DeepSurNet-NSGA II:Deep Surrogate Model-Assisted Multi-Objective Evolutionary Algorithm for Enhancing Leg Linkage in Walking Robots

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.

Development of a Decision Aid for Family Surrogate Decision Makers of Critically Ill Patients Requiring Renal Replacement Therapy in ICU:A User-Centered Design for Rapid Prototyping

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.

基于Surrogate模型的断言覆盖技术研究

随着集成电路设计规模不断增大,验证成为制约设计进程的瓶颈之一。目前,仿真仍是集成电路设计验证的主导方法之一,仿真的完备性通常通过各种覆盖率测度来度量。功能覆盖率是抽象层次较高的一种覆盖率,实际工程中,功能常以SystemVerilog断言形式呈现。目前常用的随机测试向量生成较难生成大量激活断言的测试向量;而采用约束求解的策略时,一旦覆盖条件中涉及到非初始输入信号(内部信号、输出信号),约束求解的效率将极为低下,导致仍然难以覆盖目标断言。针对含非初始输入信号断言的覆盖问题,提出了一种利用Surrogate模型的断言覆盖率提升方法,主要是为非初始输入信号生成体现其与初始输入信号关系的、只包含初始输入信号的Surrogate模型,再以此Surrogate模型作为约束求解的对象,降低了约束求解的复杂度。实验结果表明,相比于随机测试向量生成,该方法在断言覆盖方面有较大提升。

基于Surrogate方法的低速大扭矩门用电机机壳振动噪声优化

门用电机振动与噪声的研究对改善公共交通领域乘客体验有极大的促进作用.由于应用场景中机械电磁环境较为复杂,基于电磁力波计算的传统永磁电机振动噪声方法存在计算不精确、多物理场耦合优化模型不紧密等问题.本文提出一种基于Surrogate方法的分数槽永磁同步电机八边形机壳设计方案,通过有限元仿真获取先验数据,利用克雷金法重构Surrogate数学模型,经误差修正后,采用搜索算法对电机机壳振动噪声进行优化.结果表明,本文方法对多物理量耦合仿真优化具有较高的拟合精确度与较快的计算速度,具有良好的工程可行性.

基于Surrogate算法的永磁同步电机定子辅助槽参数优化

齿槽转矩是电机转矩的波动值,会导致电机的振动与噪声,在电机定子上开辅助凹槽可以有效抑制齿槽转矩,辅助槽的几何参数会影响辅助槽抑制齿槽转矩的效果,需要科学地选取辅助槽的各项参数.以某型12槽5对极内置式永磁同步电机为研究对象,首先采用能量法解析齿槽转矩,利用解析计算配合有限元的方法确定辅助槽个数;然后选取辅助槽槽宽、槽深、槽间距作为待优化参数,采用拉丁超立方抽样方法进行抽样,运用MagNet电磁仿真软件对抽样得到的120个样本点进行仿真,仿真结果用于Kriging模型的搭建,以该模型为基础采用Surrogate算法对辅助槽几何参数进行寻优.结果表明,基于本文方法得到的辅助槽方案削弱了永磁同步电机85.21%的齿槽转矩,反电动势正弦性增加谐波含量下降,电机性能明显改善.

Multi-objective optimization of the cathode catalyst layer micro-composition of polymer electrolyte membrane fuel cells using a multi-scale,two-phase fuel cell model and data-driven surrogates

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.

A progressive surrogate gradient learning for memristive spiking neural network

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.

Establishment and Optimization of Ablation Surrogate Model for Thermal Protection Material

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.

Jini技术Surrogate体系结构研究

文章首先在深入研究Jini技术工作机制的基础上,指出了Jini技术在应用中的局限性;然后介绍了Surrogate体系结构,从运行机制的角度剖析了其改进Jini技术局限性的机理;最后,通过讨论Surrogate体系结构的设计目标总结了其优越性。

ROBUST OPTIMIZATION OF AERODYNAMIC DESIGN USING SURROGATE MODEL

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.

基于Surrogate的预装式储能电站布局优化

为解决预装式储能电站内部布局优化的问题,同时兼顾集装箱内部通风散热效果最好与储能容量最大,提出一种基于Surrogate的预装式储能电站布局优化方案,以进、出风口半径、通风道宽度为决策变量,利用拉丁超立方抽样法生成样本,设计箱体内部设备排布方案及通风口方案,利用有限元软件ANSYSWorkbench仿真计算箱体内温度分布情况;基于热分析结果,使用Surrogate建模方法构建优化模型,采用粒子群算法求解优化模型,得到最佳布局及散热方案。最后,算例验证了方法的适用性。此方法的提出,解决了当前预装式储能电站优化方案中存在的主观性偏强或求解不优的问题,有利于推动预装式储能电站设计的进一步发展。

基于Surrogate优化建模方法的预装式氢储能电站结构布局优化

为探索预装式氢储能电站的散热布局问题,提出一种基于Surrogate算法的设计方法。以功率密度最大为目标,结合COMSOL Multiphysical有限元仿真软件与Surrogate算法对质子交换膜燃料电池(PEMFC)进行结构优化,明晰了PEMFC功率密度范围及其热负荷。基于此,在预装式氢储能电站箱体可行通风散热方案中筛选出最佳方案,以氢储能电站功率最大为目标,以通风散热效果为限制条件,以出风口几何尺寸与PEMFC功率密度为变量,结合Ansys CFX软件与Surrogate算法求解氢储能电站在最佳散热方案下的布局问题,并通过某公司氢储能电站进行了算例验证。验证结果表明:该设计方法解决了有限元模拟搜索法的算力高消耗与结果低通用性的问题,为工业界在多物理量变尺度设计问题上提供了一种有效的解决方案。

Surrogate-Assisted Particle Swarm Optimization Algorithm With Pareto Active Learning for Expensive Multi-Objective Optimization

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.

Sequential RBF Surrogate-based Efficient Optimization Method for Engineering Design Problems with Expensive Black-Box Functions

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.

Sheet Metal Forming Optimization by Using Surrogate Modeling Techniques

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.

Mechanical Fault Diagnosis Based on Band-phase-randomized Surrogate Data and Multifractal

The vibration signals of machinery with various faults often show clear nonlinear characteristics.Currently,fractal dimension analysis as the common useful method for nonlinear signal analysis,is a kind of single fractal form,which only reflects the overall irregularity of signals,but cannot describe its local scaling properties.For comprehensive revealing of internal properties,a combinatorial method based on band-phase-randomized（BPR） surrogate data and multifractal is introduced.BPR surrogate data method is effective to eliminate nonlinearity in specified frequency band for a fault signal,which can be utilized to detect nonlinear degree in whole fault signal by nonlinear titration method,and the overall nonlinear distribution of fault signal is displayed in nonlinear characteristic curve that can be used to analyze the fault signal qualitatively.Then multifractal theory as a quantitative analysis method is used to describe geometrical characteristics and local scaling properties,and asymmetry coefficient of multifractal spectrum and multifractal entropy for fault signals are extracted as new criterions to diagnose machinery faults.Several typical faults include rotor misalignment,transversal crack,and static-dynamic rubbing fault are analyzed,and the results indicate that those faults can be distinguished by the proposed method effectively,which provides a qualitative and quantitative analysis way in the field of machinery fault diagnosis.

Health diagnosis of concrete dams using hybrid FWA with RBF-based surrogate model

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.

Adaptive Surrogate Model Based Optimization (ASMBO) for Unknown Groundwater Contaminant Source Characterizations Using Self-Organizing Maps

Characterization of unknown groundwater contaminant sources in terms of location, magnitude and duration of source activity is a complex problem. In this study, to increase the efficiency and accuracy of source characterization an alternative methodology to the methodologies proposed earlier is developed. This methodology, Adaptive Surrogate Modeling Based Optimization (ASMBO) uses the capabilities of Self Organizing Map (SOM) algorithm to design the surrogate models and adaptive surrogate models for source characterization. The most important advantage of this methodology is its direct utilization for groundwater contaminant characterization without the necessity of utilizing a linked simulation optimization model. The validation of the SOM based surrogate models and SOM based adaptive surrogate models demonstrates that the quantity and quality of initial sample sizes have crucial role on the accuracy of solutions as the designed monitoring locations. The performance evaluation results of the proposed methodology are obtained using error free and erroneous concentration measurement data. These results demonstrate that the developed methodology could approximate groundwater flow and transport simulation models, and substitute the optimization model for characterization of unknown groundwater contaminant sources in terms of location, magnitude and duration of source activity.

Evaluating Conservation Effectiveness of Nature Reserves Established for Surrogate Species:Case of a Giant Panda Nature Reserve in Qinling Mountains,China

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.

Optimization on the Crosswind Stability of Trains Using Neural Network Surrogate Model

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.