基于区间不确定性的车架非线性可靠性优化

采用非线性有限元分析方法,用ABAQUS软件对车架的刚度和强度进行了分析。基于分析结果选取对结构强度和质量影响比较大的梁的厚度作为区间的设计变量,把车架材料的密度和泊松比作为不确定量,利用高维模型（TPS-HDMR）构建了设计变量与应力之间的近似模型,运用Kriging模型构建了设计变量与质量的近似模型。采用遗传算法中的NSGA-Ⅱ方法和隔代遗传算法,对车架应力和质量两目标进行了优化,并加入可靠度作为约束,得到了Pareto最优解集。     

光伏板清扫机器人支撑架轻量化优化设计

以光伏板清扫机器人支撑架为例提出了1种基于拓扑优化与响应面法相结合的轻量化设计方法。对支撑架受力进行有限元分析并对模型拓扑优化;然后利用Box-Behnken设计实验得到7个设计变量的数值模拟样点,通过响应面方法进行2次回归拟合得到响应面模型;最后,利用非支配多目标遗传算法对近似模型进行了迭代优化计算,得到Pareto最优解集。结果表明:轻量化优化后的支撑架的质量减轻了24.6%,应力下降了1.007 MPa。     

基于熵权TOPSIS方法的整车动力学性能多目标优化

为提高汽车行驶平顺性和操纵稳定性等整车动力学性能优化匹配效率,提出基于TOPSIS(Technique for ordering preferences by similarity to ideal solution,TOPSIS)方法的设计变量筛选策略。首先建立考虑下控制臂和扭转梁柔性的整车刚柔耦合虚拟样机模型,并通过下控制臂和扭转梁自由模态试验以及整车行驶平顺性和操纵稳定性实车道路试验验证所建整车刚柔耦合模型的正确性。采用试验设计方法研究下控制臂和扭转梁各结构参数分别对前后悬架性能的影响程度,提出基于熵权法和TOPSIS方法的结构综合贡献系数计算方法,以此为评价指标筛选出对悬架性能影响较大的结构参数作为整车性能匹配优化的设计变量,结合Kriging近似模型和NSGA-II算法(Elitist non-dominated sorting genetic algorithm,NSGA-II)对整车行驶平顺性和操纵稳定性进行多目标优化设计,获取Pareto最优解集,并确定出前后悬架系统的优化方案。研究结果表明,与优化前相比较,悬架系统优化后的整车行驶平顺性和操纵稳定性均有所提高,能够实现汽车性能的整体改善。     

基于熵权-改进TOPSIS法的驾驶室多目标优化

为了提升商用车轻量化优化效果,文中提出了一种基于熵权-改进逼近理想解排序法(TOPSIS)的驾驶室多目标决策方法。首先,对驾驶室有限元模型进行性能分析,并通过试验模态与仿真模态进行对比,验证了模型的准确性;其次,采用区域灵敏度分析,筛选出20个厚度和4个截面形状变量,并建立2阶响应面近似模型验证其精度;最后,采用第三代非劣排序遗传算法(NSGA-Ⅲ)对驾驶室进行多目标优化设计,再联合熵权-改进TOPSIS法求得非支配帕累托解的相对贴进度,并以此作为多目标决策的最终结果。结果表明：与优化前相比较,驾驶室质量减小了24.9 kg,减幅达到8.1%,能够满足轻量化和性能需求。     

立式振动磨关键结构参数的多目标优化设计

以获得立式振动磨最佳粉碎效果为目的,建立了立式振动磨参数化仿真模型,采用拉丁超立方方法生成样本数据,构建了立式振动磨响应面近似模型且其精度达到可接受水平。以振动强度、撞击力最大,研磨介质体积最小为目标函数,采用多目标遗传算法NSGA-Ⅱ对响应面近似模型进行多目标优化设计,得到Pareto最优解集和Pareto前沿图,为立式振动磨结构设计和参数选择提供理论指导。最后,通过立式振动磨的优化算例说明了该方法的有效性。近似模型方法能提高优化效率,且该研究也为其他复杂模型的设计优化问题提供了一种方法。     

轿车侧碰撞中儿童乘员约束系统的多目标优化

在轿车侧面碰撞事故中,儿童乘员头、胸部是主要损伤部位.为最大限度地降低儿童乘员头、胸部的损伤风险,对儿童约束系统和侧面气囊的安全性能进行优化设计.采用多体动力学软件MADYMO建立儿童乘员侧面碰撞仿真模型,并通过台车试验结果验证仿真模型的有效性.以头、胸部加速度为优化目标,运用试验设计方法和方差分析进行相关设计变量的灵敏度分析,并确定主要的优化设计变量为侧面气囊的泄气率常数、儿童安全座椅的上固定点位置和下固定织带的刚度系数,进而构建优化目标的二阶多元回归代理模型,利用NSGA-Ⅱ多目标优化算法对构建的代理模型进行优化求解,获得包含头、胸部加速度最优解集的Pareto前沿曲线.通过权衡分析选取使头、胸部的损伤风险同时降低的最优解,最后确定侧面气囊的泄气率常数0.037,儿童座椅的上固定点x向位置-0.25 m,座椅下固定织带的刚度系数0.5为儿童约束系统主要设计参数的最优配置.在最优设计配置条件下得到的儿童头部合成加速度峰值比初始值下降了约24%,胸部合成加速度峰值比初始值下降约30%.     

基于Pareto遗传算法的高速插秧机变速器优化设计

以某高速插秧机变速器的优化设计为例,将Pareto最优解概念和遗传算法相结合,在遗传算法的基础上引入群体排序技术、小生境技术和Pareto解集过滤器等技术,并针对设计变量都是离散变量的特点,采用先将生成的随机数变换到约束范围后再圆整到最近离散值的方法,构造了适用于求解多目标优化问题的Pa-reto遗传算法,运用该算法获得了变速器在体积最小、中心距最小和总重合度最大目标下的Pareto最优解集。结果表明,采用Pareto遗传算法优化设计的变速器达到了综合优化设计的效果。     

基于Pareto挖掘的白车身侧碰安全件轻量化优化设计

为提高白车身轻量化优化效果,提出了熵权灰色关联分析法用于挖掘非支配Pareto解集中的最优解。建立了白车身及整车侧碰有限元模型,通过实车侧碰试验验证了所建模型的准确性。以侧碰安全件料厚为设计变量,综合考虑白车身弯扭刚度、振动频率等基本静-动态性能及侧碰安全性能,构建径向基函数神经网络结合Kriging（RBFNN-Kriging）混合近似模型并联合第二代非支配排序遗传算法（NSGA-Ⅱ算法）进行了多目标优化。最后,提出了熵权灰色关联分析法计算所有非支配Pareto解的灰色关联度,并以此为评价指标进行多目标决策。优化决策结果表明：在满足白车身性能设计基线的要求下,白车身侧碰安全件质量减小了2.68 kg,取得了较好的轻量化效果。     

基于响应面法的多材料优化模型与应用

基于二阶响应面法,建立高效多材料、多目标优化模型,研究降低某款汽车座椅骨架总质量的各零部件材料和厚度的最优设计方案。利用多目标优化方法定义了优化问题,采用最优拉丁方试验设计方法在样本空间进行采样,同时为了提高优化求解效率,采用二阶响应面近似模型拟合设计变量与响应之间的关系,并利用NSGA-Ⅱ算法对优化问题进行求解。应用中,以减轻座椅骨架总质量和提升一阶模态为优化目标,定义各零部件的材料类型及厚度为设计变量,得出材料和厚度的最优设计方案,并依据相应国标对优化方案进行校验。结果表明,在满足各项国标的前提下,该方法准确有效,在提升一阶模态的同时,实现了对座椅骨架的轻量化设计。     

基于NSGA-Ⅱ的复摆式破碎机多目标优化设计

为提高复摆式颚式破碎机工作能力,以破碎生产率和动颚行程特性值为优化目标,对颚式破碎机进行多目标优化设计。以PE250×400型颚式破碎机为优化对象,以各构件尺寸为优化设计变量,建立机构参数、腔形参数及工作参数等约束条件,构建多目标优化设计模型,利用带精英策略的非支配排序遗传算法(NSGA-II)处理多目标优化模型,得到帕累托(Pareto)最优解集。通过对比两目标的最优解分布,确定出待优化目标间的相互影响规律,并从最优解集中选择出合理参数作为最终设计结果。分析结果表明:文中提出的优化设计方法在获得更大生产率的同时,有效减小了动颚磨损,实现了颚式破碎机的多目标优化设计。     

A hybrid method of modified NSGA-II and TOPSIS for lightweight design of parameterized passenger car sub-frame

This paper presents a hybrid method integrating modified NSGA-II and TOPSIS, used for lightweight design of the front sub-frame of a passenger car. Firstly, the FE model of the sub-frame is constructed and is validated by modal test. Then, the strength performance of the sub-frame is analyzed under four typical load conditions consisting of braking, acceleration, steady state cornering and vertical bump. After that, a parameterized model of the sub-frame, in which 12 geometric parameters are defined as design variables, is developed based on the mesh morphing technology. Subsequently, modified NSGA-II is employed for multi-objective optimization of the sub-frame considering weight, maximum von-Mises stress and first order natural frequency as three conflicting objective functions. Accordingly, a set of Pareto-optimal solutions are obtained from the optimization process. Finally, the entropy weight theory and TOPSIS method are adopted to rank all these solutions from the best to the worst for determining the best compromise solution. In addition, the effectiveness of the proposed hybrid lightweight design method is demonstrated by the comparisons among baseline design and optimum solutions.     

Multi-objective optimization of collaborative manufacturing chain with time-sequence constraints

To realize the sharing and optimization deployment of manufacturing resources, a concept of collaborative manufacturing chain (CMC) is proposed for the manufacturing of complex products in a networked manufacturing environment. To acquire the optimal CMC, a multi-objective optimization model is developed to minimize the comprehensive cost and the whole production load with time-sequence constraints. Non-dominated sorting genetic algorithm (NSGA-II) is applied to solve optimization functions. The optimal solution set of Pareto is obtained. The technique for order preference by similarity to ideal solution (TOPSIS) approach is then used to identify the optimal compromise solution from the optimal solution set of Pareto. Simulation results obtained in this study indicate that the proposed model and algorithm are able to obtain satisfactory solutions.     

液压混合动力矿用卡车系统参数多目标优化研究

以液压混合动力矿用卡车为研究对象,综合考虑能量管理控制参数与传动系参数对燃油经济性和小型化目标的影响。选取能量管理控制参数与传动系参数为优化变量,以矿用卡车的动力性能为约束条件,建立燃油经济性和小型化为目标的多目标优化评价方法。选取NSGA-II算法对混合动力系统进行多目标的优化。结果表明：在满足动力性约束的基础上,优化后等效百公里油耗下降了14.86%,爬坡度上升了12.39%。该多目标优化方法的收敛性和分布性较好,得到的pareto解集能够给液压混合动力矿用卡车的设计提供更多的方案进行选择,体现了基于NSGA-II算法的多目标优化的优势。     

Artificial neural network based on genetic algorithm for emissions prediction of a SI gasoline engine

This paper proposes a hybrid learning of artificial neural network (ANN) with the nondominated sorting genetic algorithm-II (NSGAII) to improve accuracy in order to predict the exhaust emissions of a four stroke spark ignition (SI) engine. In the proposed approach, the genetic algorithm (GA) determines initial weights of local linear model tree (LOLIMOT) neural networks. A multi-objective optimization problem is determined. A sensitivity analysis is performed on NSGA-II parameters in order to provide better solutions along the optimal Pareto front. Then, a fuzzy decision maker and the technique for order preference by similarity to ideal solution (TOPSIS) are employed to select compromised solutions among the obtained Pareto solutions. The LOLIMOT-GA responses are compared with the provided by radial basis function (RBF) and multilayer perceptron (MLP) neural networks in terms of correlation coefficient R ².     

Multi-objective optimization of cycloidal gear based on segmental modification of pressure angle

In order to get the optimal profile of cycloid gear after sectional modification, a multi-objective optimization design method is proposed that considers both the modification parameters and macro-parameters of the cycloid gear. An algorithm of meshing force, meshing efficiency and anti-gluing ability between the cycloid gear and the pin gear was derived, and the related independent parameters were extracted as optimization variables. Taking high efficiency, high strength and light weight as the objective, the mathematical models of double-objective and three-objective optimization were established, and the influence of key design variable on the objective function was analyzed, and NSGA-II multi-objective genetic algorithm was used to solve the Pareto optimal solution of the optimization mathematical model. Results show that the optimized parameters can significantly improve the meshing efficiency, reduce the volume and meet the design requirements of high strength, high efficiency and lightweight on the premise of ensuring the strength of cycloid gear surface.

     

基于降维的驾驶员侧约束系统高维目标优化

为解决将高维目标变为单目标优化时各子目标不能同时较优,而多目标算法直接用于高维目标优化时又存在难以找到一个有代表性的Pareto非劣解集问题,在某轿车驾驶员侧约束系统的优化过程中提出了乘员损伤准则与多目标算法协同优化的方法。在已有相关损伤准则基础上根据最新版的FMVSS 208和ECE R94法规提出了适合研究问题的损伤准则;以提出的损伤准则为媒介,将一个高维目标优化问题降为一个低维目标优化问题,通过灵敏度分析、实验设计、多项式近似模型筛选出优化设计变量并得到近似模型,用多目标算法NSGA-Ⅱ对近似模型进行计算得到Pareto非劣解集,将得到的Pareto非劣解集中的每个解代入损伤准则损伤值计算公式,升序排列得到各子目标同时较优而损伤值最小的优化解。最终的优化结果表明：该方法很好地解决了乘员约束系统的高维目标优化问题,优化效果明显。     

Multi-objective optimization of drive gears for power split device using surrogate models

Power split device (PSD) is a key component in the energy coupling and decoupling of parallel-series hybrid electric vehicle. This paper proposes a multi-objective optimization method to achieve optimal balance solution among the volume, contact stress, and frictional energy dissipation of PSD drive gears, some of which are implicit with respect to design variables. To avoid the time-consuming problem of finite element analysis used to solve nonlinear responses, surrogate models are adopted to generate approximate expressions of design variables. Pareto-optimal solutions of PSD are obtained using multi-island genetic algorithm (MIGA), non-dominated sorting GA-II (NSGA-II), and multi-objective particle swarm optimization algorithm. The performances of PSD before and after optimization are compared. Results indicate that the proposed method is effective, and NSGA-II achieves higher optimizing efficiency in solving the multiobjective optimization problem of PSD than the other algorithms.     

基于近似模型的高速列车头型多目标优化设计

为改善高速列车气动性能,建立一套高效的多目标气动优化设计方法,对流线型头型进行多目标气动优化设计。建立高速列车流线型头型三维参数化模型,并提取5个优化设计变量;为减少优化设计时间,利用最优拉丁超立方设计方法在优化设计空间中进行均匀采样,利用计算流体力学方法获得对应于各个采样点的气动载荷,利用Kriging代理模型构建优化设计变量和气动载荷之间的近似模型;利用多体系统动力学方法计算气动载荷作用下的高速列车轮重减载率;以气动阻力和轮重减载率为优化目标,利用多目标遗传算法NSGA-II对高速列车流线型头型进行多目标优化。优化设计变量和优化目标均呈现收敛的趋势,采用Kriging近似模型优化计算的Pareto前沿与采用CFD（Computational fluid dynamics,CFD）优化计算的Pareto前沿较为接近。优化后高速列车的气动阻力最多可降低3.27%,轮重减载率最多可降低1.44%,气动阻力最优的头型与轮重减载率最优的头型的主要差异在于中部辅助控制线的变化,前者向内凹,后者则向外凸。     

轿车八自由度平顺性动力学仿真与实验研究

建立了轿车八自由度平顺性动力学模型,推导了八自由度轿车模型拉格朗日方程,并应用仿真软件MATLAB/S imu link建立了轿车平顺性的仿真模型,对驾驶员座椅、副驾驶员座椅和后排左侧座椅的垂直加速度信号进行了仿真,得出了频域内的仿真结果,并与实验结果进行了对比。     

考虑碳排放与收益的随机并行拆卸线平衡优化

针对废旧产品回收过程中,需有选择对综合收益高的零部件进行拆卸的问题。选择最小工作站数,平滑度,碳排放量,拆卸收益作为优化目标,并对相应目标进行量化分析,提出一种随机并行拆卸线平衡优化方法。在构建随机并行拆卸线基本解集的基础上,为避免Pareto解集逐渐趋同的问题,提出一种基于环形拓扑结构的花授粉算法(Ring topology flower pollination algorithm, Ring-FPA),实现了对Pareto解集的决策处理,获得考虑碳排放与收益的随机并行拆卸线平衡最优解。以手机和笔记本电脑并行拆卸线平衡优化为例,选择NSGA-Ⅱ、FPA,以及AFSA算法进行对比,对所提方法的可行性和有效性进行了验证。