相容决策支持问题法及其在变幅机构稳健优化中的应用

相容决策支持问题法是一种高效的多目标优化设计方法,与设计人员的工程经验紧密相联。将该方法引入到稳健优化设计中,建立了稳健优化设计的相容决策支持问题法模型,通过求解四连杆变幅机构的稳健优化问题,验证了该方法的有效性。     

集成稳健设计在机械产品设计中的应用研究

将优化设计理论与稳健设计方法相结合,讨论减速器的集成稳健优化设计问题。以体积最小和噪声最低两个设计属性作为优化稳健设计目标,利用不精确法的偏爱集成对减速器的多目标多属性进行数学建模,从而将集成稳健设计归结为满足稳健要求的多目标优化问题。数值算例表明,该方法是一种实用的稳健设计优化方法。     

齿轮变速箱的集成稳健优化设计

将优化设计理论与稳健设计方法相结合,讨论汽车变速箱的集成稳健优化设计问题.在单目标优化的基础上,以体积最小和噪声最低两个设计属性为目标,利用集成稳健优化设计对汽车变速箱的多目标多属性进行数学建模,从而将集成稳健设计归结为满足稳健要求的多目标优化问题.数值算例表明,该方法是一种实用的稳健设计优化方法.     

基于支持向量机的多目标模糊稳健优化设计

为了提高模糊稳健优化设计的计算效率,探讨了基于支持向量机回归机(SVR)的多目标模糊稳健设计方法,该方法以SVR作为非线性约束函数的替代模型,并采用SVR对模糊概率进行仿真计算,可显著降低模糊稳健优化设计的机时消耗;采用字典序优先级的目标规划法,建立了多目标稳健优化设计模型;把SVR与遗传算法相结合,构建了一种混合智能优化算法;通过多目标稳健设计实例,对所提出的方法进行了验证。     

任意分布参数的车辆半轴的可靠性稳健设计

组合随机摄动法、四阶矩技术、可靠性优化设计、可靠性灵敏度与稳健设计等理论方法,提出了车辆半轴的可靠性稳健设计方法,将可靠性灵敏度作为目标函数之一体现在可靠性优化设计模型之中,将可靠性稳健设计归结为满足可靠性要求、重量最轻和敏感性最低的多目标优化设计问题。在基本随机参数的前四阶矩已知的情况下,可以实现基本随机参数为任意分布参数的车辆半轴的可靠性稳健设计。数值算例表明该方法是一种有效实用的设计方法。     

基于多目标优化策略的螺旋弹簧可靠性稳健优化设计

应用可靠性稳健优化设计理论和多目标决策方法,将车辆螺旋弹簧的可靠性稳健优化设计转化为多目标问题.运用层次分析法选取粒子群算法中的全局极值和个体极值,提出基于层次分析法的多目标粒子群算法,并将该算法应用于可靠性稳健优化设计的多目标模型求解中.与传统方法相比,该方法简便、易行,并能迅速准确地得到车辆螺旋弹簧的可靠性稳健优化设计信息.     

整体法兰的可靠性稳健优化设计

将可靠性优化设计理论、可靠性灵敏度分析技术与稳健设计方法相结合，讨论整体法兰的可靠性稳健优化设计问题，提出可靠性稳健优化设计的计算方法。把可靠性灵敏度溶入可靠性优化设计模型之中，将可靠性稳健优化设计归结为满足可靠性要求的多目标优化问题。数值算例表明所提出的数值计算方法是一种非常方便和实用的可靠性稳健优化设计方法。     

基于粒子群算法的后桥可靠性稳健优化设计

为提高车辆零部件的安全性和稳健性,应用可靠性稳健优化设计理论和多目标决策方法,将车辆后桥的可靠性稳健优化设计转化为多目标优化问题.通过模糊多目标粒子群算法求出所有满足约束性条件的pareto解集,结合实际情况,依据pareto解集确定零部件的设计规格.实验表明,所提方法能迅速有效地获得可靠性稳健设计的信息.     

基于改进遗传算法的稳健可靠性优化设计

针对单目标多约束非线性优化问题的稳健可靠性优化设计,使用摄动随机有限元法进行可靠性设计,并对优化问题的目标函数和约束函数进行灵敏度分析,产生灵敏度附加项,建立稳健可靠性优化设计模型。为了提高求解效率,提出一种改进遗传算法,并应用于梁结构的稳健可靠性优化设计模型求解中,实例说明该方法的可行性与实用性。     

基于多目标非耦合优化策略的可靠性稳健优化设计

以实现多目标问题的可靠性稳健优化设计为目标,通过对目标函数和约束条件进行灵敏度分析,生成目标函数和约束函数的灵敏度附加项,建立了基于灵敏度附加目标函数的可靠性稳健优化设计模型;基于独立公理,使用正交试验和方差分析技术确定设计变量对各个设计目标的影响程度,将设计参数按无耦合或准耦合设计形式分组,把多目标优化问题单目标化,避免多个设计目标之间的反复权衡;结合增广乘子法,应用MATLAB的优化和符号工具箱来实现钳形盘式制动器的可靠性稳健优化设计。算例表明,提出的稳健设计方法具有较高的精度和可靠度。     

Pareto lexicographic α-robust approach and its application in robust multi objective assembly line balancing problem

Robustness in most of the literature is associated with min-max or min-max regret criteria. However, these criteria of robustness are conservative and therefore recently new criteria called, lexicographic α-robust method has been introduced in literature which defines the robust solution as a set of solutions whose quality or jth largest cost is not worse than the best possible jth largest cost in all scenarios. These criteria might be significant for robust optimization of single objective optimization problems. However, in real optimization problems, two or more than two conflicting objectives are desired to optimize concurrently and solution of multi objective optimization problems exists in the form of a set of solutions called Pareto solutions and from these solutions it might be difficult to decide which Pareto solution can satisfy min-max, min-max regret or lexicographic α-robust criteria by considering multiple objectives simultaneously. Therefore, lexicographic α-robust method which is a recently introduced method in literature is extended in the current research for Pareto solutions. The proposed method called Pareto lexicographic α-robust approach can define Pareto lexicographic α-robust solutions from different scenarios by considering multiple objectives simultaneously. A simple example and an application of the proposed method on a simple problem of multi objective optimization of simple assembly line balancing problem with task time uncertainty is presented to get their robust solutions. The presented method can be significant to implement on different multi objective robust optimization problems containing uncertainty.     

Development of an experiment-based robust design paradigm for multiple quality characteristics using physical programming

The well-known quality improvement methodology, robust design, is a powerful and cost-effective technique for building quality into the design of products and processes. Although several approaches to robust design have been proposed in the literature, little attention has been given to the development of a flexible robust design model. Specifically, flexibility is needed in order to consider multiple quality characteristics simultaneously, just as customers do when judging products, and to capture design preferences with a reasonable degree of accuracy. Physical programming, a relatively new optimization technique, is an effective tool that can be used to transform design preferences into specific weighted objectives. In this paper, we extend the basic concept of physical programming to robust design by establishing the links of experimental design and response surface methodology to address designers’ preferences in a multiresponse robust design paradigm. A numerical example is used to show the proposed procedure and the results obtained are validated through a sensitivity study.     

A hyperspherical particle swarm optimizer for robust engineering design

This paper presents a novel multi-objective particle swarm optimizer, called hyperspherical particle swarm optimization (HSPSO), which efficiently deals with robust engineering design problems. In contrast to traditional optimization methods which rely on single-point design configurations, the HSPSO method evolves multi-dimensional design surfaces while simultaneously optimizing several potentially conflicting objectives and minimizing product/process variations. The hyperspherical representation is accommodated by incorporating manufacturing tolerances for design variables, and sensitivity analysis is performed to maintain feasibility within the design region. Hyperspherical particles are automatically evaluated, and non-inferior solutions are identified by the Pareto-dominance strategy. To enhance the local search ability of the particle swarm optimization algorithm, a gradient descent algorithm is applied, and fitness evaluation is performed by using a crowding factor, which defines the density of the population along the Pareto front. The performance of the proposed HSPSO algorithm is highlighted by reporting on three robust engineering design problems, which involve a mixture of single objective and multiple conflicting objectives along with integer, discrete and continuous design parameters. Monte Carlo simulations are used to assess the reliability of the obtained results.     

汽车车架结构多目标拓扑优化方法研究

为了研究以静态多工况下刚度和动态振动频率为目标函数的车架拓扑结构,提出了一种结构的多目标拓扑优化研究方法。基于实体各向同性材料惩罚函数的拓扑优化方法,采用折衷规划法定义多目标拓扑优化和多刚度拓扑优化的目标函数,而振动固有频率拓扑优化的目标函数则采用平均频率法定义。通过优化得到了同时满足静态刚度和振动低阶频率要求的汽车车架结构拓扑。该方法避免了单目标拓扑优化无法考虑其他因素的缺点,适合连续体结构的多目标拓扑优化。     

具有随机型和区间型干扰因素的产品健壮设计研究

针对随机型和区间型干扰因素共同作用条件下的产品健壮设计问题，在同时考虑设计目标健壮性和设计约束可行健壮性的基础上，提出了健壮设计的分位数型设计准则；在该设计准则的基础上，建立了产品健壮设计的分位数优化模型，保证了所设计产品的抗干扰能力和可靠度。通过汽车离合器膜片弹簧的健壮设计实例，验证了该方法的有效性和实用性。     

A new method based on LPP and NSGA-II for multiobjective robust collaborative optimization

The multiobjective robust collaborative optimization framework consists of optimization both at the system and autonomous subsystem levels. Linear physical programming is used in the system level optimization, which avoids the difficulty in choosing the multidimensional Pareto set. The non-dominated sorting genetic algorithm (NSGA-II) is used in the subsystem optimization with physical objectives. The interdisciplinary incompatibility function and physical objectives have different priority levels. At the first priority level, the best individual should be in the feasible region of the subsystem. At the second priority level, the interdisciplinary incompatibility function of the best individual should be no more than the feasibility threshold. The physical objectives are improved after the achievement of the above levels. A method for producing initial population with feasibility and diversity is proposed to improve the calculation efficiency and accuracy of the subsystem optimization at the first priority level. A method for setting dynamic feasibility threshold is proposed for the non-dominated sorting to help the physical objectives to obtain better solutions at the second priority level. Finally, the results of the speed reducer show that the presented method is efficient.     

环形可展开卫星天线的多目标结构优化设计

卫星天线的结构设计是一个复杂的系统工程,通常受到多个指标的限制。依据天线各指标的重要性,本文建立了以一阶固有频率最大、质量最小为目标的天线结构多目标优化设计模型,并基于神经网络和遗传算法,结合正交实验和变加权系数技术,形成了一种有效的多目标优化算法。在MATLAB平台下实现了天线的结构多目标优化设计计算程序,求得了天线的最佳结构参数,解决了带有结构有限元计算、多离散变量、多目标相结合的复杂结构优化设计问题。