APPROXIMATION TECHNIQUES FOR APPLICATION OF GENETIC ALGORITHMS TO STRUCTURAL OPTIMIZATION

Although the genetic algorithm (GA) has very powerful robustness and fitness, it needs a large size of population and a large number of iterations to reach the optimum result. Especially when GA is used in complex structural optimization problems, if the structural reanalysis technique is not adopted, the more the number of finite element analysis (FEA) is, the more the consuming time is. In the conventional structural optimization the number of FEA can be reduced by the structural reanalysis technique based on the approximation techniques and sensitivity analysis. With these techniques, this paper provides a new approximation model-segment approximation model, adopted for the GA application. This segment approximation model can decrease the number of FEA and increase the convergence rate of GA. So it can apparently decrease the computation time of GA. Two examples demonstrate the availability of the new segment approximation model.

The Key Path of the Shape Optimization Design of Isotropic Material Pressure Vessels

An optimization design was conducted for the shape of the pressure vessel with a thin-shell shell. During this process, the optimization calculation was performed with the aid of the genetic algorithm toolbox included in Matlab. Firstly, through the parametric modeling function of APDL, models such as arc-shaped, parabolic, elliptical, and those generated by the fitting curve command were successfully constructed. Meanwhile, the relevant settings of material properties were accomplished, and the static analysis was conducted. Secondly, the optimization calculation process was initiated using the genetic algorithm toolbox in Matlab. Eventually, through analysis and judgment, the model generated by the fitting curve command was relatively superior within the category of the best shape.

Optimization Design of Fairings for VIV Suppression Based on Data-Driven Models and Genetic Algorithm

Vortex induced vibration(VIV)is a challenge in ocean engineering.Several devices including fairings have been designed to suppress VIV.However,how to optimize the design of suppression devices is still a problem to be solved.In this paper,an optimization design methodology is presented based on data-driven models and genetic algorithm(GA).Data-driven models are introduced to substitute complex physics-based equations.GA is used to rapidly search for the optimal suppression device from all possible solutions.Taking fairings as example,VIV response database for different fairings is established based on parameterized models in which model sections of fairings are controlled by several control points and Bezier curves.Then a data-driven model,which can predict the VIV response of fairings with different sections accurately and efficiently,is trained through BP neural network.Finally,a comprehensive optimization method and process is proposed based on GA and the data-driven model.The proposed method is demonstrated by its application to a case.It turns out that the proposed method can perform the optimization design of fairings effectively.VIV can be reduced obviously through the optimization design.

Development and Comparison of Hybrid Genetic Algorithms for Network Design Problem in Closed Loop Supply Chain

This paper presents four different hybrid genetic algorithms for network design problem in closed loop supply chain. They are compared using a complete factorial experiment with two factors, viz. problem size and algorithm. Based on the significance of the factor “algorithm”, the best algorithm is identified using Duncan’s multiple range test. Then it is compared with a mathematical model in terms of total cost. It is found that the best hybrid genetic algorithm identified gives results on par with the mathematical model in statistical terms. So, the best algorithm out of four algorithm proposed in this paper is proved to be superior to all other algorithms for all sizes of problems and its performance is equal to that of the mathematical model for small size and medium size problems.

Improved Genetic Optimization Algorithm with Subdomain Model for Multi-objective Optimal Design of SPMSM

For an optimal design of a surface-mounted permanent magnet synchronous motor(SPMSM),many objective functions should be considered.The classical optimization methods,which have been habitually designed based on magnetic circuit law or finite element analysis(FEA),have inaccuracy or calculation time problems when solving the multi-objective problems.To address these problems,the multi-independent-population genetic algorithm(MGA)combined with subdomain(SD)model are proposed to improve the performance of SPMSM such as magnetic field distribution,cost and efficiency.In order to analyze the flux density harmonics accurately,the accurate SD model is first established.Then,the MGA with time-saving SD model are employed to search for solutions which belong to the Pareto optimal set.Finally,for the purpose of validation,the electromagnetic performance of the new design motor are investigated by FEA,comparing with the initial design and conventional GA optimal design to demonstrate the advantage of MGA optimization method.

Design optimization of transonic compressor stage using CFD and response surface model

In order to shorten the design period, the paper describes a new optimization strategy for computationally expensive design optimization of turbomachinery, combined with design of experiment (DOE), response surface models (RSM), genetic algorithm (GA) and a 3-D Navier-Stokes solver(Numeca Fine). Data points for response evaluations were selected by improved distributed hypercube sampling (IHS) and the 3-D Navier-Stokes analysis was carried out at these sample points. The quadratic response surface model was used to approximate the relationships between the design variables and flow parameters. To maximize the adiabatic efficiency, the genetic algorithm was applied to the response surface model to perform global optimization to achieve the optimum design of NASA Stage 35. An optimum leading edge line was found, which produced a new 3-D rotor blade combined with sweep and lean, and a new stator one with skew. It is concluded that the proposed strategy can provide a reliable method for design optimization of turbomachinery blades at reasonable computing cost.

Layout Design-Based Research on Optimization and Assessment Method for Shipbuilding Workshop

The research study proposes to examine a three-dimensional visualization program, emphasizing on improving genetic algorithms through the optimization of a layout design-based standard and discrete shipbuilding workshop. By utilizing a steel processing workshop as an example, the principle of minimum logistic costs will be implemented to obtain an ideological equipment layout, and a mathematical model. The objectiveness is to minimize the total necessary distance traveled between machines. An improved control operator is implemented to improve the iterative efficiency of the genetic algorithm, and yield relevant parameters. The Computer Aided Tri-Dimensional Interface Application （CATIA） software is applied to establish the manufacturing resource base and parametric model of the steel processing workshop. Based on the results of optimized planar logistics, a visual parametric model of the steel processing workshop is constructed, and qualitative and quantitative adjustments then are applied to the model. The method for evaluating the results of the layout is subsequently established through the utilization of AHP. In order to provide a mode of reference to the optimization and layout of the digitalized production workshop, the optimized discrete production workshop will possess a certain level of practical significance.

Intelligent Optimization Methods for the Design of an Overhead Travelling Crane

In design optimization of crane metal structures, present approaches are based on simple models and mixed variables, which are difficult to use in practice and usually lead to failure of optimized results for rounding variables. Crane metal structure optimal design（CMSOD） belongs to a constrained nonlinear optimization problem with discrete variables. A novel algorithm combining ant colony algorithm with a mutation-based local search（ACAM） is developed and used for a real CMSOD for the first time. In the algorithm model, the encoded mode of continuous array elements is introduced. This not only avoids the need to round optimization design variables during mixed variable optimization, but also facilitates the construction of heuristic information, and the storage and update of the ant colony pheromone. Together with the proposed ACAM, a genetic algorithm（GA） and particle swarm optimization（PSO） are used to optimize the metal structure of a crane. The optimization results show that the convergence speed of ACAM is approximately 20% of that of the GA and around 11% of that of the PSO. The objective function value given by ACAM is 22.23% less than the practical design value, a reduction of 16.42% over the GA and 3.27% over the PSO. The developed ACAM is an effective intelligent method for CMSOD and superior to other methods.

MODELING, VALIDATION AND OPTIMAL DESIGN OF THE CLAMPING FORCE CONTROL VALVE USED IN CONTINUOUSLY VARIABLE TRANSMISSION

Associated dynamic performance of the clamping force control valve used in continuously variable transmission （CVT） is optimized. Firstly, the structure and working principle of the valve are analyzed, and then a dynamic model is set up by means of mechanism analysis. For the purpose of checking the validity of the modeling method, a prototype workpiece of the valve is manufactured for comparison test, and its simulation result follows the experimental result quite well. An associated performance index is founded considering the response time, overshoot and saving energy, and five structural parameters are selected to adjust for deriving the optimal associated performance index. The optimization problem is solved by the genetic algorithm （GA） with necessary constraints. Finally, the properties of the optimized valve are compared with those of the prototype workpiece, and the results prove that the dynamic performance indexes of the optimized valve are much better than those of the prototype workpiece.

Hybrid optimization model of product concepts

Deficiencies of applying the simple genetic algorithm to generate concepts were specified. Based on analyzing conceptual design and the morphological matrix of an excavator, the hybrid optimization model of generating its concepts was proposed, viz. an improved adaptive genetic algorithm was applied to explore the excavator concepts in the searching space of conceptual design, and a neural network was used to evaluate the fitness of the population. The optimization of generating concepts was finished through the ＂evolution - evaluation＂ iteration. The results show that by using the hybrid optimization model, not only the fitness evaluation and constraint conditions are well processed, but also the search precision and convergence speed of the optimization process are greatly improved. An example is presented to demonstrate the advantages of the orooosed method and associated algorithms.

基于多目标优化的箱梁渡槽结构设计研究

针对箱梁渡槽所受水荷载远大于同等跨径箱梁桥所受汽车荷载导致槽身纵向受力突出的问题,在渡槽结构设计中统筹考虑箱梁纵向应力储备和造价,构建了以截面尺寸、钢筋用量等6个参数为设计变量,以渡槽边缘正应力和造价为目标函数,以槽身挠度、法向应力等9个验算指标为约束条件的多目标优化模型,并基于支持向量机近似模型和NSGA2遗传算法求解多目标优化模型的Pareto最优解集,然后综合专家打分、COWA算子、博弈论综合赋权与TOPSIS法从Pareto最优解集中选出最佳方案。结果表明:与初始方案相比,最佳方案的渡槽边缘正应力储备提高了17.55%,渡槽上部结构总造价降低8.84%;与有限元分析相比,该方法在保持较高计算精度的同时节省了65.42%的计算时间成本。

基于EGA优化的农用UTV半主动悬架最优控制

针对UTV在恶劣路面行驶引起的车辆振动,以某款农用UTV悬架系统为对象,建立包含俯仰的四自由度(4-DOF)半车半主动悬架动力学模型,并提出一种EGA-LQR复合控制策略,设计满足物理约束的悬架系统自适应最优控制器。利用EGA算法的全局寻优与快速收敛特性,对LQR最优控制器的权重矩阵寻优,输出悬架系统最优控制阻尼力。在Matlab/Simulink中搭建UTV的路面与悬架模型进行时域仿真,仿真分析结果表明,EGA-LQR控制显著减小了车体质心垂向振动加速度、车体俯仰角加速度、前后轮动位移以及前后悬架动行程的均方根值,有效保证了UTV在农田路面下行驶的舒适性与安全性。

基于高维混合模型的离心泵叶轮子午面优化设计

为提高离心泵在设计工况下的运行效率和扬程,提出一种基于高维混合模型的离心泵叶轮优化设计方法.选取一台比转数为157的单级离心泵作为研究对象,通过CFturbo软件对优化变量进行参数化,然后结合数值模拟获得高维混合模型的训练集.在此基础上采用获取的训练集通过MATLAB机器学习得出效率、扬程与优化参数之间关于支持向量回归的高维模型,并采用遗传算法寻优.在设计工况下,所拟合的高维混合模型预测的效率和扬程值比原模型分别高1.5%和3.2 m,数值模拟验证优化方案的效率和扬程分别比原模型高0.9%和2.1 m.算例研究表明,将高维混合模型应用于离心泵叶轮的优化设计中可以实现快速寻优并提高离心泵水力性能.

柔性立管限弯器高效设计方法

目的考虑聚氨酯材料非线性的影响,建立一种简单高效的柔性立管限弯器设计方法。方法首先确定限弯器的初始尺寸,在此基础上进行参数分析,并根据分析结果绘制设计图谱,然后通过插值获得最佳的设计尺寸。结果针对深水与浅水2种典型环境,分别采用本方法与基于遗传算法的优化设计方法进行限弯器设计,通过结果对比验证了方法的有效性。同时发现,深水环境条件设计出的限弯器相对短粗,而浅水条件设计出的限弯器则较为细长,且考虑材料非线性后,2种环境下限弯器的设计尺寸均有所减小。结论本文建立的限弯器设计方法具有广泛的适用性,且能够在确保得到理想设计方案的前提下,显著提高设计效率。与基于遗传算法的优化设计方法相比,采用线性材料和考虑材料非线性时的设计效率可分别提高约10倍和20倍。

考虑非平稳随机过程的机场旅检通道服务资源配置优化

针对机场旅客到达过程的非平稳性和随机性造成旅检通道需求不确定且具备高时变性、配置难度大这一问题,提出一种非平稳排队模型嵌入遗传算法的方法。建立具有多个并行服务台的单阶段服务系统的非平稳排队模型,将输入过程和服务过程拟合为一般分布,并提出拓展的积压后移平稳(stationary backlog-carryover,SBC)近似,以快速求解系统性能指标值。提出一种将拓展的SBC近似嵌入遗传算法的优化方法,设计实验算例和优化算例。通过解析结果与仿真结果进行对比,验证拓展SBC近似的精确性以及优化方法的有效性,并分析非平稳性对系统性能以及优化结果的影响。将该方法应用在实际案例中,结果表明,优化后的配置方案比实际配置方案减少了14.80%的旅检通道数量,这证明该方法是有效的。

基于多保真代理模型的塔机臂架结构优化

起重臂架是动臂式塔机的关键工作部件,负责提升运输建筑材料,其制造成本和工作耗能与自身重量成正比,需要对臂架的重量优化展开研究,因此提出了一种针对动臂式塔机臂架的高效优化设计方法。首先,基于有限元法,计算了不同臂架幅度下的极限工况应力及变形,确定了最危险工况,并以此工况作为优化设计的基础工况;然后,选定了臂架上12个结构参数为设计变量,进行了参数化建模,利用拉丁超立方采样(LHS)选取了样本点,基于有限元仿真数据,构建了三种基于径向基函数(RBF)的代理模型并对其精度进行了评估;最后,基于精度高的多保真代理模型(MFS),以臂架重量最小为优化目标,构建了臂架优化问题的数学模型,使用遗传算法(GA)完成了优化设计。研究结果表明:优化后的臂架满足强度、刚度要求,整个臂架不会发生断裂和失稳,而且臂架重量减轻了25.03%;同时,多保真代理模型的预测结果和仿真结果误差小于3%,证明了多保真代理模型的准确性,进而证明了基于多保真代理模型的臂架优化方法的有效性。

基于近似模型的蓄电池工程车电池布局优化

蓄电池工程车中蓄电池的重量大、数量多,其布局对车辆的整体性能影响较大。为优化车架的电池布局以及解决优化过程中频繁调用有限元仿真的问题,提出了一种响应面近似模型与多岛遗传算法相结合的蓄电池布局优化设计方法。首先建立了蓄电池工程车车架的参数优化模型、设计变量与目标函数的二阶响应面近似模型,然后采用多岛遗传算法对优化模型进行求解,使用动力学指标评定解的优劣,并选择一个最优解作为最优电池布局方案,最后使用缩尺车架模型对优化结果进行了试验验证。结果表明:蓄电池优化后的车架最大变形减小50.9%,最大等效应力减小37.9%,最大垂向振动加速度减小了14.6%,仿真与试验结果的误差为7.1%,达到了优化设计的目的。

基于遗传算法的卧式加工中心阶梯轨式立柱的多目标优化

立柱是影响机床整机刚度的薄弱环节,其性能直接影响卧式加工中心的加工精度,因此文章提出了一种卧式加工中心阶梯轨式立柱的多目标优化设计方法。首先,基于Ansys对整机进行仿真发现立柱是影响整机静动态性能最薄弱的环节,并当主轴箱处于立柱最上沿处时立柱的刚度最差;其次,选择立柱跨距、立柱高度差、丝杠面宽度、上导轨面宽度作为设计变量;再次,以立柱导轨面X向和Z向变形范围、一阶固有频率以及质量作为评价指标,利用多目标遗传算法寻求最优解,基于此通过灵敏度分析对设计变量圆整并进行仿真验证;最后,结果表明,优化后的立柱导轨面Z向最大变形减小了7.37%,Z向变形范围减小了11.57%,X向变形范围减小了2.41%,一阶固有频率提高了1.4%。

基于NSGA-Ⅲ算法的低影响开发措施规划设计

为完善海绵城市建设的整体规划设计,基于东南亚某经济开发区,结合雨洪管理模型(storm water management model,SWMM)和第三代非支配排序遗传算法(non-dominated sorting genetic algorithm-Ⅲ,NSGA-Ⅲ)建立了一个四目标优化模型,以地表径流系数、管道过载时间、节点溢流量等3个城市内涝指标和总投资成本作为优化目标进行求解.结果表明:该优化模型可实现多目标同步优化,获得效益较高的低影响开发(low impact development,LID)措施的设计方案,优化后地表径流系数为0.309~0.355,管道过载时间为23.834~27.967 h,节点溢流量为10477~21802 m^(3),工程总投资成本为7.479亿~9.593亿元.研究结果可为未来海绵城市内涝控制设计提供技术参考.

Towards Energy Efficient Shape Rolling:Roll Pass Optimal Design and Case Studies

Shape rolling is widely employed in the production of long workpieces with appropriate cross-section profiles for other industrial applications. In the development of shape rolling systems, roll pass design (RPD) plays an essential role on the quality control of products, service life of rolls, productivity of rolling systems, as well as energy consumption of rolling operations. This study attempts to establish a generic strategy based on hybrid modeling and an improved genetic algorithm, to support the optimizations of RPD and shape rolling operations at a systematic perspective. Objectives include improving the quality and efficiency of RPD, reducing energy consumption of shape rolling, as well as releasing the demands on costly trails and expert knowledge in RPD. Hybrid modeling based on cross-disciplinary knowledge is developed to overcome the limitations of isolated single-disciplinary models. And conventional genetic algorithm is improved for the implementation of optimal design. Targeting to integrate empirical data and published reliable solutions into optimizations, a parameters estimation method is proposed to transfer the initially misaligned models into a uniform pattern. A tool based on the Matlab platform is developed to demonstrate the optimal design operations, with case studies involved to validate the proposed methodology.