Kriging模型并行加点策略的多目标优化方法

针对复杂机械装备多学科多目标优化设计成本高、周期长等问题,提出一种近似模型与并行加点策略相结合的多目标优化方法。基于Kriging模型,将添加更新样本点定义为同时考虑Pareto最优解和预测误差的动态多目标优化问题,应用改进NSGA-II优化算法和极大极小距离准则,确定最优的并行更新样本点,在提高Kriging模型精度的同时实现多目标优化。测试函数验证和实例结果表明,该方法可有效提高复杂系统多目标优化效率,同时获得收敛性和分散性俱佳的Pareto最优解。     

基于GA的多约束的多目标排序优化

主要论述了一种解决多约束多目标排序优化的方法.采用在Pareto遗传算法中嵌入优先级约束的检索和调整程序,以保证参与变异和交叉的染色体的有效性.研究结果表明此方法可提高寻优效率.     

基于Pareto最优原理的钻机钻进参数多目标优化

针对某双管定向钻机,提出了基于Pareto最优原理的钻进参数多目标优化方法。该方法根据钻机性能与工况,考虑水力对钻头比能影响,确定了钻进参数优化模型。针对罚函数处理约束条件的不足,引入了改进约束条件处理策略,提出了基于小生境思想拥挤度值计算方法及自适应交叉和变异算子。测试了改进算法的性能,并将改进算法用于求解基于某煤矿工程实际建立的钻机钻进参数优化模型。研究结果表明：与NSGA-Ⅱ和MOPSO算法相比,改进算法在求解测试问题时具有更好的收敛性与分布性。利用改进算法求解实际问题时得到的Pareto前端解集分布均匀,而且有效提高了机械钻速,延长了钻头寿命并降低了钻头比能。     

基于多目标优化的备件保障决策研究

备件供应保障优化是提高武器装备的战备完好性和降低寿命周期费用的重要途径.通过分析复杂武器装备可修复备件两级供应保障系统,提出了一种基于Pareto最优多目标优化算法,研究了备件保障与装备战备完好性的关系,建立了基于并列选择备件购置费用和库存费用、以系统供应保障度为目标的多目标可修复备件供应保障优化模型,并结合边际效能法和Pareto多目标优化方法进行求解,得到一个均匀分布于Pareto前沿的优化解集.最后,对模型进行了算例分析和参数敏感性分析.实例应用证明,模型可以优化备件库存,并可以评估备件库存水平、两级维修能力和系统自身可靠性等因素对系统保障效能的影响.     

一种用于多目标约束优化的改进进化算法

当前求解多目标优化的进化算法主要考虑如何处理相互冲突的多个目标间的优化,很少考虑对约束条件处理的问题.对此,给出了一种基于双群体搜索机制的改进差分进化算法,以求解多目标约束优化问题.采用两个不同种群,分别保存可行个体与不可行个体的双群体约束处理策略,利用基于Pareto的分类排序多目标优化技术,完成对进化个体解的评价.并通过群体混沌初始化、自适应交叉和变异操作来提高基本差分进化算法的性能.对三个经典测试函数的仿真结果表明,文中算法在均匀性、逼近性及收敛速度三方面均优于非支配排序遗传算法,而收敛速度也优于另两种改进进化算法.     

基于iSIGHT的鼓式制动器多目标优化设计

以鼓式制动器效能因素最大和制动鼓体积最小为目标,对鼓式制动器进行多目标优化设计,利用iSIGHT集成的多目标优化遗传算法,得到多目标优化问题的Pareto最优解集,再利用Pareto最优解集专用后处理工具EDM,找到最合适的折衷解.     

一种新的多目标工程问题的优化方法

基于多目标优化设计中理想点法及约束法的基本思想，吸取线性加权法中权系数的处理方法，通过构造新的评价函数来求设计者满足的非劣解；并与混合离散变量的组合型法结合起来，从而形成多目标混合离散变量的优化方法。该方法对工程问题优化设计具有普遍的适用性。     

一种新的约束优化方法

提出了一种新的约束优化方法,此方法根据设计点附近“定向单元”的函数值信息确定出有效的搜索方向。进而较快地获得最优点。本方法的算法原理简单,易于编程,经三个例题验证,取得了满意的结果,说明了该方法的正确性和可靠性。为优化设计领域提供了一种新的约束优化算法。     

基于Pareto解集的多目标优化方法及其应用

针对传统多目标优化设计方法的弱点,基于Pareto概念,借助遗传算法所具有的并行搜索特性,引入群体排序技术、小生境技术求得多目标优化问题的Pareto解集,实现了先寻优后决策的求解模式。实际工程算例表明,该模式可同时获得多个Pareto最优解,据此决策能有效弱化设计人员先验知识不足的影响,因而较传统多目标优化方法更为实用有效。     

基于拥挤距离排序的铝电解多目标优化

针对铝电解生产过程难以快速、准确地获得节能减排多目标优化Pareto前沿问题,提出一种基于拥挤距离排序的多目标细菌觅食算法。方法在保证铝电解槽平稳运行的基础上,建立电流效率最大和温室气体排放量最小的多目标优化模型;利用拥挤距离更新外部档案及对菌群步长进行自适应动态调整,以改进种群的收敛性和多样性,最后对优化模型求解。通过实验可知,改进后的算法能快速获得分布均匀的Pareto最优解,运用优化后的决策参数指导生产,能在提高电流效率的同时减少温室气体的排放量,实现铝电解生产过程节能减排的目的。     

Fixed structure compensator design using a constrained hybrid evolutionary optimization approach

This paper presents an efficient technique for designing a fixed order compensator for compensating current mode control architecture of DC−DC converters. The compensator design is formulated as an optimization problem, which seeks to attain a set of frequency domain specifications. The highly nonlinear nature of the optimization problem demands the use of an initial parameterization independent global search technique. In this regard, the optimization problem is solved using a hybrid evolutionary optimization approach, because of its simple structure, faster execution time and greater probability in achieving the global solution. The proposed algorithm involves the combination of a population search based optimization approach i.e. Particle Swarm Optimization (PSO) and local search based method. The op-amp dynamics have been incorporated during the design process. Considering the limitations of fixed structure compensator in achieving loop bandwidth higher than a certain threshold, the proposed approach also determines the op-amp bandwidth, which would be able to achieve the same. The effectiveness of the proposed approach in meeting the desired frequency domain specifications is experimentally tested on a peak current mode control dc−dc buck converter.     

Manufacturing cost constrained topology optimization for additive manufacturing

Frontiers of Mechanical Engineering - This paper presents a manufacturing cost constrained topology optimization algorithm considering the laser powder bed additive manufacturing process. Topology...     

Dynamic optimization algorithm of constrained motion

The constrained motion requires the determination of constraint force acting on unconstrained systems for satisfying given constraints. Most of the methods to decide the force depend on numerical approaches such that the Lagrange multiplier method, and the other methods need vector analysis or complicated intermediate process. In 1992, Udwadia and Kalaba presented the generalized inverse method to describe the constrained motion as well as to calculate the constraint force. The generalized inverse method has the advantages which do not require any linearization process for the control of nonlinear systems and can explicitly describe the motion of holonomically and/or nonholonomically constrained systems. In this paper, an explicit equation to describe the constrained motion is derived by minimizing the performance index, which is a function of constraint force vector, with respect to the constraint force. At this time, it is shown that the positive-definite weighting matrix in the performance index must be the inverse of mass matrix on the basis of the Gauss’s principle and the derived differential equation coincides with the generalized inverse method. The effectiveness of this method is illustrated by means of two numerical applications.     

CLFs-based optimization control for a class of constrained visual servoing systems

In this paper, we use the control Lyapunov function (CLF) technique to present an optimized visual servo control method for constrained eye-in-hand robot visual servoing systems. With the knowledge of camera intrinsic parameters and depth of target changes, visual servo control laws (i.e. translation speed) with adjustable parameters are derived by image point features and some known CLF of the visual servoing system. The Fibonacci method is employed to online compute the optimal value of those adjustable parameters, which yields an optimized control law to satisfy constraints of the visual servoing system. The Lyapunov's theorem and the properties of CLF are used to establish stability of the constrained visual servoing system in the closed-loop with the optimized control law. One merit of the presented method is that there is no requirement of online calculating the pseudo-inverse of the image Jacobian's matrix and the homography matrix. Simulation and experimental results illustrated the effectiveness of the method proposed here.     

New approach for robust multi-objective optimization of turning parameters using probabilistic genetic algorithm

Limited by techniques, the process of remanufacturing exists masses of uncertainties which have a great impact on the remanufactured parts quality, how to achieve a higher quality of mechanical products by using limited remanufactured parts precision, has become one of the key issues of remanufacturing industry. Firstly, with a target to reduce uncertainties and improve the quality of automatic products, a method of tolerance grading allocation for remanufactured parts is proposed based on the uncertainty analysis of the remanufacturing assembly. The dimensional tolerances of the mechanical parts are divided into positive and negative two groups. We use selective assembly method to reduce assembling deviation. Then, the method is proven by mathematical formulas that the remanufactured parts variance can be expanded to two times, and the tolerances can be liberalized 40 % through tolerance grading allocation method. It is also the theoretical basis for improving the reuse radio and quantitatively describing the tolerance liberalization in this paper. Finally, feasibility research on this method is studied from the angle of cost–benefit. Furthermore, a tolerance grading allocation example of remanufactured engine piston assembly in a power corporation shows the validity and practicality of the proposed method.     

基于机会约束目标规划的可靠性优化设计

在机械系统的方案设计中,零部件或子系统的可靠性数据具有模糊性.运用模糊机会约束规划理论,对具有N个子系统,每个子系统有多种设计可选的可靠性优化问题建立机会约束的目标规划模型.在系统可靠性机会约束的目标规划模型求解中,首先检验模型机会约束,当机会约束不可行时,采用罚函数方法;然后给出了嵌入检验机会约束检验的遗传算法方法.接着给出了一个经典的连接结构确定的系统可靠性设计问题,其参数均采用三角模糊数,实例求解验证了该算法的有效性.     

基于混沌优化的有约束广义预测控制器

针对传统优化算法解决约束广义预测控制的优化问题时存在计算量大、不能找到全局最优解等缺点,该文尝试用混沌优化算法来解决此类问题.提出一种用混沌优化算法作为滚动优化策略的有约束广义预测控制算法(COGPC).该算法可以有效地处理约束并找到全局最优解,是一种新的"软约束"方法.最后通过仿真验证了该方法的有效性.     

人工鱼群算法的非线性约束优化

人工鱼群算法(AFSA)是一种新型的寻优策略,它具有鲁棒性强,全局收敛性好,以及对初值的不敏感性等优点.本文引人了半可行域的概念,并结合人工鱼群算法本身的特点,设计了基于竞争选择和惩罚函数的适应度函数,从而得到了一个利用AFSA算法求解约束优化问题的新进化算法.数值计算证明了算法的有效性.     

基于ESO的约束阻尼板拓扑优化设计研究

基于渐进结构拓扑优化方法（Evolution Structural Optimization，ESO），以阻尼材料用量为约束条件、模态损耗因子最大化为目标函数，研究了约束阻尼板结构的拓扑优化设计问题，推导了灵敏度分析公式，给出了阻尼结构拓扑优化设计方法，得到了在一定阻尼材料用量下约束阻尼板结构的模态损耗因子最大的拓扑构形。该方法对于阻尼结构的优化设计有一定的意义，具有较强的工程实用性。