specific energy absorption(SEA) and ratio of specific energy absorption to initial peak force(REAF) of circumscribed circle structure are lager than those of inscribed circle structure under the same design parameters. In other words, circumscribed circle structure has better performances with higher energy-absorbing ability and lower initial peak force. Besides, error analysis was adopted and the result of which indicates that the Kriging surrogate model has high nonlinear fitting precision. What is more, the SEA and REAF optimum values of the two structures have been obtained through analysis, and the crushing results have been illustrated when the two structures reach optimum SEA and REAF.展开更多
The Next Generation Subsea Production System(NextGen SPS)is an innovative concept for petroleum development in ultra-deep water areas,mainly consisting of artificial seabed(AS),rigid ris-ers,flexible jumpers and moori...The Next Generation Subsea Production System(NextGen SPS)is an innovative concept for petroleum development in ultra-deep water areas,mainly consisting of artificial seabed(AS),rigid ris-ers,flexible jumpers and mooring lines.To improve the overall performance and design efficiency of NextGen SPS,an integrated design approach for the NextGen SPS based on multidisciplinary design optimization(MDO)method was investigated in this paper by combing the multidisciplinary feasible(MDF)architecture and particle swarm optimization(PSO)algorithm to establish the design frame-work.Two sub-disciplines of hydrodynamic analysis and global performance analysis were defined,and analysis method in each sub-discipline was introduced.Surrogate models of hydrodynamic analy-sis and global performance analysis were developed by using Latin hypercube sampling method and back propagation neural network(BPNN).Surrogate models were incorporated into the design frame-work,through which an integrated design for NextGen SPS at a depth of 3000 m was implemented.It is concluded that both the overall performance and the design efficiency of NextGen SPS are improved.展开更多
In chaotic cryptosysterns, using （very） high dimensional chaotic attractors for encrypting a given message maybe can improve the privacy of chaotic encoding. A kind of hyperchaotic systems are studied by using some ...In chaotic cryptosysterns, using （very） high dimensional chaotic attractors for encrypting a given message maybe can improve the privacy of chaotic encoding. A kind of hyperchaotic systems are studied by using some classical methods. The results show that for improving the security of the chaotic cryptosystems, besides the high dimension, the sub-Nyquist sampling interval （SI） is also necessary. Then, we verify this result using the methods of time series analysis.展开更多
基金Project(52108433)supported by the National Natural Science Foundation of ChinaProject(HSR202004)supported by the Open Foundation of National Engineering Research Center of High-Speed Railway Construction Technology(CSU),China+3 种基金Projects(2024RC3170,2021RC4031)supported by the Science and Technology Innovation Program of Hunan Province,ChinaProjects(2024JJ5018,2024JJ5427)supported by the Hunan Provincial Natural Science Foundation,ChinaProject(KQ2402027)supported by the Changsha City Natural Science Foundation,ChinaProjects(2021-Special-08,2022-Special-09)supported by the Science and Technology Research and Development Program Project of China Railway Group Limited。
文摘This paper proposed a RIME-VMD-BiLSTM surrogate model to rapidly and precisely predict the seismic response of a nonlinear vehicle-track-bridge(VTB)system.The surrogate model employs the RIME algorithm to optimize the variational mode decomposition(VMD)parameters(k and α)and the architecture and hyperparameter of the bidirectional long-and short-term memory network(BiLSTM).After comparing different combinations and optimization algorithms,the surrogate model was trained and used to analyze a typical 9-span 32-m high-speed railway simply supported bridge system.A series of numerical examples considering the vehicle speed,bridge damping,seismic intensity,and training strategy on the prediction effect of the surrogate model were conducted on the extended OpenSees platform.The results show that the BiLSTM model performed better than the LSTM model,whereas the prediction effects of the single-LSTM and BiLSTM models were relatively poor.With the introduction of the VMD and RIME optimization techniques,the prediction effect of the proposed RIME-VMD-BiLSTM model was excellent.The abovementioned factors had a significant influence on the seismic response of a VTB system but little impact on the prediction effect of the surrogate model.The proposed surrogate model exhibits notable transferability and robustness for predicting the VTB’s nonlinear seismic response.
基金Project(U1334208)supported by the National Natural Science Foundation of ChinaProject(2013GK2001)supported by the Fund of Hunan Provincial Science and Technology Department,China
文摘In order to optimize the crashworthy characteristic of energy-absorbing structures, the surrogate models of specific energy absorption （SEA） and ratio of SEA to initial peak force （REAF） with respect to the design parameters were respectively constructed based on surrogate model optimization methods （polynomial response surface method （PRSM） and Kriging method （KM））. Firstly, the sample data were prepared through the design of experiment （DOE）. Then, the test data models were set up based on the theory of surrogate model, and the data samples were trained to obtain the response relationship between the SEA ＆amp; REAF and design parameters. At last, the structure optimal parameters were obtained by visual analysis and genetic algorithm （GA）. The results indicate that the KM, where the local interpolation method is used in Gauss correlation function, has the highest fitting accuracy and the structure optimal parameters are obtained as： the SEA of 29.8558 kJ/kg （corresponding toa=70 mm andt= 3.5 mm） and REAF of 0.2896 （corresponding toa=70 mm andt=1.9615 mm）. The basis function of the quartic PRSM with higher order than that of the quadratic PRSM, and the mutual influence of the design variables are considered, so the fitting accuracy of the quartic PRSM is higher than that of the quadratic PRSM.
基金supported by the National Key Research and Development Program(2021YFB3502500).
文摘Multi-objective optimization(MOO)for the microwave metamaterial absorber(MMA)normally adopts evolutionary algo-rithms,and these optimization algorithms require many objec-tive function evaluations.To remedy this issue,a surrogate-based MOO algorithm is proposed in this paper where Kriging models are employed to approximate objective functions.An efficient sampling strategy is presented to sequentially capture promising samples in the design region for exact evaluations.Firstly,new sample points are generated by the MOO on surro-gate models.Then,new samples are captured by exploiting each objective function.Furthermore,a weighted sum of the improvement of hypervolume(IHV)and the distance to sampled points is calculated to select the new sample.Compared with two well-known MOO algorithms,the proposed algorithm is vali-dated by benchmark problems.In addition,two broadband MMAs are applied to verify the feasibility and efficiency of the proposed algorithm.
基金The project supported by grants from the Academic Research Fund Tier 1(R-184-000-228-112)the Cancer Science Institute of Singapore,Experimental Therapeutics I Program(grant R-713-001-011-271)
文摘OBJECTIVE To evaluate if RNA helicase DDX20,highly expressed in triple negative breast cancer(TNBC)cells,could serve as a surrogate marker for simvastatin treatment response.METHODS We first assessed correlation between 17 mevalonate pathway-related genes and expression of DDX20 in a cohort of 1325 breast cancer tumors.TNBC cells,MDA-MB-231,were then treated with simvastatin and mevalonate pathway intermediates to assess the alteration in DDX20 expression.In the mouse model,MDA-MB-231 cells were injected to tail veins of mice,groups of 8mice each were injected intraperioneally with vehicle or simvastatin 25mg·kg-1 3times a week for 6weeks.The number of metastatic colonies formed was quantified and immunohistochemical(IHC)staining of DDX20 was carried out in the lung tissues.RESULTS Among the 17 genes evaluated,positive correlation with DDX20 expression was observed in eight of them,with HMGCR having the highest correlation.Our in vitro experiments show exposure of breast cancer cells to simvastatin lead to a Rho-dependent decrease in gene expression of DDX20,leading to decreased tumor proliferation in a mevalonate pathway-dependent manner.Conversely,ectopic overexpression of DDX20 significantly abrogated the anti-metastatic activity of simvastatin.A similar observation is seen in the mouse model,where simvastatin-injected mice show significantly fewer visible lung metastases compared to placebo-fed mice.IHC staining on these lung tissues showed decreased DDX20 expression in simvastatin-injected group,corroborating our observations in vitro.CONCLUSION DDX20 is a potential surrogate marker for simvastatin treatment response in breast cancer and a long term implication of our findings is the possibility of an effective combinatorial therapeutic intervention using statins(to suppress DDX20 gene expression)and a suitable firstline agent″for the kill″of invasive breast cancer.
基金Supported by the National Key Research and Development Program of China(2022YFB3904803)。
文摘The Infrared Hyperspectral Atmospheric SounderⅡ(HIRAS-Ⅱ)is the key equipment on FengYun-3E(FY-3E)satellite,which can realize vertical atmospheric detection,featuring hyper spectral,high sensitivity and high precision.To ensure its accuracy of detection,it is necessary to correlate their thermal models to in-orbit da⁃ta.In this work,an investigation of intelligent correlation method named Intelligent Correlation Platform for Ther⁃mal Model(ICP-TM)was established,the advanced Kriging surrogate model and efficient adaptive region opti⁃mization algorithm were introduced.After the correlation with this method for FY-3E/HIRAS-Ⅱ,the results indi⁃cate that compared with the data in orbit,the error of the thermal model has decreased from 5 K to within±1 K in cold case(10℃).Then,the correlated model is validated in hot case(20℃),and the correlated model exhibits good universality.This correlation precision is also much superiors to the general ones like 3 K in other similar lit⁃erature.Furthermore,the process is finished in 8 days using ICP-TM,the efficiency is much better than 3 months based on manual.The results show that the proposed approach significantly enhances the accuracy and efficiency of thermal model,this contributes to the precise thermal control of subsequent infrared optical payloads.
基金supported by the National Natural Science Foundation of China(Grant Nos.52425211,52272360,and 52472394)Chongqing Natural Science Foundation(CSTB2023NSCQ-MSX0300)。
文摘Trans-medium flight vehicles can combine high aerial maneuverability and underwater concealment ability,which have attracted much attention recently.As the most crucial procedure,the trajectory design generally determines the trans-medium flight vehicle performance.To quantitatively analyze the flight vehicle performance,an entire aerial-aquatic trajectory model is developed in this paper.Different from modeling a trajectory purely for the water entry process,the constructed entire trajectory model has integrated aerial,water entry,and underwater trajectories together,which can consider the influence of the connected trajectories.As for the aerial and underwater trajectories,explicit dynamic models are established to obtain the trajectory parameters.Due to the complicated fluid force during high-velocity water entry,a computational fluid dynamics model is investigated to analyze this phase.The compu-tational domain size is adaptively refined according to the final aerial trajectory state,where the redundant computational domain is removed.An entire trajectory optimization problem is then formulated to maximize the total flight range via tuning the joint states of different trajectories.Simultaneously,several constraints,i.e.,the max impact load,trajectory height,etc.,are involved in the optimization problem.Rather than directly optimizing by a heuristic algorithm,a multi-surrogate cooperative sampling-based optimization method is proposed to alleviate the computational complexity of the entire trajectory optimization problem.In this method,various surrogates coopera-tively generate infill sample points,thereby preventing the poor approximation.After optimization,the total flight range can be improved by 20%,while all the constraints are satisfied.The result demonstrates the effectiveness and practicability of the developed model and optimization framework.
文摘Low collateral damage weapons achieve controlled personnel injury through the coupling of shock waves and particle swarms,where the particle swarms arise from the high-explosive dispersion of compacted metal particle ring.To investigate the dynamic response of the human target under combined shock waves and particle swarms loading,a physical human surrogate torso model(HSTM)was developed,and the dynamic response test experiment was conducted under the combined loading.The effects of particle size on the loading parameters,the damage patterns of the ballistic plate and HSTM,and the dynamic response parameters of the HSTM with and without protection are mainly analyzed.Our findings revealed that particle swarms can effectively delay the shock wave attenuation,especially the best effect when the particle size was 0.28–0.45 mm.The ballistic plate mainly exhibited dense perforation of the outer fabric and impacted crater damage of ceramic plates,whereas the unprotected HSTM was mainly dominated by high-density and small-size ballistic cavity group damage.The peak values of the dynamic response parameters for the HSTM under combined loading were significantly larger than those under bare charge loading,with multiple peaks observed.Under unprotected conditions,the peak acceleration of skeletons and peak pressure of organs increased with the particle size.Under protected conditions,the particle size,the number of particles hit,and the fit of the ballistic plate to the HSTM together affected the dynamic response parameters of the HSTM.
基金supported by the National Natural Science Foundation of China(62076225,62073300)the Natural Science Foundation for Distinguished Young Scholars of Hubei(2019CFA081)the Fundamental Research Funds for the Central Universities,China University of Geosciences(Wuhan)(CUGGC03).
文摘Surrogate models have shown to be effective in assisting evolutionary algorithms(EAs)for solving computationally expensive complex optimization problems.However,the effectiveness of the existing surrogate-assisted evolutionary algorithms still needs to be improved.A data-driven evolutionary sampling optimization(DESO)framework is proposed,where at each generation it randomly employs one of two evolutionary sampling strategies,surrogate screening and surrogate local search based on historical data,to effectively balance global and local search.In DESO,the radial basis function(RBF)is used as the surrogate model in the sampling strategy,and different degrees of the evolutionary process are used to sample candidate points.The sampled points by sampling strategies are evaluated,and then added into the database for the updating surrogate model and population in the next sampling.To get the insight of DESO,extensive experiments and analysis of DESO have been performed.The proposed algorithm presents superior computational efficiency and robustness compared with five state-of-the-art algorithms on benchmark problems from 20 to 200 dimensions.Besides,DESO is applied to an airfoil design problem to show its effectiveness.
基金Project(2009CB320603)supported by the National Basic Research Program of ChinaProject(IRT0712)supported by Program for Changjiang Scholars and Innovative Research Team in University+1 种基金Project(B504)supported by the Shanghai Leading Academic Discipline ProgramProject(61174118)supported by the National Natural Science Foundation of China
文摘In order to reduce the computation of complex problems, a new surrogate-assisted estimation of distribution algorithm with Gaussian process was proposed. Coevolution was used in dual populations which evolved in parallel. The search space was projected into multiple subspaces and searched by sub-populations. Also, the whole space was exploited by the other population which exchanges information with the sub-populations. In order to make the evolutionary course efficient, multivariate Gaussian model and Gaussian mixture model were used in both populations separately to estimate the distribution of individuals and reproduce new generations. For the surrogate model, Gaussian process was combined with the algorithm which predicted variance of the predictions. The results on six benchmark functions show that the new algorithm performs better than other surrogate-model based algorithms and the computation complexity is only 10% of the original estimation of distribution algorithm.
基金Projects(51405516,U1334208)supported by the National Natural Science Foundation of ChinaProject(2013GK2001)supported by the Science and Technology Program for Hunan Provincial Science and Technology Department,ChinaProject(2013zzts040)supported by the Graduate Degree Thesis Innovation Foundation of Central South University,China
文摘For the safety protection of passengers when train crashes occur, special structures are crucially needed as a kind of indispensable energy absorbing device. With the help of the structures, crash kinetic-energy can be completely absorbed or dissipated for the aim of safety. Two composite structures(circumscribed circle structure and inscribed circle structure) were constructed. In addition, comparison and optimization of the crashworthy characteristic of the two structures were carried out based on the method of explicit finite element analysis(FEA) and Kriging surrogate model. According to the result of Kriging surrogate model, conclusions can be safely drawn that the specific energy absorption(SEA) and ratio of specific energy absorption to initial peak force(REAF) of circumscribed circle structure are lager than those of inscribed circle structure under the same design parameters. In other words, circumscribed circle structure has better performances with higher energy-absorbing ability and lower initial peak force. Besides, error analysis was adopted and the result of which indicates that the Kriging surrogate model has high nonlinear fitting precision. What is more, the SEA and REAF optimum values of the two structures have been obtained through analysis, and the crushing results have been illustrated when the two structures reach optimum SEA and REAF.
文摘The Next Generation Subsea Production System(NextGen SPS)is an innovative concept for petroleum development in ultra-deep water areas,mainly consisting of artificial seabed(AS),rigid ris-ers,flexible jumpers and mooring lines.To improve the overall performance and design efficiency of NextGen SPS,an integrated design approach for the NextGen SPS based on multidisciplinary design optimization(MDO)method was investigated in this paper by combing the multidisciplinary feasible(MDF)architecture and particle swarm optimization(PSO)algorithm to establish the design frame-work.Two sub-disciplines of hydrodynamic analysis and global performance analysis were defined,and analysis method in each sub-discipline was introduced.Surrogate models of hydrodynamic analy-sis and global performance analysis were developed by using Latin hypercube sampling method and back propagation neural network(BPNN).Surrogate models were incorporated into the design frame-work,through which an integrated design for NextGen SPS at a depth of 3000 m was implemented.It is concluded that both the overall performance and the design efficiency of NextGen SPS are improved.
基金This project was supported by National"985"Engineering of China .
文摘In chaotic cryptosysterns, using （very） high dimensional chaotic attractors for encrypting a given message maybe can improve the privacy of chaotic encoding. A kind of hyperchaotic systems are studied by using some classical methods. The results show that for improving the security of the chaotic cryptosystems, besides the high dimension, the sub-Nyquist sampling interval （SI） is also necessary. Then, we verify this result using the methods of time series analysis.
