基于公理设计和相容决策支持问题法的稳健优化设计方法

 针对多目标稳健优化问题,建立了多目标稳健优化的损失函数,利用灵敏度分析方法确定各设计变量对各设计目标的影响程度,确定主要的设计参数,便于调整和控制设计参数的公差.根据信息公理与损失函数的一致性关系,建立以最小化各目标的总损失函数为目标函数.并在相容决策支持问题法框架基础上,提出一种基于公理设计和相容决策支持问题法的多目标稳健优化设计模型.实例分析表明,提出的方法是可行的.     

考虑静动力学特性的材料结构一体化多目标优化设计

针对复合材料微结构的内部构型和宏观排布的可设计性,以结构基频最大和柔度最小加权系数为目标,将微结构设计和多尺度计算结合,建立了考虑静动力学特性的材料/结构一体化多目标优化设计模型,实现了相应的算法和算例.方法中引入了微观和宏观两个尺度上的独立密度变量,采用RAMP(Rational Approximation ofMaterial Properties)方法对密度进行惩罚,利用有限元超单元技术建立材料与结构的联系,通过规一化目标函数有效避免了不同性质目标函数的量级差异.通过算例,获得了静动态权重系数对结构拓扑构型和目标函数(宏观结构的柔度和基频)的影响规律.研究结果表明:该方法是有效的,可作为对轻质结构进行静动态多目标优化设计的一种新方法.     

基于等效静态载荷法的汽车前端结构抗撞性尺寸和形貌优化

为了减少在低速正碰下汽车的维修成本以及实现汽车前端结构的轻量化。采用等效静态载荷方法对汽车前端结构的尺寸和形状进行了抗撞性优化设计,以整个结构质量最小为优化的目标函数,以前部结构主要部件的厚度尺寸以及节点坐标形貌为设计变量,以侵入量以及加强筋的冲压工艺要求为约束进行了碰撞优化设计。等效静态载荷法将非线性瞬态碰撞优化问题转化为多工况线性静态优化问题,数值算例证明等效静态载荷法可高效地求解碰撞优化问题,并得到高精度的最优解。     

多目标优化在锻造毛坯形状优化设计中的应用

以形状相对简单的单工序锻件为研究对象,以同时获得材料消耗最小和变形程度最均匀的终锻件为优化目标,对锻造过程毛坯形状进行多目标优化设计的研究.给出了单工序锻件形状优化设计的总的目标函数及其各子目标函数的表示方法及计算表达式,以毛坯的高径(宽)比作为优化设计的变量,基于刚粘塑性有限元理论,采用黄金分割法进行优化迭代,编制了相应的程序,并对一典型的轴对称H型锻件的毛坯形状进行了多目标的优化设计,取得了明显的效果.     

基于粒子群算法的高黏度大比重物料无轴螺旋输送机构优化设计

 为解决高黏度大比重物料无轴螺旋输送机的螺旋叶片变形问题,利用粒子群优化算法,以无轴螺旋叶片刚度变形最小为优化设计目标,构建了基于粒子群算法的无轴螺旋叶片优化设计模型.应用构建的理论模型,进行了实例设计,并通过实验研究进一步证实该设计方法具有先进性和实用性．将智能算法应用于机械优化设计,为该领域研究提供新思路．     

离散变量优化设计的方向下降速率搜索法

根据离散变量优化设计的特点,提出了方向下降速率搜索方法。针对具有单调性的一类目标函数、约束函数的优化设计问题,提出了一种在可行集外由目标函数最小的点出发,沿目标函数增加最小,约束函数降低最多的方向逐步搜索,逐步靠近可集边界的方向下降速度搜索法。     

考虑热振特性的连续体结构拓扑优化设计

为实现传热和振动条件下连续体结构的拓扑优化设计,以结构散热弱度最小化和动态特征值最大化加权函数为目标,建立传热和振动条件下连续体结构的多目标拓扑优化模型,实现了相应的算法和算例。方法中采用Rational Approximation of Material Properties(RAMP)方法对密度进行惩罚,利用优化准则法控制设计目标与材料分布,以敏度过滤技术抑制棋盘格效应,通过归一化目标函数有效地避免了不同性质目标函数的量级差异。通过算例,获得了热-振权重系数对结构拓扑构型和目标函数(宏观结构的散热弱度和基频)的影响规律,算例结果表明了该方法的有效性。     

曲柄摇杆机构预定轨迹的优化设计与仿真

基于M atlab对曲柄摇杆机构进行多约束条件下预定轨迹的优化设计,以实际轨迹点与预定轨迹点坐标差值的最小平方和建立目标函数,采用fmincon函数求出了约束条件下非线性多元函数的优化解.利用UG NX软件对所研究曲柄摇杆机构进行了运动仿真与轨迹验证.结果表明,所用优化设计方法能够有效地解决多约束条件下预定轨迹的曲柄摇杆机构设计问题.     

基于满意度原理的自立式高耸钢结构环形TLCD控制多目标优化设计

主要针对自立式高耸钢结构振动控制的优化设计开展研究,根据自立式高耸钢结构的特点,设计了环形调谐液柱阻尼器(Tuned Liquid Column Damper,TLCD),描述了其力学模型,并进一步推导了高耸结构环形TLCD控制的动力学方程。采用Sigmoid函数和线性叠加法构建了用于自立式高耸钢结构风振控制的复合满意度函数,从而基于满意度原理,并结合模式搜索算法,建立了自立式高耸钢结构多目标优化设计方法。针对某自立式高耸钢结构的风振控制设计,以环形TLCD的几何参数为设计变量,以由顶点位移、质量比和迎风面积比组成的复合满意度为目标,编制程序开展了所建立方法的数值算例研究。研究表明,该方法能够快速有效地得到一组满足工程需要的设计参数,同时最优参数和相应设计目标对权重组合的变异系数小于0.1,因此该方法具有较高的鲁棒性,降低了多目标优化设计时权重系数选取的难度。     

基于Newmark法敏度计算的刚架结构动力优化

提出一种有效的求解结构最小质量设计,同时满足动位移和动应力约束的二阶优化设计方法。在有限元法和纽马克法基础上导出一种高效的动应力、动位移对设计变量一阶导数和二阶导数的算法。建立含时间参数,以结构质量最小化为目标,同时满足动位移、动应力和设计变量约束的优化数学模型,通过积分型内点罚函数将含时间参数的不等式约束优化问题转变为一系列不含时间参数的无约束优化问题。利用动位移、动应力对设计变量一阶导数和二阶导数的信息计算内点罚函数的梯度和海森矩阵,利用梯度和海森矩阵构造求解优化设计问题高效有效的二阶优化算法。算例结果表明该文的优化设计方法能获得刚架结构的局部最优设计,优化的效率高于增广拉格朗日乘子法。     

Evolutionary multi-objective concurrent maximisation of process tolerances

Concurrent tolerancing which simultaneously optimises process tolerance based on constraints of both dimensional and geometrical tolerances (DGTs), and process accuracy with multi-objective functions is tedious to solve by a conventional optimisation technique like a linear programming approach. Concurrent tolerancing becomes an optimisation problem to determine optimum allotment of the process tolerances under the design function constraints. Optimum solution for this advanced tolerance design problem is difficult to obtain using traditional optimisation techniques. The proposed algorithms (elitist non-dominated sorting genetic algorithm (NSGA-II) and multi-objective differential evolution (MODE)) significantly outperform the previous algorithms for obtaining the optimum solution. The average fitness factor method and the normalised weighting objective function method are used to select the best optimal solution from Pareto optimal fronts. Two multi-objective performance measures namely solution spread measure and ratio of non-dominated individuals are used to evaluate the strength of the Pareto optimal fronts. Two more multi-objective performance measures namely optimiser overhead and algorithm effort are used to find the computational effort of the NSGA-II and MODE algorithms. Comparison of the results establishes that the proposed algorithms are superior to the algorithms in the literature.     

Topology optimization of structures under multiple loading cases with a new compliance–volume product

This article proposes a new topology optimization method for the design of structures under multiple loading cases. The design is formulated as a multi-objective optimization problem by minimizing a new compliance–volume product, which optimizes the overall stiffness and volume simultaneously to avoid the empirical decision on design constraints and obtain an even lower structural volume. A normalized exponential weighted criterion (NEWC) method is included in the multi-objective optimization problem for the capture of the entire Pareto frontier. A weight evaluation method, in terms of the fuzzy multiple-attribute group decision-making (FMAGDM) theory, is incorporated in the problem to evaluate the weights of the objectives and guarantee the optimal design in an acceptable level. The solid isotropic material with penalty (SIMP) method is used to represent the dependence of elemental densities on material properties. Three typical numerical examples are employed to show the effectiveness of the proposed method.     

产品操作界面元素布局多目标优化设计

目的针对产品设计中操作界面布局设计时存在的随意性、不确定性大等问题,使用多目标优化设计的方法,寻找界面元素的最优布置,以提升界面的使用舒适性和人机交互效率。方法在分析了工效学准则和界面布局美度评价准则的基础上,确立了层次性、相关性、简洁性和舒适性四个界面布局基本原则,并依据原则构建了界面元素布局多目标优化数学模型,在此模型的基础上采用改进的遗传算法,建立基于遗传算法的界面元素布局多目标优化方法。结果给出产品操作界面布局设计的基本原则,提出一种基于遗传算法的产品操作界面元素布局的多目标优化方法及流程。结论提出的布局原则和优化方法能较好地协助设计师获得满足设计需求的布局方案,实例结果表明了理论模型的合理性与遗传算法对于界面元素布局多目标优化问题求解的有效性。     

碳纤维拉伸工艺优化的多目标动态规划方法

将碳纤维的数字化加工优化问题按阶段划分为若干多目标决策子问题。以其中的拉伸过程为例,按拉伸的时间顺序将其划分为6个阶段,以原丝线密度、强度和断裂伸长率为优化目标,提出了一种碳纤维拉伸工艺优化的多目标动态规划方法。以实验数据为基础,拟合出产品性能与生产工艺的关系。采用专家打分,确定各拉伸阶段的权重,合理分配拉伸比。     

Self-adaptive multi-objective harmony search for optimal design of water distribution networks

In multi-objective optimization computing, it is important to assign suitable parameters to each optimization problem to obtain better solutions. In this study, a self-adaptive multi-objective harmony search (SaMOHS) algorithm is developed to apply the parameter-setting-free technique, which is an example of a self-adaptive methodology. The SaMOHS algorithm attempts to remove some of the inconvenience from parameter setting and selects the most adaptive parameters during the iterative solution search process. To verify the proposed algorithm, an optimal least cost water distribution network design problem is applied to three different target networks. The results are compared with other well-known algorithms such as multi-objective harmony search and the non-dominated sorting genetic algorithm-II. The efficiency of the proposed algorithm is quantified by suitable performance indices. The results indicate that SaMOHS can be efficiently applied to the search for Pareto-optimal solutions in a multi-objective solution space.     

Optimal design method with biomechanical analysis for a work environment reducing physical workload: illustration by application to work table height design

A work environment should be designed to minimise physical workload. We propose an optimal work environment design method to accomplish this, in which joint moment ratios were calculated by biomechanical analyses through digital human modelling (DHM) and were used to indicate physical workload. The work environment design problem was formulated as a multi-objective optimisation problem, minimising the average and maximum joint moment ratio values to determine the optimal work environment. Sequential approximate optimisation, which improves the accuracy of the response surface by sequentially adding new sampling points from a simulation, was applied to efficiently obtain a precise optimal solution. The proposed method was applied to designing a work table height for light assembly tasks. This method determined the optimal work table height considering the anthropometric diversity of workers from a relatively small number of subjects. Through this case study, the validity of the proposed method is discussed.     

A particle swarm optimizer for constrained multi-objective engineering design problems

This article presents a particle swarm optimizer (PSO) capable of handling constrained multi-objective optimization problems. The latter occur frequently in engineering design, especially when cost and performance are simultaneously optimized. The proposed algorithm combines the swarm intelligence fundamentals with elements from bio-inspired algorithms. A distinctive feature of the algorithm is the utilization of an arithmetic recombination operator, which allows interaction between non-dominated particles. Furthermore, there is no utilization of an external archive to store optimal solutions. The PSO algorithm is applied to multi-objective optimization benchmark problems and also to constrained multi-objective engineering design problems. The algorithmic effectiveness is demonstrated through comparisons of the PSO results with those obtained from other evolutionary optimization algorithms. The proposed particle swarm optimizer was able to perform in a very satisfactory manner in problems with multiple constraints and/or high dimensionality. Promising results were also obtained for a multi-objective engineering design problem with mixed variables.     

A simulation-based multi-objective genetic algorithm (SMOGA) procedure for BOT network design problem

Solving optimization problems with multiple objectives under uncertainty is generally a very difficult task. Evolutionary algorithms, particularly genetic algorithms, have shown to be effective in solving this type of complex problems. In this paper, we develop a simulation-based multi-objective genetic algorithm (SMOGA) procedure to solve the build-operate-transfer (BOT) network design problem with multiple objectives under demand uncertainty. The SMOGA procedure integrates stochastic simulation, a traffic assignment algorithm, a distance-based method, and a genetic algorithm (GA) to solve a multi-objective BOT network design problem formulated as a stochastic bi-level mathematical program. To demonstrate the feasibility of SMOGA procedure, we solve two mean-variance models for determining the optimal toll and capacity in a BOT roadway project subject to demand uncertainty. Using the inter-city expressway in the Pearl River Delta Region of South China as a case study, numerical results show that the SMOGA procedure is robust in generating ‘good’ non-dominated solutions with respect to a number of parameters used in the GA, and performs better than the weighted-sum method in terms of the quality of non-dominated solutions.     

Multi-objective aerodynamic shape optimization for unsteady viscous flows

This paper presents an adjoint method for the multi-objective aerodynamic shape optimization of unsteady viscous flows. The goal is to introduce a Mach number variation into the Non-Linear Frequency Domain (NLFD) method and implement a novel approach to present a time-varying cost function through a multi-objective adjoint boundary condition. The paper presents the complete formulation of the time dependent optimal design problem. The approach is firstly demonstrated for the redesign of a helicopter rotor blade in two-dimensional flow and in three-dimensional viscous flow, the technique is employed to validate and redesign the NASA Rectangular Supercritical Wing (RSW).