This research paper presents a comprehensive investigation into the effectiveness of the DeepSurNet-NSGA II(Deep Surrogate Model-Assisted Non-dominated Sorting Genetic Algorithm II)for solving complex multiobjective o...This research paper presents a comprehensive investigation into the effectiveness of the DeepSurNet-NSGA II(Deep Surrogate Model-Assisted Non-dominated Sorting Genetic Algorithm II)for solving complex multiobjective optimization problems,with a particular focus on robotic leg-linkage design.The study introduces an innovative approach that integrates deep learning-based surrogate models with the robust Non-dominated Sorting Genetic Algorithm II,aiming to enhance the efficiency and precision of the optimization process.Through a series of empirical experiments and algorithmic analyses,the paper demonstrates a high degree of correlation between solutions generated by the DeepSurNet-NSGA II and those obtained from direct experimental methods,underscoring the algorithm’s capability to accurately approximate the Pareto-optimal frontier while significantly reducing computational demands.The methodology encompasses a detailed exploration of the algorithm’s configuration,the experimental setup,and the criteria for performance evaluation,ensuring the reproducibility of results and facilitating future advancements in the field.The findings of this study not only confirm the practical applicability and theoretical soundness of the DeepSurNet-NSGA II in navigating the intricacies of multi-objective optimization but also highlight its potential as a transformative tool in engineering and design optimization.By bridging the gap between complex optimization challenges and achievable solutions,this research contributes valuable insights into the optimization domain,offering a promising direction for future inquiries and technological innovations.展开更多
Objectives Renal replacement therapy(RRT)is increasingly adopted for critically ill patients diagnosed with acute kidney injury,but the optimal time for initiation remains unclear and prognosis is uncertain,leading to...Objectives Renal replacement therapy(RRT)is increasingly adopted for critically ill patients diagnosed with acute kidney injury,but the optimal time for initiation remains unclear and prognosis is uncertain,leading to medical complexity,ethical conflicts,and decision dilemmas in intensive care unit(ICU)settings.This study aimed to develop a decision aid(DA)for the family surrogate of critically ill patients to support their engagement in shared decision-making process with clinicians.Methods Development of DA employed a systematic process with user-centered design(UCD)principle,which included:(i)competitive analysis:searched,screened,and assessed the existing DAs to gather insights for design strategies,developmental techniques,and functionalities;(ii)user needs assessment:interviewed family surrogates in our hospital to explore target user group's decision-making experience and identify their unmet needs;(iii)evidence syntheses:integrate latest clinical evidence and pertinent information to inform the content development of DA.Results The competitive analysis included 16 relevant DAs,from which we derived valuable insights using existing resources.User decision needs were explored among a cohort of 15 family surrogates,revealing four thematic issues in decision-making,including stuck into dilemmas,sense of uncertainty,limited capacity,and delayed decision confirmation.A total of 27 articles were included for evidence syntheses.Relevant decision making knowledge on disease and treatment,as delineated in the literature sourced from decision support system or clinical guidelines,were formatted as the foundational knowledge base.Twenty-one items of evidence were extracted and integrated into the content panels of benefits and risks of RRT,possible outcomes,and reasons to choose.The DA was drafted into a web-based phototype using the elements of UCD.This platform could guide users in their preparation of decision-making through a sequential four-step process:identifying treatment options,weighing the benefits and risks,clarifying personal preferences and values,and formulating a schedule for formal shared decision-making with clinicians.Conclusions We developed a rapid prototype of DA tailored for family surrogate decision makers of critically ill patients in need of RRT in ICU setting.Future studies are needed to evaluate its usability,feasibility,and clinical effects of this intervention.展开更多
Polymer electrolyte membrane fuel cells(PEMFCs)are considered a promising alternative to internal combustion engines in the automotive sector.Their commercialization is mainly hindered due to the cost and effectivenes...Polymer electrolyte membrane fuel cells(PEMFCs)are considered a promising alternative to internal combustion engines in the automotive sector.Their commercialization is mainly hindered due to the cost and effectiveness of using platinum(Pt)in them.The cathode catalyst layer(CL)is considered a core component in PEMFCs,and its composition often considerably affects the cell performance(V_(cell))also PEMFC fabrication and production(C_(stack))costs.In this study,a data-driven multi-objective optimization analysis is conducted to effectively evaluate the effects of various cathode CL compositions on Vcelland Cstack.Four essential cathode CL parameters,i.e.,platinum loading(L_(Pt)),weight ratio of ionomer to carbon(wt_(I/C)),weight ratio of Pt to carbon(wt_(Pt/c)),and porosity of cathode CL(ε_(cCL)),are considered as the design variables.The simulation results of a three-dimensional,multi-scale,two-phase comprehensive PEMFC model are used to train and test two famous surrogates:multi-layer perceptron(MLP)and response surface analysis(RSA).Their accuracies are verified using root mean square error and adjusted R^(2).MLP which outperforms RSA in terms of prediction capability is then linked to a multi-objective non-dominated sorting genetic algorithmⅡ.Compared to a typical PEMFC stack,the results of the optimal study show that the single-cell voltage,Vcellis improved by 28 m V for the same stack price and the stack cost evaluated through the U.S department of energy cost model is reduced by$5.86/k W for the same stack performance.展开更多
In recent years, spiking neural networks(SNNs) have received increasing attention of research in the field of artificial intelligence due to their high biological plausibility, low energy consumption, and abundant spa...In recent years, spiking neural networks(SNNs) have received increasing attention of research in the field of artificial intelligence due to their high biological plausibility, low energy consumption, and abundant spatio-temporal information.However, the non-differential spike activity makes SNNs more difficult to train in supervised training. Most existing methods focusing on introducing an approximated derivative to replace it, while they are often based on static surrogate functions. In this paper, we propose a progressive surrogate gradient learning for backpropagation of SNNs, which is able to approximate the step function gradually and to reduce information loss. Furthermore, memristor cross arrays are used for speeding up calculation and reducing system energy consumption for their hardware advantage. The proposed algorithm is evaluated on both static and neuromorphic datasets using fully connected and convolutional network architecture, and the experimental results indicate that our approach has a high performance compared with previous research.展开更多
The temperature response calculation of thermal protection materials,especially ablative thermal protection materials,usually adopts the ablation model,which is complicated in process and requires a large amount of ca...The temperature response calculation of thermal protection materials,especially ablative thermal protection materials,usually adopts the ablation model,which is complicated in process and requires a large amount of calculation.Especially in the process of optimization calculation and parameter identification,the ablation model needs to be called many times,so it is necessary to construct an ablation surrogate model to improve the computational efficiency under the premise of ensuring the accuracy.In this paper,the Gaussian process model method is used to construct a thermal protection material ablation surrogate model,and the prediction accuracy of the surrogate model is improved through optimization.展开更多
To reduce the high computational cost of the uncertainty analysis, a procedure is proposed for the aerodynamic optimization under uncertainties, in which the surrogate model is used to simplify the computation of the ...To reduce the high computational cost of the uncertainty analysis, a procedure is proposed for the aerodynamic optimization under uncertainties, in which the surrogate model is used to simplify the computation of the uncertainty analysis. The surrogate model is constructed by using the Latin Hypercube design and the Kriging model. The random parameters are used to account for the small manufacturing errors and the variations of operating conditions. Based on the surrogate model, an uncertainty analysis approach, called the Monte Carlo simulation, is used to compute the mean value and the variance of the predicated performance. The robust optimization for aerodynamic design is formulated, and solved by the genetic algorithm. And then, an airfoil optimization problem is used to test the proposed procedure. Results show that the optimal solutions obtained from the uncertainty-based optimization formulation are less sensitive to uncertainties. And the design constraints are still satisfied under the uncertainties.展开更多
As a promising technique, surrogate-based design and optimization（SBDO） has been widely used in modern engineering design optimizations. Currently, static surrogate-based optimization methods have been successfully ...As a promising technique, surrogate-based design and optimization（SBDO） has been widely used in modern engineering design optimizations. Currently, static surrogate-based optimization methods have been successfully applied to expensive optimization problems. However, due to the low efficiency and poor flexibility, static surrogate-based optimization methods are difficult to efficiently solve practical engineering cases. At the aim of enhancing efficiency, a novel surrogate-based efficient optimization method is developed by using sequential radial basis function（SEO-SRBF）. Moreover, augmented Lagrangian multiplier method is adopted to solve the problems involving expensive constraints. In order to study the performance of SEO-SRBF, several numerical benchmark functions and engineering problems are solved by SEO-SRBF and other well-known surrogate-based optimization methods including EGO, MPS, and IARSM. The optimal solutions, number of function evaluations, and algorithm execution time are recorded for comparison. The comparison results demonstrate that SEO-SRBF shows satisfactory performance in both optimization efficiency and global convergence capability. The CPU time required for running SEO-SRBF is dramatically less than that of other algorithms. In the torque arm optimization case using FEA simulation, SEO-SRBF further reduces 21% of thematerial volume compared with the solution from static-RBF subject to the stress constraint. This study provides the efficient strategy to solve expensive constrained optimization problems.展开更多
For multi-objective optimization problems, particle swarm optimization(PSO) algorithm generally needs a large number of fitness evaluations to obtain the Pareto optimal solutions. However, it will become substantially...For multi-objective optimization problems, particle swarm optimization(PSO) algorithm generally needs a large number of fitness evaluations to obtain the Pareto optimal solutions. However, it will become substantially time-consuming when handling computationally expensive fitness functions. In order to save the computational cost, a surrogate-assisted PSO with Pareto active learning is proposed. In real physical space(the objective functions are computationally expensive), PSO is used as an optimizer, and its optimization results are used to construct the surrogate models. In virtual space, objective functions are replaced by the cheaper surrogate models, PSO is viewed as a sampler to produce the candidate solutions. To enhance the quality of candidate solutions, a hybrid mutation sampling method based on the simulated evolution is proposed, which combines the advantage of fast convergence of PSO and implements mutation to increase diversity. Furthermore, ε-Pareto active learning(ε-PAL)method is employed to pre-select candidate solutions to guide PSO in the real physical space. However, little work has considered the method of determining parameter ε. Therefore, a greedy search method is presented to determine the value ofεwhere the number of active sampling is employed as the evaluation criteria of classification cost. Experimental studies involving application on a number of benchmark test problems and parameter determination for multi-input multi-output least squares support vector machines(MLSSVM) are given, in which the results demonstrate promising performance of the proposed algorithm compared with other representative multi-objective particle swarm optimization(MOPSO) algorithms.展开更多
Many nature reserves are established to protect the habitat needs of particular endangered species of interest but their effectiveness for protecting other species is questionable.In this study,this effectiveness was ...Many nature reserves are established to protect the habitat needs of particular endangered species of interest but their effectiveness for protecting other species is questionable.In this study,this effectiveness was evaluated in a nature reserve network located in the Qinling Mountains,Shaanxi Province,China.The network of reserves was established mainly for the conservation of the giant panda,a species considered as a surrogate for the conservation of many other endangered species in the region.The habitat suitability of nine protected species,including the giant panda,was modeled by using Maximum Entropy(MAXENT)and their spatial congruence was analyzed.Habitat suitability of these species was also overlapped with nature reserve boundaries and their management zones(i.e.,core,buffer and experimental zones).Results show that in general the habitat of the giant panda constitutes a reasonable surrogate of the habitat of other protected species,and giant panda reserves protect a relatively high proportion of the habitat of other protected species.Therefore,giant panda habitat conservation also allows the conservation of the habitat of other protected species in the region.However,a large area of suitable habitat was excluded from the nature reserve network.In addition,four species exhibited a low proportion of highly suitable habitat inside the core zones of nature reserves.It suggests that a high proportion of suitable habitat of protected species not targeted for conservation is located in the experimental and buffer zones,thus,is being affected by human activities.To increase their conservation effectiveness,nature reserves and their management zones need to be re-examined in order to include suitable habitat of more endangered species.The procedures described in this study can be easily implemented for the conservation of many endangered species not only in China but in many other parts of the world.展开更多
Structural health monitoring is important to ensuring the health and safety of dams.An inverse analysis method based on a novel hybrid fireworks algorithm (FWA) and the radial basis function (RBF) model is proposed to...Structural health monitoring is important to ensuring the health and safety of dams.An inverse analysis method based on a novel hybrid fireworks algorithm (FWA) and the radial basis function (RBF) model is proposed to diagnose the health condition of concrete dams.The damage of concrete dams is diagnosed by identifying the elastic modulus of materials using the displacement changes at different reservoir water levels.FWA is a global optimization intelligent algorithm.The proposed hybrid algorithm combines the FWA with the pattern search algorithm, which has a high capability for local optimization.Examples of benchmark functions and pseudo-experiment examples of concrete dams illustrate that the hybrid FWA improves the convergence speed and robustness of the original algorithm.To address the time consumption problem, an RBF-based surrogate model was established to replace part of the finite element method in inverse analysis.Numerical examples of concrete dams illustrate that the use of an RBF-based surrogate model significantly reduces the computation time of inverse analysis with little influence on identification accuracy.The presented hybrid FWA combined with the RBF network can quickly and accurately determine the elastic modulus of materials, and then determine the health status of the concrete dam.展开更多
Surrogate assisted optimization has been widely applied in sheet metal forming design due to its efficiency. Therefore, to improve the efficiency of design and reduce the product development cycle, it is important for...Surrogate assisted optimization has been widely applied in sheet metal forming design due to its efficiency. Therefore, to improve the efficiency of design and reduce the product development cycle, it is important for scholars and engineers to have some insight into the performance of each surrogate assisted optimization method and make them more flexible practically. For this purpose, the state-of-the-art surrogate assisted optimizations are investigated. Furthermore, in view of the bottleneck and development of the surrogate assisted optimization and sheet metal forming design, some important issues on the surrogate assisted optimization in support of the sheet metal forming design are analyzed and discussed, involving the description of the sheet metal forming design, off-line and online sampling strategies, space mapping algorithm, high dimensional problems, robust design, some challenges and potential feasible methods. Generally, this paper provides insightful observations into the performance and potential development of these methods in sheet metal forming design.展开更多
Under the influence of crosswinds,the running safety of trains will decrease sharply,so it is necessary to optimize the suspension parameters of trains.This paper studies the dynamic performance of high-speed trains u...Under the influence of crosswinds,the running safety of trains will decrease sharply,so it is necessary to optimize the suspension parameters of trains.This paper studies the dynamic performance of high-speed trains under cross-wind conditions,and optimizes the running safety of train.A computational fluid dynamics simulation was used to determine the aerodynamic loads and moments experienced by a train.A series of dynamic models of a train,with different dynamic parameters were constructed,and analyzed,with safety metrics for these being determined.Finally,a surrogate model was built and an optimization algorithm was used upon this surrogate model,to find the minimum possible values for:derailment coefficient,vertical wheel-rail contact force,wheel load reduction ratio,wheel lateral force and overturning coefficient.There were 9 design variables,all associated with the dynamic parameters of the bogie.When the train was running with the speed of 350 km/h,under a crosswind speed of 15 m/s,the benchmark dynamic model performed poorly.The derailment coefficient was 1.31.The vertical wheel-rail contact force was 133.30 kN.The wheel load reduction rate was 0.643.The wheel lateral force was 85.67 kN,and the overturning coefficient was 0.425.After optimization,under the same running conditions,the metrics of the train were 0.268,100.44 kN,0.474,34.36 kN,and 0.421,respectively.This paper show that by combining train aerodynamics,vehicle system dynamics and many-objective optimization theory,a train’s stability can be more comprehensively analyzed,with more safety metrics being considered.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
The response of the train–bridge system has an obvious random behavior.A high traffic density and a long maintenance period of a track will result in a substantial increase in the number of trains running on a bridge...The response of the train–bridge system has an obvious random behavior.A high traffic density and a long maintenance period of a track will result in a substantial increase in the number of trains running on a bridge,and there is small likelihood that the maximum responses of the train and bridge happen in the total maintenance period of the track.Firstly,the coupling model of train–bridge systems is reviewed.Then,an ensemble method is presented,which can estimate the small probabilities of a dynamic system with stochastic excitations.The main idea of the ensemble method is to use the NARX(nonlinear autoregressive with exogenous input)model to replace the physical model and apply subset simulation with splitting to obtain the extreme distribution.Finally,the efficiency of the suggested method is compared with the direct Monte Carlo simulation method,and the probability exceedance of train responses under the vertical track irregularity is discussed.The results show that when the small probability of train responses under vertical track irregularity is estimated,the ensemble method can reduce both the calculation time of a single sample and the required number of samples.展开更多
文摘This research paper presents a comprehensive investigation into the effectiveness of the DeepSurNet-NSGA II(Deep Surrogate Model-Assisted Non-dominated Sorting Genetic Algorithm II)for solving complex multiobjective optimization problems,with a particular focus on robotic leg-linkage design.The study introduces an innovative approach that integrates deep learning-based surrogate models with the robust Non-dominated Sorting Genetic Algorithm II,aiming to enhance the efficiency and precision of the optimization process.Through a series of empirical experiments and algorithmic analyses,the paper demonstrates a high degree of correlation between solutions generated by the DeepSurNet-NSGA II and those obtained from direct experimental methods,underscoring the algorithm’s capability to accurately approximate the Pareto-optimal frontier while significantly reducing computational demands.The methodology encompasses a detailed exploration of the algorithm’s configuration,the experimental setup,and the criteria for performance evaluation,ensuring the reproducibility of results and facilitating future advancements in the field.The findings of this study not only confirm the practical applicability and theoretical soundness of the DeepSurNet-NSGA II in navigating the intricacies of multi-objective optimization but also highlight its potential as a transformative tool in engineering and design optimization.By bridging the gap between complex optimization challenges and achievable solutions,this research contributes valuable insights into the optimization domain,offering a promising direction for future inquiries and technological innovations.
文摘Objectives Renal replacement therapy(RRT)is increasingly adopted for critically ill patients diagnosed with acute kidney injury,but the optimal time for initiation remains unclear and prognosis is uncertain,leading to medical complexity,ethical conflicts,and decision dilemmas in intensive care unit(ICU)settings.This study aimed to develop a decision aid(DA)for the family surrogate of critically ill patients to support their engagement in shared decision-making process with clinicians.Methods Development of DA employed a systematic process with user-centered design(UCD)principle,which included:(i)competitive analysis:searched,screened,and assessed the existing DAs to gather insights for design strategies,developmental techniques,and functionalities;(ii)user needs assessment:interviewed family surrogates in our hospital to explore target user group's decision-making experience and identify their unmet needs;(iii)evidence syntheses:integrate latest clinical evidence and pertinent information to inform the content development of DA.Results The competitive analysis included 16 relevant DAs,from which we derived valuable insights using existing resources.User decision needs were explored among a cohort of 15 family surrogates,revealing four thematic issues in decision-making,including stuck into dilemmas,sense of uncertainty,limited capacity,and delayed decision confirmation.A total of 27 articles were included for evidence syntheses.Relevant decision making knowledge on disease and treatment,as delineated in the literature sourced from decision support system or clinical guidelines,were formatted as the foundational knowledge base.Twenty-one items of evidence were extracted and integrated into the content panels of benefits and risks of RRT,possible outcomes,and reasons to choose.The DA was drafted into a web-based phototype using the elements of UCD.This platform could guide users in their preparation of decision-making through a sequential four-step process:identifying treatment options,weighing the benefits and risks,clarifying personal preferences and values,and formulating a schedule for formal shared decision-making with clinicians.Conclusions We developed a rapid prototype of DA tailored for family surrogate decision makers of critically ill patients in need of RRT in ICU setting.Future studies are needed to evaluate its usability,feasibility,and clinical effects of this intervention.
基金supported by the Technology Innovation Program of the Korea Evaluation Institute of Industrial Technology (KEIT)under the Ministry of Trade,Industry and Energy (MOTIE)of Republic of Korea (20012121)by the National Research Foundation of Korea (NRF)grant funded by the Korea government (MSIT) (2022M3J7A106294)。
文摘Polymer electrolyte membrane fuel cells(PEMFCs)are considered a promising alternative to internal combustion engines in the automotive sector.Their commercialization is mainly hindered due to the cost and effectiveness of using platinum(Pt)in them.The cathode catalyst layer(CL)is considered a core component in PEMFCs,and its composition often considerably affects the cell performance(V_(cell))also PEMFC fabrication and production(C_(stack))costs.In this study,a data-driven multi-objective optimization analysis is conducted to effectively evaluate the effects of various cathode CL compositions on Vcelland Cstack.Four essential cathode CL parameters,i.e.,platinum loading(L_(Pt)),weight ratio of ionomer to carbon(wt_(I/C)),weight ratio of Pt to carbon(wt_(Pt/c)),and porosity of cathode CL(ε_(cCL)),are considered as the design variables.The simulation results of a three-dimensional,multi-scale,two-phase comprehensive PEMFC model are used to train and test two famous surrogates:multi-layer perceptron(MLP)and response surface analysis(RSA).Their accuracies are verified using root mean square error and adjusted R^(2).MLP which outperforms RSA in terms of prediction capability is then linked to a multi-objective non-dominated sorting genetic algorithmⅡ.Compared to a typical PEMFC stack,the results of the optimal study show that the single-cell voltage,Vcellis improved by 28 m V for the same stack price and the stack cost evaluated through the U.S department of energy cost model is reduced by$5.86/k W for the same stack performance.
基金Project supported by the Natural Science Foundation of Chongqing(Grant No.cstc2021jcyj-msxmX0565)the Fundamental Research Funds for the Central Universities(Grant No.SWU021002)the Graduate Research Innovation Project of Chongqing(Grant No.CYS22242)。
文摘In recent years, spiking neural networks(SNNs) have received increasing attention of research in the field of artificial intelligence due to their high biological plausibility, low energy consumption, and abundant spatio-temporal information.However, the non-differential spike activity makes SNNs more difficult to train in supervised training. Most existing methods focusing on introducing an approximated derivative to replace it, while they are often based on static surrogate functions. In this paper, we propose a progressive surrogate gradient learning for backpropagation of SNNs, which is able to approximate the step function gradually and to reduce information loss. Furthermore, memristor cross arrays are used for speeding up calculation and reducing system energy consumption for their hardware advantage. The proposed algorithm is evaluated on both static and neuromorphic datasets using fully connected and convolutional network architecture, and the experimental results indicate that our approach has a high performance compared with previous research.
基金supported by Independent Research and Development Project of CASC(YF-ZZYF-2022-132)。
文摘The temperature response calculation of thermal protection materials,especially ablative thermal protection materials,usually adopts the ablation model,which is complicated in process and requires a large amount of calculation.Especially in the process of optimization calculation and parameter identification,the ablation model needs to be called many times,so it is necessary to construct an ablation surrogate model to improve the computational efficiency under the premise of ensuring the accuracy.In this paper,the Gaussian process model method is used to construct a thermal protection material ablation surrogate model,and the prediction accuracy of the surrogate model is improved through optimization.
文摘To reduce the high computational cost of the uncertainty analysis, a procedure is proposed for the aerodynamic optimization under uncertainties, in which the surrogate model is used to simplify the computation of the uncertainty analysis. The surrogate model is constructed by using the Latin Hypercube design and the Kriging model. The random parameters are used to account for the small manufacturing errors and the variations of operating conditions. Based on the surrogate model, an uncertainty analysis approach, called the Monte Carlo simulation, is used to compute the mean value and the variance of the predicated performance. The robust optimization for aerodynamic design is formulated, and solved by the genetic algorithm. And then, an airfoil optimization problem is used to test the proposed procedure. Results show that the optimal solutions obtained from the uncertainty-based optimization formulation are less sensitive to uncertainties. And the design constraints are still satisfied under the uncertainties.
基金Supported by National Natural Science Foundation of China (Grant Nos.51105040,11372036)Aeronautical Science Foundation of China (Grant Nos.2011ZA72003,2009ZA72002)+1 种基金Excellent Young Scholars Research Fund of Beijing Institute of Technology (Grant No.2010Y0102)Foundation Research Fund of Beijing Institute of Technology (Grant No.20130142008)
文摘As a promising technique, surrogate-based design and optimization（SBDO） has been widely used in modern engineering design optimizations. Currently, static surrogate-based optimization methods have been successfully applied to expensive optimization problems. However, due to the low efficiency and poor flexibility, static surrogate-based optimization methods are difficult to efficiently solve practical engineering cases. At the aim of enhancing efficiency, a novel surrogate-based efficient optimization method is developed by using sequential radial basis function（SEO-SRBF）. Moreover, augmented Lagrangian multiplier method is adopted to solve the problems involving expensive constraints. In order to study the performance of SEO-SRBF, several numerical benchmark functions and engineering problems are solved by SEO-SRBF and other well-known surrogate-based optimization methods including EGO, MPS, and IARSM. The optimal solutions, number of function evaluations, and algorithm execution time are recorded for comparison. The comparison results demonstrate that SEO-SRBF shows satisfactory performance in both optimization efficiency and global convergence capability. The CPU time required for running SEO-SRBF is dramatically less than that of other algorithms. In the torque arm optimization case using FEA simulation, SEO-SRBF further reduces 21% of thematerial volume compared with the solution from static-RBF subject to the stress constraint. This study provides the efficient strategy to solve expensive constrained optimization problems.
基金supported by the National Natural Sciences Foundation of China(61603069,61533005,61522304,U1560102)the National Key Research and Development Program of China(2017YFA0700300)
文摘For multi-objective optimization problems, particle swarm optimization(PSO) algorithm generally needs a large number of fitness evaluations to obtain the Pareto optimal solutions. However, it will become substantially time-consuming when handling computationally expensive fitness functions. In order to save the computational cost, a surrogate-assisted PSO with Pareto active learning is proposed. In real physical space(the objective functions are computationally expensive), PSO is used as an optimizer, and its optimization results are used to construct the surrogate models. In virtual space, objective functions are replaced by the cheaper surrogate models, PSO is viewed as a sampler to produce the candidate solutions. To enhance the quality of candidate solutions, a hybrid mutation sampling method based on the simulated evolution is proposed, which combines the advantage of fast convergence of PSO and implements mutation to increase diversity. Furthermore, ε-Pareto active learning(ε-PAL)method is employed to pre-select candidate solutions to guide PSO in the real physical space. However, little work has considered the method of determining parameter ε. Therefore, a greedy search method is presented to determine the value ofεwhere the number of active sampling is employed as the evaluation criteria of classification cost. Experimental studies involving application on a number of benchmark test problems and parameter determination for multi-input multi-output least squares support vector machines(MLSSVM) are given, in which the results demonstrate promising performance of the proposed algorithm compared with other representative multi-objective particle swarm optimization(MOPSO) algorithms.
基金Under the auspices of National Natural Science Foundation of China(No.40901289)Major State Basic Research Development Program of China(No.2009CB421104),U.S.National Science Foundation
文摘Many nature reserves are established to protect the habitat needs of particular endangered species of interest but their effectiveness for protecting other species is questionable.In this study,this effectiveness was evaluated in a nature reserve network located in the Qinling Mountains,Shaanxi Province,China.The network of reserves was established mainly for the conservation of the giant panda,a species considered as a surrogate for the conservation of many other endangered species in the region.The habitat suitability of nine protected species,including the giant panda,was modeled by using Maximum Entropy(MAXENT)and their spatial congruence was analyzed.Habitat suitability of these species was also overlapped with nature reserve boundaries and their management zones(i.e.,core,buffer and experimental zones).Results show that in general the habitat of the giant panda constitutes a reasonable surrogate of the habitat of other protected species,and giant panda reserves protect a relatively high proportion of the habitat of other protected species.Therefore,giant panda habitat conservation also allows the conservation of the habitat of other protected species in the region.However,a large area of suitable habitat was excluded from the nature reserve network.In addition,four species exhibited a low proportion of highly suitable habitat inside the core zones of nature reserves.It suggests that a high proportion of suitable habitat of protected species not targeted for conservation is located in the experimental and buffer zones,thus,is being affected by human activities.To increase their conservation effectiveness,nature reserves and their management zones need to be re-examined in order to include suitable habitat of more endangered species.The procedures described in this study can be easily implemented for the conservation of many endangered species not only in China but in many other parts of the world.
基金supported by the National Key Research and Development Program of China(Grants No.2016YFC0401600 and 2017YFC0404906)the National Natural Science Foundation of China(Grants No.51769033 and 51779035)the Fundamental Research Funds for the Central Universities(Grants No.DUT17ZD205 and DUT19LK14)
文摘Structural health monitoring is important to ensuring the health and safety of dams.An inverse analysis method based on a novel hybrid fireworks algorithm (FWA) and the radial basis function (RBF) model is proposed to diagnose the health condition of concrete dams.The damage of concrete dams is diagnosed by identifying the elastic modulus of materials using the displacement changes at different reservoir water levels.FWA is a global optimization intelligent algorithm.The proposed hybrid algorithm combines the FWA with the pattern search algorithm, which has a high capability for local optimization.Examples of benchmark functions and pseudo-experiment examples of concrete dams illustrate that the hybrid FWA improves the convergence speed and robustness of the original algorithm.To address the time consumption problem, an RBF-based surrogate model was established to replace part of the finite element method in inverse analysis.Numerical examples of concrete dams illustrate that the use of an RBF-based surrogate model significantly reduces the computation time of inverse analysis with little influence on identification accuracy.The presented hybrid FWA combined with the RBF network can quickly and accurately determine the elastic modulus of materials, and then determine the health status of the concrete dam.
基金Supported by National Natural Science Foundation of China(Grant Nos.11572120,11172097,11302266)
文摘Surrogate assisted optimization has been widely applied in sheet metal forming design due to its efficiency. Therefore, to improve the efficiency of design and reduce the product development cycle, it is important for scholars and engineers to have some insight into the performance of each surrogate assisted optimization method and make them more flexible practically. For this purpose, the state-of-the-art surrogate assisted optimizations are investigated. Furthermore, in view of the bottleneck and development of the surrogate assisted optimization and sheet metal forming design, some important issues on the surrogate assisted optimization in support of the sheet metal forming design are analyzed and discussed, involving the description of the sheet metal forming design, off-line and online sampling strategies, space mapping algorithm, high dimensional problems, robust design, some challenges and potential feasible methods. Generally, this paper provides insightful observations into the performance and potential development of these methods in sheet metal forming design.
基金Supported by The National Key Research and Development Program of China(Grant No.2020YFA0710902)The National Natural Science Foundation of China(Grant No.12172308)+1 种基金Sichuan Provincial Science and Technology Program of China(Grant No.2019YJ0227)State Key Laboratory of Traction Power of China(Grant No.2019TPL_T02).
文摘Under the influence of crosswinds,the running safety of trains will decrease sharply,so it is necessary to optimize the suspension parameters of trains.This paper studies the dynamic performance of high-speed trains under cross-wind conditions,and optimizes the running safety of train.A computational fluid dynamics simulation was used to determine the aerodynamic loads and moments experienced by a train.A series of dynamic models of a train,with different dynamic parameters were constructed,and analyzed,with safety metrics for these being determined.Finally,a surrogate model was built and an optimization algorithm was used upon this surrogate model,to find the minimum possible values for:derailment coefficient,vertical wheel-rail contact force,wheel load reduction ratio,wheel lateral force and overturning coefficient.There were 9 design variables,all associated with the dynamic parameters of the bogie.When the train was running with the speed of 350 km/h,under a crosswind speed of 15 m/s,the benchmark dynamic model performed poorly.The derailment coefficient was 1.31.The vertical wheel-rail contact force was 133.30 kN.The wheel load reduction rate was 0.643.The wheel lateral force was 85.67 kN,and the overturning coefficient was 0.425.After optimization,under the same running conditions,the metrics of the train were 0.268,100.44 kN,0.474,34.36 kN,and 0.421,respectively.This paper show that by combining train aerodynamics,vehicle system dynamics and many-objective optimization theory,a train’s stability can be more comprehensively analyzed,with more safety metrics being considered.
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This work was financially supported by the National Natural Science Foundation of China(Nos.51978589,51778544,and 51525804).
文摘The response of the train–bridge system has an obvious random behavior.A high traffic density and a long maintenance period of a track will result in a substantial increase in the number of trains running on a bridge,and there is small likelihood that the maximum responses of the train and bridge happen in the total maintenance period of the track.Firstly,the coupling model of train–bridge systems is reviewed.Then,an ensemble method is presented,which can estimate the small probabilities of a dynamic system with stochastic excitations.The main idea of the ensemble method is to use the NARX(nonlinear autoregressive with exogenous input)model to replace the physical model and apply subset simulation with splitting to obtain the extreme distribution.Finally,the efficiency of the suggested method is compared with the direct Monte Carlo simulation method,and the probability exceedance of train responses under the vertical track irregularity is discussed.The results show that when the small probability of train responses under vertical track irregularity is estimated,the ensemble method can reduce both the calculation time of a single sample and the required number of samples.
