空间桁架结构优化设计

为获得空间桁架结构的合理构型,以某空间设备支撑结构为例,分析结构材料在设计空间的分布形式和桁架结构的传力路径。在已知载荷约束和设计空间大小的条件下,基于连续体拓扑优化方法,以静态多工况刚度和动态固有频率为多目标函数进行优化分析。依据设计要求确定计算模型的结点数和结点位置,获得满足要求的空间桁架结构并进行优化设计。优化结果比原模型质量减少36.7%,一阶模态提高3.6%。     

网络化制造资源优化配置DSS的优化模型研究

首先简介了网络化制造资源优化配置决策支持系统的体系结构及决策过程,在此基础上讨论了优化配置关键技术—EMP优化模型的两种实现方法:EMP综合评判及基于遗传算法的EMP优化方法,最后针对基于多目标遗传算法的EMP优化模型,设计了实现算法。     

基于有限元分析和结构优化的飞机垂尾质量估算

为在总体设计阶段较准确地估算飞机的质量,以垂尾为研究对象,提出基于有限元分析和结构优化的垂尾质量估算方法.该方法的主要步骤为:用CATIA实现垂尾的参数化定义并生成CAD模型;用AVL软件进行气动载荷分析;用PCL编写程序将CAD模型导入MSC Patran中,并应用其结构优化功能计算得到垂尾质量.在iSight中实现...     

飞机多目标优化设计网格的研究与应用

针对飞机多目标拓扑优化提出一种通用的遗传算法计算模型,在此模型基础上,基于对等计算(P2P)技术将分布的计算资源整合为高性能计算环境,以网格服务方式提供统一的资源服务和可视化的用户使用环境,实现多目标优化设计网格,解决飞机设计中遇到的复合材料多目标拓扑优化问题.首先对系统体系结构以及多目标遗传算法做出较详细的描述,然后以优化某型大展弦比机翼为例,给出一组实验数据.结果证明,该系统大大缩短了计算时间,具有良好的并行加速效果.     

汽车零部件立体仓库货位优化问题研究

以立体仓库库存为研究对象,从物流仓储管理角度,研究了货位分配优化问题。分 析了汽车零部件货位布局优化原则,建立多目标货位分配优化数学模型,对遗传算法进行了算子 设计,运用Matlab 软件实现模型的求解,得出可行的货位优化方案。最后结合实例进行多目标 货位优化数学模型求解及应用,并以三维仿真图形展示了优化效果,验证了所设计的遗传算法的 有效性,对同类问题的解决具有参考意义。     

基于改进初始解的遗传算法的布局设计方法

带性能约束的三维布局问题属于具有很强应用背景的组合优化问题,进行了基于全局的布局求解方法的探索。由于NP完全问题的计算复杂性,使得遗传算法求解问题的全局最优解时效率较低。改进了遗传算法的初始解,对提高算法的效率进行了研究。并以旋转卫星舱布局的简化模型为背景,建立了多目标优化数学模型。实例结果与传统遗传算法以及乘子法的计算结果比较,表明该算法具有较好的求解效率。     

铝板纵波电磁超声检测中电磁铁的遗传算法优化设计

提出一种提升电磁超声检测能力的电磁铁的遗传算法优化方法,首先采用单目标优化方法,分别以磁感应强度和均匀度为目标,利用响应曲面法（RSM）,确定磁感应强度与磁芯,线圈等因素的二阶响应模型,根据响应模型得到优化参数。然后采用多目标优化方法,以磁感应强度和均匀度为目标,采用Matlab与Comsol联合仿真,并结合遗传算法进行优化。两组优化结果表明：多目标优化方法得到的有效区域磁感应强度更高,均匀度更好,通过实验证明优化后纵波信号提升60%,证明优化方法有效,可以将该方法运用到其他形式的电磁铁设计。     

受压杆件的虚拟优化设计

基于ICM(独立、连续、映射)方法解决受压杆件的虚拟优化设计问题.在CAD/CAE软件Patran平台上建立受压杆件的三维模型;建立以结构重量为目标,以屈曲临界力为约束的拓扑优化数学模型;借助泰勒展式、过滤函数及瑞利商将模型作近似处理,避免了灵敏度的计算;将优化模型转化为对偶规划,并利用序列二次规划求解,减少了设计变量的数目,缩小了模型的求解规模.并且找出了拓扑结构中瓶颈的位置,据此可以得到较为理想的受压杆件设计结构.     

基于多目标遗传算法的RITS物理结构优化设计

提出了RITS物理结构优化设计问题模型和基于多目标优化的解决思路。RITS结构优化设计待优化函数具有高维、多峰、非线性等特点，求解难度人。重点研究了一类基于Pareto机制的具有快速全局收敛能力的多目标遗传算法，并以紧急救援系统为例进行结构优化设计，验证了算法的有效性。     

变邻域遗传算法在车间物流调度中的应用

针对含有自动引导小车(Automated Guided Vehicle,AGV)的离散化车间物流调度问题,以最小化物流任务时间惩罚成本和最小化运载小车的总行驶距离为优化目标,构建离散化车间多目标物流调度优化模型,设计一种基于Pareto寻优的多目标混合变邻域搜索遗传算法(VNSGA-II).以遗传算法为基础,通过使用NSGA-II的Pareto分层和拥挤度计算方法评估种群优劣实现多目标优化,为了提高算法的寻优能力,避免算法陷入局部最优,通过添加保优记忆库对精英个体进行保护,并利用变邻域搜索算法在搜索过程中的局部寻优能力,针对本文模型特点,设计6个随机邻域结构,来达到算法求解最优值的目标.并提出了基于关键AGV小车的插入邻域和基于关键物流任务的交换邻域调整策略以进一步降低成本.最后,以某离散车间物流调度为实例,分别使用VNSGA-II、带精英策略的快速非支配排序遗传算法Ⅱ(Nondominated Sorting Genetic AlgorithmⅡ,NSGA-II)和强Pareto进化算法(Strong Pareto Evolutionary Algorithm 2,SPEA2)对问题进行求解,计算结果表明,VNSGA-II能得到更好的Pareto解集,验证了算法的有效性和可行性.     

某型横向橡胶止挡结构优化设计

采用多学科优化平台Isight和参数高阶网格变形软件Sculptor对某型横向橡胶止挡进行结构形状优化设计.通过Sculptor进行参数化建模,通过Isight多目标优化算法NSGA-II获得合理的形状参数.采用Abaqus进行有限元计算,结果表明改进后的止挡垂向静刚度满足设计要求,结构设计合理.     

卫星有效载荷的多目标多学科设计优化研究

在卫星有效载荷系统研究中,实施多目标多学科优化的可行性设计。首先,分析了开展卫星有效载荷多学科设计优化的关键技术。建立了包含天线、转发器、数据传输、可靠性、成本和质量的多学科分析模型。然后,应用多目标遗传算法对某卫星有效载荷的可靠性和成本进行多目标设计优化,获得最优解集。最后,运用多学科协同优化结合遗传算法进行可靠性单目标设计优化。研究结果表明:有效载荷的多目标多学科设计优化全面考虑了多个学科之间的关系,设计人员可按需选择其满意的优化结果,大幅提高设计效率;协同优化方法有助于实现学科自治、并行设计,提高设计的灵活性和缩短设计周期。     

Multidisciplinary design of a guided flying vehicle using simplex nondominated sorting genetic algorithm II

This paper presents design of a typical Guided Flying Vehicle (GFV) using the multidisciplinary design optimization (MDO). The main objectives of this multi-disciplinary design are maximizing the payload’s weight as well as minimizing the miss distance. The main disciplines considered for this design include aerodynamics, dynamic, guidance, control, structure, weight and balance. This design of GFV is applied to three and six Degree of Freedom (DOF) to show comparison of simulation results. The hybrid scheme of optimization algorithm is based on Nelder-Mead Simplex optimization algorithm and Nondominated Sorting Genetic Algorithm II (NSGA II), called Simplex-NSGA II. This scheme is implemented for finding an optimal solution through the MDO. The Simplex-NSGA II method is a heuristic optimization algorithm that applies to multi-objective functions and the results are then compared with the most famous algorithms, like Nondominated Sorting Genetic Algorithm II (NSGA II) and Multi-Objective Particle Swarm Optimization (MOPSO). Simulation results demonstrate the superior performance of the Simplex-NSGA II over NSGA II and MOPSO. Also, it is used in this study in order to achieve an optimal solution using MDO in both 3DOF and 6DOF simulations of GFV to reach desirable performance index.     

基于隐式参数化耦合模型的车身集成优化

为提高车身截面优化效率,基于SFE CONCEPT构建白车身关注部位的隐式参数化模型,并与白车身有限元非参数化模型进行耦合,使参数化模型与有限元模型耦合边界处的连接关系随截面变化自动更新,通过试验验证耦合模型的有效性。基于试验设计（design of experiments, DOE）方法、近似模型、多目标优化等策略,对白车身耦合模型进行刚度和模态等多学科集成优化,实现车身局部结构快速轻量化设计。     

Multi‐objective robust fuzzy fractional order proportional–integral–derivative controller design for nonlinear hydraulic turbine governing system using evolutionary computation techniques

The present paper proposes a novel multi‐objective robust fuzzy fractional order proportional–integral–derivative (PID) controller design for nonlinear hydraulic turbine governing system (HTGS) by using evolutionary computation techniques. The fuzzy fractional order PID (FOPID) controller takes closed loop error and its fractional derivative as inputs and performs fuzzy logic operations. Then, it produces the output through the fractional order integrator. The predominant advantages of the proposed controller are its capability to handle complex nonlinear processes like HTGS in heuristic manner, due to fuzzy incorporation and extending an additional flexibility in tuning the order of fractional derivative/integral terms to enhance the closed loop performance. The present work formulates the optimal tuning problem of fuzzy FOPID controller for HTGS as a multi‐objective one instead of a traditional single‐objective one towards satisfying the conflicting criteria such as less settling time and minimum damped oscillations simultaneously to ensure the improved dynamic performance of HTGS. The multi‐objective evolutionary computation techniques such as non‐dominated sorting genetic algorithm‐II (NSGA‐II) and modified NSGA‐II have been utilized to find the optimal input/output scaling factors of the proposed controller along with the order of fractional derivative/integral terms for HTGS system under no load and load turbulence conditions. The performance of the proposed fuzzy FOPID controller is compared with PID and FOPID controllers. The simulations have been conducted to test the tracking capability and robust performance of HTGS during dynamic set point changes for a wide range of operating conditions and model parameter variations, respectively. The proposed robust fuzzy FOPID controller has ensured better fitness value and better time domain specifications than the PID and FOPID controllers, during optimization towards satisfying the conflicting objectives such as less settling time and minimum damped oscillations simultaneously, due to its special inheritance of fuzzy and FOPID properties.     

基于Isight的电动汽车动力性燃油经济性优化

针对电动汽车动力性与燃油经济性优化目标矛盾的问题,基于Isight软件集成的第二代非劣排序遗传算法(NSGA-II)算法用MATLAB软件编写程序,采用Isight直接集成MATLAB程序并进行多目标优化.优化结果表明:基于Isight软件自带的NSGA-II算法,可明显改善电动汽车动力性、经济性.     

改进NSGA Ⅱ算法在车辆路径多目标优化问题中的应用

本文选用NSGA Ⅱ作为求解VRP多目标优化问题的算法基础,分析概括出VRP的三个主要目标函数和三个约束条件,实现了VRP多目标优化问题的数学建模。选择MATLAB作为软件工具进行代码编写,选取Benchmark Problems中C101里的数据作为实验数据进行软件仿真;并且针对NSGA Ⅱ在设计方面的不足之处,对NSGA Ⅱ的初始群体确定和交叉算子两个环节进行改进;然后通过对两种算法仿真结果的比较分析,证实了改进算法在克服早熟现象、提高算法效率以及算法稳定性方面的有效性。     

Multi-objective optimization of double suction centrifugal pump using Kriging metamodels

This paper proposes a new multi-objective optimization method for a family of double suction centrifugal pumps with various blade shapes, using a Simulation-Kriging model-Experiment (SKE) approach. The Kriging metamodel is established to approximate the characteristic performance functions of a pump, namely, the efficiency and required net positive suction head (NPSHr). Hence, the two objectives are to maximize the efficiency and simultaneously to minimize NPSHr. The Non-dominated Sorting Genetic Algorithm II (NSGA II) and Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) have been applied to the multi-objective optimization problem, respectively. The Pareto solution set is obtained by a more effective and efficient manner of the two multi-objective optimization algorithms. A tradeoff optimal design point is selected from the Pareto solution set by means of a robust design based on Monte Carlo simulations, and the optimal solution is further compared with the value of the physical prototype test. The results show that the solution of the proposed multi-objective optimization method is in line with the experiment test.     

某大兆瓦风电机组主机架多阶段多目标优化

为解决大兆瓦风电机组主机架研发难度大、周期长的问题,采用贯穿主机架全生命周期的多阶段多目标优化方法进行研发设计.在概念设计阶段侧重于获得主机架初始构型,以机架材料分布为设计变量,以材料体积为约束条件,以各工况极限强度为目标进行拓扑优化;在详细设计阶段侧重于机架的轻量化,以主机架结构尺寸为设计变量,以疲劳性能为约束条件,以各工况极限强度和机架质量最小为目标进行参数优化.分析结果表明:通过该方法得到的主机架不仅能满足设计要求,而且可以大大缩短研发周期、提高研发效率.