基于多目标遗传算法的弧齿锥齿轮多学科优化设计

针对常规设计中弧齿锥齿轮存在的问题,将多学科优化设计引入到弧齿锥齿轮设计中,改善其不足。以大小弧齿锥齿轮体积之和最小、弧齿锥齿轮齿根弯曲应力最小以及工作噪音最小三个方面为目标函数,以齿数、齿宽中点螺旋角、齿宽和大端模数为设计变量,考虑齿面接触疲劳强度、齿根弯曲疲劳强度、环境保护等8个方面约束,建立弧齿锥齿轮的多学科优化设计数学模型。根据该数学模型,运用多目标遗传算法对文中的实例优化求解,计算结果表明该设计方法是合理的,行之有效的。     

航空弧齿锥齿轮低敏感度齿面优化设计

为了降低弧齿锥齿轮的齿面接触印痕关于安装距偏差（又称安装误差）的敏感度，需要开展某航空弧齿锥齿轮的低敏感度齿面优化设计。在轮齿承载接触分析（Loaded tooth contact analysis,LTCA）技术中，引入齿轮副的安装误差，形成了计及安装误差的轮齿接触分析（Error loaded tooth contact analysis,ELTCA）方法，基于ELTCA建立了齿面接触印痕与安装误差间的内在关系；借助接触印痕的量化描述，获得了齿面接触印痕关于安装误差的敏感度矩阵；基于局部综合法建立敏感度关于齿面接触参数的优化设计目标函数，形成弧齿锥齿轮的低敏感度齿面优化设计模型；采用神经网络与遗传算法进行求解，获得了低敏感度齿面的切齿参数；算例结果表明，优化后的齿面接触印痕关于安装误差的敏感度较优化前降低了78.87%。     

考虑可靠度约束的弧齿锥齿轮参数优化

在弧齿锥齿轮传动普通优化的基础上，将锥齿轮的强度应力作为随机变量处理，建立了弧齿锥齿轮传动的优化设计数学模型，并对实例进行了计算。最后将计算结果应用于弧齿锥齿轮传动的设计中，取得了较好的效果，验证了本方法的正确可行性。     

弧齿锥齿轮优化设计的数学模型

针对弧齿锥齿轮在设计中计算量较为复杂且又容易出错的问题,介绍了如何建立弧齿锥齿轮优化设计的数学模型,用以提高弧齿锥齿轮减速器的设计效率.     

具有模糊可靠度约束的弧齿锥齿轮参数的模糊优化设计

在充分考虑了弧齿锥齿轮各设计参数的模糊性和随机性的基础上,结合传统的优化设计方法,探讨了具有模糊可靠度约束的弧齿锥齿轮参数的模糊优化设计的方法。     

弧齿锥齿轮协同设计系统建模的研究与实现

针对弧齿锥齿轮及网络化协同设计与制造领域的先进思想与方法,构建了基于Web Services的弧齿锥齿轮齿面协同设计系统的基本模型。首先,以弧齿锥齿轮中主流的仿真加工建模为手段设计了通用的弧齿锥齿轮齿面设计的全生命周期流程。然后,基于该流程提出了实现协同的多种关键技术,如协同仿真技术、优化设计技术和相关的支撑系统如信息集成系统、设计监控系统、零件库系统。最后,搭建了弧齿锥齿轮齿面协同设计系统集成平台的基本框架,以此实现高效、高精度、高柔性化的弧齿锥齿轮的协同设计。     

弹性支承下弧齿锥齿轮切齿参数的多目标优化设计研究

保证弹性支承下弧齿锥齿轮具有良好的接触特性是新型航空发动机动力传输系统设计的关键技术之一。针对某航空发动机转子系统中的弧齿锥齿轮,以齿轮副在刚性支承下的接触区中心点和传动误差值作为预期的目标值,建立了弧齿锥齿轮的多目标优化模型,结合线性加权与轮流坐标搜索法,对弧齿锥齿轮切齿加工参数进行了优化设计,使设计后的齿轮在弹性支承下具有良好的接触特性。实现了对弹性支承下弧齿锥齿轮接触印痕与传动误差的预控。     

弧齿锥齿轮传动的模糊可靠性优化设计

工程车辆驱动桥主减速器的功能是降低转速，增大扭矩，以满足车辆工作和行驶的要求，故采用弧齿锥齿轮传动较为合适。弧齿锥齿轮传动的特点是同时啮合的齿数较多，齿间压力分布状况好，啮合平稳，噪声小。由于小齿轮的齿数可以做得很少，因此可在大齿轮的同样外形尺寸下获得较大的传动比。然而，由于弧齿锥齿轮传动设计计算复杂，所受限制条件多，且应力和强度是随机的或是模糊变量，而且驱动桥主减速器又是工程车辆的关键部件，因此对驱动桥主减速器的弧齿锥齿轮传动进行模糊可靠性优化设计具有现实意义。１　模糊可靠性优化设计数学模型的…     

基于遗传算法的高齿弧齿锥齿轮优化设计

基于遗传算法提出了的高齿弧齿锥齿轮的优化设计。设计变量是齿顶高系数和变位系数(非零变位 ) ,约束条件为齿顶变尖、根切、刀具条件限制、强度限制等 ,目标函数为重合度最大。本文编制的优化设计程序经实例计算达到了重合度的设计目标。     

高齿弧齿锥齿轮的承载啮合仿真和动态性能试验

研究了高齿弧齿锥齿轮和普通弧齿锥齿轮的加载接触过程，对比了二者在载荷作用下的齿面载荷分布、齿面加载接触印痕和承载传动误差，计算了齿根弯曲应力。在台架试验台上测定了高齿弧齿锥齿轮和普通弧齿锥齿轮的噪声和振动量，实验证明高齿弧齿锥齿轮齿面载荷分布合理，弯曲应力较小，具有较低的噪声和较好的动态特性。     

基于遗传算法的多设计任务规划研究

在满足产品设计周期的条件下,以完成所有设计任务的时间最短为规划目标,提出并构建了一种面向多设计任务的规划模型。在该规划模型中,设计节点的并发性和任务设计的并行性被考虑。针对该任务规划模型的解算,则采用遗传算法(GeneticAlgorithm,GA)实现。最后,采用具体的实例来验证该任务规划模型及算法的可行性。     

Applying simulated annealing to cellular manufacturing system design

Cell formation and cellular layout design are the two main steps in designing a cellular manufacturing system (CMS). In this paper, we will present an integrated methodology based on a new concept of similarity coefficients and the use of simulated annealing (SA) as an optimization tool. In comparison with the previous works, the proposed methodology takes into account relevant production data, such as alternative process routings and the production volumes of parts. The SA-based optimization tool is parallel in nature and, hence, can reduce the computation time significantly, so it is capable of handling large-scale problems. Finally, the SA-based procedure is compared with a genetic algorithm (GA) based procedure and it will be shown that the SA-based algorithm can be as effective as a GA-based algorithm, but with less computational time and effort.     

Optimum design of externally pressurized air bearing using Cluster OpenMP

This study presents a performance evaluation of a new portable parallel programming paradigm, the Cluster OpenMP (CLOMP) for distributed computing, in conducting an optimum design of air bearings. The multi-objective optimization was carried out by using a genetic algorithm (GA) incorporating Pareto optimality criterion. Since the GA is natural parallel evolution algorithm, the computation of the search was carried out in parallel by using the CLOMP. In this study, the performance of a CLOMP cluster of four dual-core computers for the air bearing optimization was compared with a shared-memory processing (SMP) computer equipped with two quad-core processors. To examine the parallel efficiency of the CLOMP in the GA optimization, several multithread applications of various task sizes were tested. It is shown that the air bearing optimization can be effectively dealt with by the CLOMP (parallel efficiency of 96.2-98.8%) as well as the SMP computing (93.1-99.4%) in the studied cases. The CLOMP retains the characteristics of directive-based OpenMP, such as incremental programming and serial-coding compatibility. The verified high parallel efficiency of the CLOMP cluster demonstrates its potential applications of the scalable computing in many tribological optimizations.     

A hybrid particle swarm optimization for parallel machine total tardiness scheduling

The parallel machine scheduling problem has received increasing attention in recent years. This research considers the problem of scheduling jobs on parallel machines with a total tardiness objective. In the view of its non-deterministic polynomial-time hard nature, the particle swarm optimization (PSO), which is inspired by the swarming or collaborative behavior of biological populations, is employed to solve the parallel machine total tardiness problem (PMTP). Since it is very hard to directly apply standard PSO to this problem, a new solution representation is designed based on real number encoding, which can conveniently convert the job sequences of PMTP to continuous position values. Moreover, in order to enhance the performance of PSO, we introduce clonal selection algorithm (CSA) into PSO and therefore propose a new CSPSO method. The incorporation of CSA can greatly improve the swarm diversity and avoid premature convergence. We further investigate three parameters of PSO and CSPSO, finding that the parameters have marginal impact on CSPSO, which indicates that CSPSO is a very stable and robust method. The performance of CSPSO is evaluated in comparison with traditional genetic algorithm (GA) and standard PSO on 250 benchmark instances. Experimental results show that CSPSO significantly outperforms GA and PSO, with obtaining the optimal solutions of 237 instances. Additionally, PSO appears more effective than GA.     

A parallel computing application of the genetic algorithm for lubrication optimization

This study investigated the performance of parallel optimization by means of a genetic algorithm (GA) for lubrication analysis. An air-bearing design was used as the illustrated example and the parallel computation was conducted in a single system image (SSI) cluster, a system of loosely network-connected desktop computers. The main advantages of using GAs as optimization tools are for multi-objective optimization, and high probability of achieving global optimum in a complex problem. To prevent a premature convergence in the early stage of evolution for multi-objective optimization, the Pareto optimality was used as an effective criterion in offspring selections. Since the execution of the genetic algorithm (GA) in search of optimum is population-based, the computations can be performed in parallel. In the cases of uneven computational loads a simple dynamic load-balancing scheme is proposed for optimizing the parallel efficiency. It is demonstrated that the huge amount of computing demand of the GA for complex multi-objective optimization problems can be effectively dealt with by parallel computing in an SSI cluster.     

基于整数编码并行遗传算法的复合材料螺旋桨结构优化设计

提出一种针对大型复合材料结构铺层厚度和铺层顺序同时进行优化的整数编码并行遗传算法,并将该优化方法应用于复合材料螺旋桨结构优化问题.首先,在区域划分的基础上,对每一单层设定一个长度控制因子来决定单层的铺设区域,以实现对大型复合材料结构的整体一次性优化.然后对遗传算法进行改进,使之成为一种能同时优化铺层厚度和铺层顺序的高效算法.并采用并行编程语言标准MPI( message passing interface)构建并行编程环境,利用主从式并行遗传算法框架,实现遗传算法在单机多进程上的并行计算.最后针对复合材料螺旋桨结构进行优化设计,以验证该方法的高效性,并分析并行遗传算法的加速效果.     

基于遗传算法和神经网络的六自由度并联平台位置正解

位置正解是并联平台机构应用的基础。提出了用GA +BP混合算法求六自由度并联平台位置正解的方法。首先用改进的Newton Raphson法对数学迭代模型进行求解运算 ,将所得的输入输出数据组作为训练样本 ,再用GA+BP混合算法对该模型进行了精确求解 ,仿真研究表明GA +BP混合算法运算速度快、计算精度高 ,用于求解并联平台机构的位置正解是一种比较理想的方法。     

求解Job shop调度的协同优化算法

针对量子粒子群算法、遗传算法在求解车间调度存在的局部收敛的问题,提出用量子粒子群算法与遗传算法相结合的协同优化方法求解该问题。该算法采用量子粒子群算法与遗传算法的并行搜索结构,通过迁移算子把各个种群联系起来。仿真结果表明,该算法收敛速度快,且具有较高的求解质量。     

Parallel Test Tasks Scheduling and Resources Configuration Based on GA-ACA

A Genetic Algorithm-Ant Colony Algorithm(GA-ACA),which can be used to optimize multi-Unit Under Test(UUT)parallel test tasks sequences and resources configuration quickly and accurately,is proposed in the paper.With the establishment of the mathematic model of multi-UUT parallel test tasks and resources,the condition of multi-UUT resources mergence is analyzed to obtain minimum resource requirement under minimum test time.The definition of cost efficiency is put forward,followed by the design of gene coding and path selection project,which can satisfy multi-UUT parallel test tasks scheduling.At the threshold of the algorithm,GA is adopted to provide initial pheromone for ACA,and then dual-convergence pheromone feedback mode is applied in ACA to avoid local optimization and parameters dependence.The practical application proves that the algorithm has a remarkable effect on solving the problems of multi-UUT parallel test tasks scheduling and resources configuration.     

基于Internet网络的主从式并行遗传算法及其在叶栅优化中的应用

为了解决遗传算法在优化中由于适应度评价很费时而导致计算时间过长的问题 ,本文发展了一种基于In ternet网络实现的主从式并行遗传算法。在函数优化的测试实验中 ,通过控制待优化函数适应度评价的时间 ,验证了主从式模型在适应度评价很费时且远远超过通讯时间时将获得接近于线性的加速比 ,讨论了主从式并行遗传算法应用于气动性能优化中的可行性。通过二维叶栅的优化算例 ,证明了本文提出的算法适合于需要大计算资源的叶栅气动优化设计